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<article>
<div>
<h5>Java Machine Learning Course</h5>
</div>
<div></div>
<p>  						 
	</p><div>
	<ul>
		<p>
		<b>Python Machine Learning Course. </b>
		<br /><b>Prerequisites: </b>Basic knowledge of Python 
		<br /><b>Our Style: </b>Hands-on, Practical  
		<br /><b>Location:  </b>Online, Instructor-led  
		<br /><b>Download:  </b>anaconda.com
		<br /><b>Duration:  </b>2 consecutive days, the first of which shows as the booking date.
		<br /><b>Times: </b>10am - 5pm
	    </p>
	</ul>	

<p></p>
</div></article>

<div>
	  <div>
		 <h5>Python Machine Learning Course </h5>
	  </div>
	  <div></div>
		<p> Python Machine Learning algorithms can derive trends (learn) from data and make predictions on data by extrapolating on existing trends. Companies can take advantage of this to gain insights and ultimately improve business.
		<br />
		<br />Using Python Machine Learning scikit-learn, practice how to use Python Machine Learning algorithms to perform predictions on data.
		<br />
		<br />Learn the below listed algorithms, a small collection of available Python Machine Learning algorithms.
		<br />
		<br />Gain a good understanding of how to plan a Machine Learning project.
		We create, experiment and run example code to implement the Python Machine Learning algorithms.
	  </p>
<div>
<h5>Course Topics</h5>
</div>
<div></div>
<div>
	<ul>	  
			<li>Supervised Machine Learning:
			<strong>Classification Algorithms:</strong>  	 
					Classification Algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine
					<br /><strong>Regression Algorithms:</strong>  Linear, Polynomial
			</li> 
	
	
			<li> <strong>  Unsupervised Machine Learning: </strong> Clustering Algorithms: K-means clustering, Hierarchical Clustering

			<br /><strong>Dimension Reduction Algorithms: </strong>Principal Component Analysis Latent Dirichlet allocation (LDA)

			<br /><strong>Association Algorithms:</strong> Apriori, Euclat

			<br /><strong>Other machine learning Algorithms:</strong>
			Reinforcement learning Algorithms: Q-Learning
			</li>
	
			<li>
			<strong>Ensemble Methods Algorithms:</strong> Stacking, bagging, boosting.
			</li> 
			
			<li>
			<strong>Random </strong>Forest Random Forest, Gradient Boosting
			</li> 
	 
		<li><strong>Neural Networks and Deep Leaning Algorithms:</strong>
		Convolutional Network (CNN)
		</li> 
		
		<li><strong>Data Exploration and Preprocessing:</strong> The first part of a Machine Learning project understands the data and the problem at hand. Data cleaning, data transformation and data pre-processing are covered using Python functions to make data exploration and preprocessing relatively easy. 
		</li>
	
		<li> Injecting domain knowledge in the process, attributes are extracted from the data and engineered into Machine Learning algorithms.
		</li> 
</ul>
</div>
		  
		
		 
</div>

Python Scikit-Learn Machine Learning Course


Prerequisites: Basic knowledge of Python 
Our Style: Hands-on, Practical 
Location: Online, Instructor-led 
Download: anaconda.com 
Duration: 1 days
Times: 11am - 5pm 
How to attend: 
You can select your dates and use the link  https://meet.goto.com/910697125  to attend
Python Machine Learning Course Description
 Python Machine Learning algorithms can derive trends (learn) from data and make predictions on data by extrapolating on existing trends. Companies can take advantage of this to gain insights and ultimately improve business. 
 
Using Python Machine Learning scikit-learn, practice how to use Python Machine Learning algorithms to perform predictions on data. 
 
Learn the below listed algorithms, a small collection of available Python Machine Learning algorithms. 
 
Gain a good understanding of how to plan a Machine Learning project. We create, experiment, and run example code to implement the Python Machine Learning algorithms. 
Course Topics
Supervised Machine Learning: Classification Algorithms: Classification Algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine 
Regression Algorithms: Linear, Polynomial
Unsupervised Machine Learning: Clustering Algorithms: K-means clustering, Hierarchical Clustering 
Dimension Reduction Algorithms: Principal Component Analysis Latent Dirichlet allocation (LDA) 
Association Algorithms: Apriori, Euclat 
Other machine learning Algorithms: Reinforcement learning Algorithms: Q-Learning
Ensemble Methods Algorithms: Stacking, bagging, boosting.
Random Forest Random Forest, Gradient Boosting
Neural Networks and Deep Leaning Algorithms: Convolutional Network (CNN)
Data Exploration and Preprocessing: The first part of a Machine Learning project understands the data and the problem at hand. Data cleaning, data transformation and data pre-processing are covered using Python functions to make data exploration and preprocessing relatively easy.
Injecting domain knowledge in the process, attributes are extracted from the data and engineered into Machine Learning algorithms.

PCWorkshops

Unsupervised Machine Learning

Python

Python Machine Learning algorithms can derive trends (learn) from data and make predictions on data by extrapolating on existing trends. Companies can take advantage of this to gain insights and ultimately improve business. 
 
Using Python Machine Learning scikit-learn, practice how to use Python Machine Learning algorithms to perform predictions on data. 
 
Learn the below listed algorithms, a small collection of available Python Machine Learning algorithms. 

Python Machine Learning, online instructor-led 

General admission

<strong>Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning &amp; More!</strong>
        <p>Do you ever want to be a data scientist and build Machine Learning projects that can solve real-life problems? If yes, then this course is perfect for you.
You will train machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more!
Inside the course, you'll learn how to:
&bull;	Set up a Python development environment correctly
&bull;	Gain complete machine learning toolsets to tackle most real-world problems
&bull;	Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.
&bull;	Combine multiple models with by bagging, boosting or stacking
&bull;	Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data
&bull;	Develop in Jupyter (IPython) notebook, Spyder and various IDE
&bull;	Communicate visually and effectively with Matplotlib and Seaborn
&bull;	Engineer new features to improve algorithm predictions
&bull;	Make use of train/test, K-fold and Stratified K-fold cross-validation to select the correct model and predict model perform with unseen data
&bull;	Use SVM for handwriting recognition, and classification problems in general
&bull;	Use decision trees to predict staff attrition
&bull;	Apply the association rule to retail shopping datasets
&bull;	And much more!
By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real-life problems in your business, job or personal life with Machine Learning algorithms.</p>
        <h2>What You Will Learn</h2><p>&bull;&nbsp;&nbsp;Learn to Build Powerful Machine Learning Models to Solve Any Problem<br>&bull;&nbsp;&nbsp;Learn to Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells &amp; more</p>
        <h2>Audience</h2><p>A newbie who wants to learn machine learning algorithm with Python. Anyone who has a deep interest in the practical application of machine learning to real world problems. Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms. Any intermediate to advanced EXCEL users who is unable to work with large datasets. Anyone interested to present their findings in a professional and convincing manner. Anyone who wishes to start or transit into a career as a data scientist. Anyone who wants to apply machine learning to their domain.</p>
        <h2>Approach</h2><p>You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.</p>
        <h2>Key Features</h2><p>&bull;&nbsp;&nbsp;Solve any problem in your business or job with powerful Machine Learning models * &bull;&nbsp;&nbsp;Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, and unsupervised Machine Learning etc.</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/the-complete-machine-learning-course-with-python</p>
        <h2>About the Author</h2><h3>Anthony Ng</h3><p>Anthony Ng has spent almost 10 years in the education sector covering topics such as algorithmic trading, financial data analytics, investment, and portfolio management and more. He has worked in various financial institutions and has assisted Quantopian to conduct Algorithmic Trading Workshops in Singapore since 2016. He has also presented in QuantCon Singapore 2016 and 2017. He is passionate about finance, data science and Python and enjoys researching, teaching and sharing knowledge. He holds a Master of Science in Financial Engineering from NUS Singapore and MBA and Bcom from Otago University.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Does the Course Cover?</p><p style='margin: 0'>Introduction: What Does the Course Cover?</p></td></tr></tbody></table><br><h4>2. Getting Started with Anaconda</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. [Windows OS] Downloading & Installing Anaconda</p><p style='margin: 0'>Getting Started with Anaconda: [Windows OS] Downloading &amp; Installing Anaconda</p></td></tr><tr><td style='padding: 15px;'><p>2. [Windows OS] Managing Environment</p><p style='margin: 0'>Getting Started with Anaconda: [Windows OS] Managing Environment</p></td></tr><tr><td style='padding: 15px;'><p>3. Navigating the Spyder & Jupyter Notebook Interface</p><p style='margin: 0'>Getting Started with Anaconda: Navigating the Spyder &amp; Jupyter Notebook Interface</p></td></tr><tr><td style='padding: 15px;'><p>4. Downloading the IRIS Datasets</p><p style='margin: 0'>Getting Started with Anaconda: Downloading the IRIS Datasets</p></td></tr><tr><td style='padding: 15px;'><p>5. Data Exploration and Analysis</p><p style='margin: 0'>Getting Started with Anaconda: Data Exploration and Analysis</p></td></tr><tr><td style='padding: 15px;'><p>6. Presenting Your Data</p><p style='margin: 0'>Getting Started with Anaconda: Presenting Your Data</p></td></tr></tbody></table><br><h4>3. Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Regression: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Categories of Machine Learning</p><p style='margin: 0'>Regression: Categories of Machine Learning</p></td></tr><tr><td style='padding: 15px;'><p>3. Working with Scikit-Learn</p><p style='margin: 0'>Regression: Working with Scikit-Learn</p></td></tr><tr><td style='padding: 15px;'><p>4. Boston Housing Data - EDA</p><p style='margin: 0'>Regression: Boston Housing Data - EDA</p></td></tr><tr><td style='padding: 15px;'><p>5. Correlation Analysis and Feature Selection</p><p style='margin: 0'>Regression: Correlation Analysis and Feature Selection</p></td></tr><tr><td style='padding: 15px;'><p>6. Simple Linear Regression Modelling with Boston Housing Data</p><p style='margin: 0'>Regression: Simple Linear Regression Modelling with Boston Housing Data</p></td></tr><tr><td style='padding: 15px;'><p>7. Robust Regression</p><p style='margin: 0'>Regression: Robust Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Evaluate Model Performance</p><p style='margin: 0'>Regression: Evaluate Model Performance</p></td></tr><tr><td style='padding: 15px;'><p>9. Multiple Regression with statsmodel</p><p style='margin: 0'>Regression: Multiple Regression with statsmodel</p></td></tr><tr><td style='padding: 15px;'><p>10. Multiple Regression and Feature Importance</p><p style='margin: 0'>Regression: Multiple Regression and Feature Importance</p></td></tr><tr><td style='padding: 15px;'><p>11. Ordinary Least Square Regression and Gradient Descent</p><p style='margin: 0'>Regression: Ordinary Least Square Regression and Gradient Descent</p></td></tr><tr><td style='padding: 15px;'><p>12. Regularised Method for Regression</p><p style='margin: 0'>Regression: Regularised Method for Regression</p></td></tr><tr><td style='padding: 15px;'><p>13. Polynomial Regression</p><p style='margin: 0'>Regression: Polynomial Regression</p></td></tr><tr><td style='padding: 15px;'><p>14. Dealing with Non-linear relationships</p><p style='margin: 0'>Regression: Dealing with Non-linear relationships</p></td></tr><tr><td style='padding: 15px;'><p>15. Feature Importance Revisited</p><p style='margin: 0'>Regression: Feature Importance Revisited</p></td></tr><tr><td style='padding: 15px;'><p>16. Data Pre-Processing 1</p><p style='margin: 0'>Regression: Data Pre-Processing 1</p></td></tr><tr><td style='padding: 15px;'><p>17. Data Pre-Processing 2</p><p style='margin: 0'>Regression: Data Pre-Processing 2</p></td></tr><tr><td style='padding: 15px;'><p>18. Variance Bias Trade Off - Validation Curve</p><p style='margin: 0'>Regression: Variance Bias Trade Off - Validation Curve</p></td></tr><tr><td style='padding: 15px;'><p>19. Variance Bias Trade Off - Learning Curve</p><p style='margin: 0'>Regression: Variance Bias Trade Off - Learning Curve</p></td></tr><tr><td style='padding: 15px;'><p>20. Cross Validation</p><p style='margin: 0'>Regression: Cross Validation</p></td></tr></tbody></table><br><h4>4. Classification</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Classification: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic Regression 1</p><p style='margin: 0'>Classification: Logistic Regression 1</p></td></tr><tr><td style='padding: 15px;'><p>3. Logistic Regression 2</p><p style='margin: 0'>Classification: Logistic Regression 2</p></td></tr><tr><td style='padding: 15px;'><p>4. MNIST Project 1 - Introduction</p><p style='margin: 0'>Classification: MNIST Project 1 - Introduction</p></td></tr><tr><td style='padding: 15px;'><p>5. MNIST Project 2 - SGDClassifiers</p><p style='margin: 0'>Classification: MNIST Project 2 - SGDClassifier</p></td></tr><tr><td style='padding: 15px;'><p>6. MNIST Project 3 - Performance Measures</p><p style='margin: 0'>Classification: MNIST Project 3 - Performance Measures</p></td></tr><tr><td style='padding: 15px;'><p>7. MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score</p><p style='margin: 0'>Classification: MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score</p></td></tr><tr><td style='padding: 15px;'><p>8. MNIST Project 5 - Precision and Recall Tradeoff</p><p style='margin: 0'>Classification: MNIST Project 5 - Precision and Recall Tradeoff</p></td></tr><tr><td style='padding: 15px;'><p>9. MNIST Project 6 - The ROC Curve</p><p style='margin: 0'>Classification: MNIST Project 6 - The ROC Curve</p></td></tr></tbody></table><br><h4>5. Support Vector Machine (SVM)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Support Vector Machine (SVM): Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Support Vector Machine (SVM) Concepts</p><p style='margin: 0'>Support Vector Machine (SVM): Support Vector Machine (SVM) Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. Linear SVM Classification</p><p style='margin: 0'>Support Vector Machine (SVM): Linear SVM Classification</p></td></tr><tr><td style='padding: 15px;'><p>4. Polynomial Kernel</p><p style='margin: 0'>Support Vector Machine (SVM): Polynomial Kernel</p></td></tr><tr><td style='padding: 15px;'><p>5. Gaussian Radial Basis Function</p><p style='margin: 0'>Support Vector Machine (SVM): Gaussian Radial Basis Function</p></td></tr><tr><td style='padding: 15px;'><p>6. Support Vector Regression</p><p style='margin: 0'>Support Vector Machine (SVM): Support Vector Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Advantages and Disadvantages of SVM</p><p style='margin: 0'>Support Vector Machine (SVM): Advantages and Disadvantages of SVM</p></td></tr></tbody></table><br><h4>6. Tree</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Tree: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Decision Tree</p><p style='margin: 0'>Tree: What is Decision Tree</p></td></tr><tr><td style='padding: 15px;'><p>3. Training a Decision Tree</p><p style='margin: 0'>Tree: Training a Decision Tree</p></td></tr><tr><td style='padding: 15px;'><p>4. Visualising a Decision Trees</p><p style='margin: 0'>Tree: Visualising a Decision Trees</p></td></tr><tr><td style='padding: 15px;'><p>5. Decision Tree Learning Algorithm</p><p style='margin: 0'>Tree: Decision Tree Learning Algorithm</p></td></tr><tr><td style='padding: 15px;'><p>6. Decision Tree Regression</p><p style='margin: 0'>Tree: Decision Tree Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Overfitting and Grid Search</p><p style='margin: 0'>Tree: Overfitting and Grid Search</p></td></tr><tr><td style='padding: 15px;'><p>8. Where to From Here</p><p style='margin: 0'>Tree: Where to From Here</p></td></tr><tr><td style='padding: 15px;'><p>9. Project HR - Loading and preprocesing data</p><p style='margin: 0'>Tree: Project HR - Loading and preprocesing data</p></td></tr><tr><td style='padding: 15px;'><p>10. Project HR - Modelling</p><p style='margin: 0'>Tree: Project HR - Modelling</p></td></tr></tbody></table><br><h4>7. Ensemble Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Ensemble Machine Learning: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Ensemble Learning Methods Introduction</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble Learning Methods Introduction</p></td></tr><tr><td style='padding: 15px;'><p>3. Bagging Part 1</p><p style='margin: 0'>Ensemble Machine Learning: Bagging Part 1</p></td></tr><tr><td style='padding: 15px;'><p>4. Bagging Part 2</p><p style='margin: 0'>Ensemble Machine Learning: Bagging Part 2</p></td></tr><tr><td style='padding: 15px;'><p>5. Random Forests</p><p style='margin: 0'>Ensemble Machine Learning: Random Forests</p></td></tr><tr><td style='padding: 15px;'><p>6. Extra-Trees</p><p style='margin: 0'>Ensemble Machine Learning: Extra-Trees</p></td></tr><tr><td style='padding: 15px;'><p>7. AdaBoost</p><p style='margin: 0'>Ensemble Machine Learning: Decision Tree Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Gradient Boosting Machine</p><p style='margin: 0'>Ensemble Machine Learning: Gradient Boosting Machine</p></td></tr><tr><td style='padding: 15px;'><p>9. XGBoost</p><p style='margin: 0'>Ensemble Machine Learning: XGBoost</p></td></tr><tr><td style='padding: 15px;'><p>10. Project HR - Human Resources Analytics</p><p style='margin: 0'>Ensemble Machine Learning: Project HR - Human Resources Analytics</p></td></tr><tr><td style='padding: 15px;'><p>11. Ensemble of ensembles Part 1</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble of ensembles Part 1</p></td></tr><tr><td style='padding: 15px;'><p>12. Ensemble of ensembles Part 2</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble of ensembles Part 2</p></td></tr></tbody></table><br><h4>8. k-Nearest Neighbours (kNN)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. kNN Introduction</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. kNN Concepts</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. kNN and Iris Dataset Demo</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN and Iris Dataset Demo</p></td></tr><tr><td style='padding: 15px;'><p>4. Distance Metric</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Distance Metric</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Cancer Detection Part 1</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Project Cancer Detection Part 1</p></td></tr><tr><td style='padding: 15px;'><p>6. Project Cancer Detection Part 2</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Project Cancer Detection Part 2</p></td></tr></tbody></table><br><h4>9. Dimensionality Reduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Dimensionality Reduction: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Dimensionality Reduction Concept</p><p style='margin: 0'>Dimensionality Reduction: Dimensionality Reduction Concept</p></td></tr><tr><td style='padding: 15px;'><p>3. PCA Introduction</p><p style='margin: 0'>Dimensionality Reduction: PCA Introduction</p></td></tr><tr><td style='padding: 15px;'><p>4. Dimensionality Reduction Demo</p><p style='margin: 0'>Dimensionality Reduction: Dimensionality Reduction Demo</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Wine 1: Dimensionality Reduction with PCA</p><p style='margin: 0'>Dimensionality Reduction: Project Wine 1: Dimensionality Reduction with PCA</p></td></tr><tr><td style='padding: 15px;'><p>6. Project Wine 2: Choosing the Number of Components</p><p style='margin: 0'>Dimensionality Reduction: Project Wine 2: Choosing the Number of Components</p></td></tr><tr><td style='padding: 15px;'><p>7. Kernel PCA</p><p style='margin: 0'>Dimensionality Reduction: Kernel PCA</p></td></tr><tr><td style='padding: 15px;'><p>8. Kernel PCA Demo</p><p style='margin: 0'>Dimensionality Reduction: Kernel PCA Demo</p></td></tr><tr><td style='padding: 15px;'><p>9. LDA & Comparison between LDA and PCA</p><p style='margin: 0'>Dimensionality Reduction: LDA &amp; Comparison between LDA and PCA</p></td></tr></tbody></table><br><h4>10. Unsupervised Learning: Clustering</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Unsupervised Learning: Clustering: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Clustering Concepts</p><p style='margin: 0'>Unsupervised Learning: Clustering: Clustering Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. MLextend</p><p style='margin: 0'>Unsupervised Learning: Clustering: MLextend</p></td></tr><tr><td style='padding: 15px;'><p>4. Ward's Agglomerative Hierarchical Clustering</p><p style='margin: 0'>Unsupervised Learning: Clustering: Ward's Agglomerative Hierarchical Clustering</p></td></tr><tr><td style='padding: 15px;'><p>5. Truncating Dendrogram</p><p style='margin: 0'>Unsupervised Learning: Clustering: Truncating Dendrogram</p></td></tr><tr><td style='padding: 15px;'><p>6. k-Means Clustering</p><p style='margin: 0'>Unsupervised Learning: Clustering: k-Means Clustering</p></td></tr><tr><td style='padding: 15px;'><p>7. Elbow Method</p><p style='margin: 0'>Unsupervised Learning: Clustering: Elbow Method</p></td></tr><tr><td style='padding: 15px;'><p>8. Silhouette Analysis</p><p style='margin: 0'>Unsupervised Learning: Clustering: Silhouette Analysis</p></td></tr><tr><td style='padding: 15px;'><p>9. Mean Shift</p><p style='margin: 0'>Unsupervised Learning: Clustering: Mean Shift</p></td></tr></tbody></table>

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!
Do you ever want to be a data scientist and build Machine Learning projects that can solve real-life problems? If yes, then this course is perfect for you.
You will train machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more!
Inside the course, you'll learn how to:
•	Set up a Python development environment correctly
•	Gain complete machine learning toolsets to tackle most real-world problems
•	Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.
•	Combine multiple models with by bagging, boosting or stacking
•	Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data
•	Develop in Jupyter (IPython) notebook, Spyder and various IDE
•	Communicate visually and effectively with Matplotlib and Seaborn
•	Engineer new features to improve algorithm predictions
•	Make use of train/test, K-fold and Stratified K-fold cross-validation to select the correct model and predict model perform with unseen data
•	Use SVM for handwriting recognition, and classification problems in general
•	Use decision trees to predict staff attrition
•	Apply the association rule to retail shopping datasets
•	And much more!
By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real-life problems in your business, job or personal life with Machine Learning algorithms.
What You Will Learn
• Learn to Build Powerful Machine Learning Models to Solve Any Problem
• Learn to Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells & more
Audience
A newbie who wants to learn machine learning algorithm with Python. Anyone who has a deep interest in the practical application of machine learning to real world problems. Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms. Any intermediate to advanced EXCEL users who is unable to work with large datasets. Anyone interested to present their findings in a professional and convincing manner. Anyone who wishes to start or transit into a career as a data scientist. Anyone who wants to apply machine learning to their domain.
Approach
You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.
Key Features
• Solve any problem in your business or job with powerful Machine Learning models * • Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, and unsupervised Machine Learning etc.
Github Repo
https://github.com/packtpublishing/the-complete-machine-learning-course-with-python
About the Author
Anthony Ng
Anthony Ng has spent almost 10 years in the education sector covering topics such as algorithmic trading, financial data analytics, investment, and portfolio management and more. He has worked in various financial institutions and has assisted Quantopian to conduct Algorithmic Trading Workshops in Singapore since 2016. He has also presented in QuantCon Singapore 2016 and 2017. He is passionate about finance, data science and Python and enjoys researching, teaching and sharing knowledge. He holds a Master of Science in Financial Engineering from NUS Singapore and MBA and Bcom from Otago University.
Course Outline
1. Introduction

1. What Does the Course Cover?
Introduction: What Does the Course Cover?
2. Getting Started with Anaconda

1. [Windows OS] Downloading & Installing Anaconda
Getting Started with Anaconda: [Windows OS] Downloading & Installing Anaconda
2. [Windows OS] Managing Environment
Getting Started with Anaconda: [Windows OS] Managing Environment
3. Navigating the Spyder & Jupyter Notebook Interface
Getting Started with Anaconda: Navigating the Spyder & Jupyter Notebook Interface
4. Downloading the IRIS Datasets
Getting Started with Anaconda: Downloading the IRIS Datasets
5. Data Exploration and Analysis
Getting Started with Anaconda: Data Exploration and Analysis
6. Presenting Your Data
Getting Started with Anaconda: Presenting Your Data
3. Regression

1. Introduction
Regression: Introduction
2. Categories of Machine Learning
Regression: Categories of Machine Learning
3. Working with Scikit-Learn
Regression: Working with Scikit-Learn
4. Boston Housing Data - EDA
Regression: Boston Housing Data - EDA
5. Correlation Analysis and Feature Selection
Regression: Correlation Analysis and Feature Selection
6. Simple Linear Regression Modelling with Boston Housing Data
Regression: Simple Linear Regression Modelling with Boston Housing Data
7. Robust Regression
Regression: Robust Regression
8. Evaluate Model Performance
Regression: Evaluate Model Performance
9. Multiple Regression with statsmodel
Regression: Multiple Regression with statsmodel
10. Multiple Regression and Feature Importance
Regression: Multiple Regression and Feature Importance
11. Ordinary Least Square Regression and Gradient Descent
Regression: Ordinary Least Square Regression and Gradient Descent
12. Regularised Method for Regression
Regression: Regularised Method for Regression
13. Polynomial Regression
Regression: Polynomial Regression
14. Dealing with Non-linear relationships
Regression: Dealing with Non-linear relationships
15. Feature Importance Revisited
Regression: Feature Importance Revisited
16. Data Pre-Processing 1
Regression: Data Pre-Processing 1
17. Data Pre-Processing 2
Regression: Data Pre-Processing 2
18. Variance Bias Trade Off - Validation Curve
Regression: Variance Bias Trade Off - Validation Curve
19. Variance Bias Trade Off - Learning Curve
Regression: Variance Bias Trade Off - Learning Curve
20. Cross Validation
Regression: Cross Validation
4. Classification

1. Introduction
Classification: Introduction
2. Logistic Regression 1
Classification: Logistic Regression 1
3. Logistic Regression 2
Classification: Logistic Regression 2
4. MNIST Project 1 - Introduction
Classification: MNIST Project 1 - Introduction
5. MNIST Project 2 - SGDClassifiers
Classification: MNIST Project 2 - SGDClassifier
6. MNIST Project 3 - Performance Measures
Classification: MNIST Project 3 - Performance Measures
7. MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score
Classification: MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score
8. MNIST Project 5 - Precision and Recall Tradeoff
Classification: MNIST Project 5 - Precision and Recall Tradeoff
9. MNIST Project 6 - The ROC Curve
Classification: MNIST Project 6 - The ROC Curve
5. Support Vector Machine (SVM)

1. Introduction
Support Vector Machine (SVM): Introduction
2. Support Vector Machine (SVM) Concepts
Support Vector Machine (SVM): Support Vector Machine (SVM) Concepts
3. Linear SVM Classification
Support Vector Machine (SVM): Linear SVM Classification
4. Polynomial Kernel
Support Vector Machine (SVM): Polynomial Kernel
5. Gaussian Radial Basis Function
Support Vector Machine (SVM): Gaussian Radial Basis Function
6. Support Vector Regression
Support Vector Machine (SVM): Support Vector Regression
7. Advantages and Disadvantages of SVM
Support Vector Machine (SVM): Advantages and Disadvantages of SVM
6. Tree

1. Introduction
Tree: Introduction
2. What is Decision Tree
Tree: What is Decision Tree
3. Training a Decision Tree
Tree: Training a Decision Tree
4. Visualising a Decision Trees
Tree: Visualising a Decision Trees
5. Decision Tree Learning Algorithm
Tree: Decision Tree Learning Algorithm
6. Decision Tree Regression
Tree: Decision Tree Regression
7. Overfitting and Grid Search
Tree: Overfitting and Grid Search
8. Where to From Here
Tree: Where to From Here
9. Project HR - Loading and preprocesing data
Tree: Project HR - Loading and preprocesing data
10. Project HR - Modelling
Tree: Project HR - Modelling
7. Ensemble Machine Learning

1. Introduction
Ensemble Machine Learning: Introduction
2. Ensemble Learning Methods Introduction
Ensemble Machine Learning: Ensemble Learning Methods Introduction
3. Bagging Part 1
Ensemble Machine Learning: Bagging Part 1
4. Bagging Part 2
Ensemble Machine Learning: Bagging Part 2
5. Random Forests
Ensemble Machine Learning: Random Forests
6. Extra-Trees
Ensemble Machine Learning: Extra-Trees
7. AdaBoost
Ensemble Machine Learning: Decision Tree Regression
8. Gradient Boosting Machine
Ensemble Machine Learning: Gradient Boosting Machine
9. XGBoost
Ensemble Machine Learning: XGBoost
10. Project HR - Human Resources Analytics
Ensemble Machine Learning: Project HR - Human Resources Analytics
11. Ensemble of ensembles Part 1
Ensemble Machine Learning: Ensemble of ensembles Part 1
12. Ensemble of ensembles Part 2
Ensemble Machine Learning: Ensemble of ensembles Part 2
8. k-Nearest Neighbours (kNN)

1. kNN Introduction
K-Nearest Neighbours (kNN): kNN Introduction
2. kNN Concepts
K-Nearest Neighbours (kNN): kNN Concepts
3. kNN and Iris Dataset Demo
K-Nearest Neighbours (kNN): kNN and Iris Dataset Demo
4. Distance Metric
K-Nearest Neighbours (kNN): Distance Metric
5. Project Cancer Detection Part 1
K-Nearest Neighbours (kNN): Project Cancer Detection Part 1
6. Project Cancer Detection Part 2
K-Nearest Neighbours (kNN): Project Cancer Detection Part 2
9. Dimensionality Reduction

1. Introduction
Dimensionality Reduction: Introduction
2. Dimensionality Reduction Concept
Dimensionality Reduction: Dimensionality Reduction Concept
3. PCA Introduction
Dimensionality Reduction: PCA Introduction
4. Dimensionality Reduction Demo
Dimensionality Reduction: Dimensionality Reduction Demo
5. Project Wine 1: Dimensionality Reduction with PCA
Dimensionality Reduction: Project Wine 1: Dimensionality Reduction with PCA
6. Project Wine 2: Choosing the Number of Components
Dimensionality Reduction: Project Wine 2: Choosing the Number of Components
7. Kernel PCA
Dimensionality Reduction: Kernel PCA
8. Kernel PCA Demo
Dimensionality Reduction: Kernel PCA Demo
9. LDA & Comparison between LDA and PCA
Dimensionality Reduction: LDA & Comparison between LDA and PCA
10. Unsupervised Learning: Clustering

1. Introduction
Unsupervised Learning: Clustering: Introduction
2. Clustering Concepts
Unsupervised Learning: Clustering: Clustering Concepts
3. MLextend
Unsupervised Learning: Clustering: MLextend
4. Ward's Agglomerative Hierarchical Clustering
Unsupervised Learning: Clustering: Ward's Agglomerative Hierarchical Clustering
5. Truncating Dendrogram
Unsupervised Learning: Clustering: Truncating Dendrogram
6. k-Means Clustering
Unsupervised Learning: Clustering: k-Means Clustering
7. Elbow Method
Unsupervised Learning: Clustering: Elbow Method
8. Silhouette Analysis
Unsupervised Learning: Clustering: Silhouette Analysis
9. Mean Shift
Unsupervised Learning: Clustering: Mean Shift

Packt

TensorFlow

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning &amp; More!

The Complete Machine Learning Course with Python

General Admission

<div id="course-pills-1">Duration<p>2 Days</p><p>12 CPD hours</p>This course is intended for<p>Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming<br />Also familiar with Python</p>Overview<p>? NumPy, pandas, Matplotlib,  scikit-learn<br />? Python REPLs<br />? Jupyter Notebooks<br />? Data analytics life-cycle phases<br />? Data repairing and normalizing<br />? Data aggregation and grouping<br />? Data visualization<br />? Data science algorithms for supervised and unsupervised machine learning</p><p>Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases.</p></div><div id="course-pills-2"><h4>Python for Data Science</h4><ul><li>? Using Modules</li><li>? Listing Methods in a Module</li><li>? Creating Your Own Modules</li><li>? List Comprehension</li><li>? Dictionary Comprehension</li><li>? String Comprehension</li><li>? Python 2 vs Python 3</li><li>? Sets (Python 3+)</li><li>? Python Idioms</li><li>? Python Data Science ?Ecosystem?</li><li>? NumPy</li><li>? NumPy Arrays</li><li>? NumPy Idioms</li><li>? pandas</li><li>? Data Wrangling with pandas' DataFrame</li><li>? SciPy</li><li>? Scikit-learn</li><li>? SciPy or scikit-learn?</li><li>? Matplotlib</li><li>? Python vs R</li><li>? Python on Apache Spark</li><li>? Python Dev Tools and REPLs</li><li>? Anaconda</li><li>? IPython</li><li>? Visual Studio Code</li><li>? Jupyter</li><li>? Jupyter Basic Commands</li><li>? Summary</li></ul><h4>Applied Data Science</h4><ul><li>? What is Data Science?</li><li>? Data Science Ecosystem</li><li>? Data Mining vs. Data Science</li><li>? Business Analytics vs. Data Science</li><li>? Data Science, Machine Learning, AI?</li><li>? Who is a Data Scientist?</li><li>? Data Science Skill Sets Venn Diagram</li><li>? Data Scientists at Work</li><li>? Examples of Data Science Projects</li><li>? An Example of a Data Product</li><li>? Applied Data Science at Google</li><li>? Data Science Gotchas</li><li>? Summary</li></ul><h4>Data Analytics Life-cycle Phases</h4><ul><li>? Big Data Analytics Pipeline</li><li>? Data Discovery Phase</li><li>? Data Harvesting Phase</li><li>? Data Priming Phase</li><li>? Data Logistics and Data Governance</li><li>? Exploratory Data Analysis</li><li>? Model Planning Phase</li><li>? Model Building Phase</li><li>? Communicating the Results</li><li>? Production Roll-out</li><li>? Summary</li></ul><h4>Repairing and Normalizing Data</h4><ul><li>? Repairing and Normalizing Data</li><li>? Dealing with the Missing Data</li><li>? Sample Data Set</li><li>? Getting Info on Null Data</li><li>? Dropping a Column</li><li>? Interpolating Missing Data in pandas</li><li>? Replacing the Missing Values with the Mean Value</li><li>? Scaling (Normalizing) the Data</li><li>? Data Preprocessing with scikit-learn</li><li>? Scaling with the scale() Function</li><li>? The MinMaxScaler Object</li><li>? Summary</li></ul><h4>Descriptive Statistics Computing Features in Python</h4><ul><li>? Descriptive Statistics</li><li>? Non-uniformity of a Probability Distribution</li><li>? Using NumPy for Calculating Descriptive Statistics Measures</li><li>? Finding Min and Max in NumPy</li><li>? Using pandas for Calculating Descriptive Statistics Measures</li><li>? Correlation</li><li>? Regression and Correlation</li><li>? Covariance</li><li>? Getting Pairwise Correlation and Covariance Measures</li><li>? Finding Min and Max in pandas DataFrame</li><li>? Summary</li></ul><h4>Data Aggregation and Grouping</h4><ul><li>? Data Aggregation and Grouping</li><li>? Sample Data Set</li><li>? The pandas.core.groupby.SeriesGroupBy Object</li><li>? Grouping by Two or More Columns</li><li>? Emulating the SQL's WHERE Clause</li><li>? The Pivot Tables</li><li>? Cross-Tabulation</li><li>? Summary</li></ul><h4>Data Visualization with matplotlib</h4><ul><li>? Data Visualization<br />? What is matplotlib?<br />? Getting Started with matplotlib<br />? The Plotting Window<br />? The Figure Options<br />? The matplotlib.pyplot.plot() Function<br />? The matplotlib.pyplot.bar() Function<br />? The matplotlib.pyplot.pie () Function<br />? Subplots<br />? Using the matplotlib.gridspec.GridSpec Object<br />? The matplotlib.pyplot.subplot() Function<br />? Hands-on Exercise<br />? Figures<br />? Saving Figures to File<br />? Visualization with pandas<br />? Working with matplotlib in Jupyter Notebooks<br />? Summary</li></ul><h4>Data Science and ML Algorithms in scikit-learn</h4><ul><li>? Data Science, Machine Learning, AI?</li><li>? Types of Machine Learning</li><li>? Terminology: Features and Observations</li><li>? Continuous and Categorical Features (Variables)</li><li>? Terminology: Axis</li><li>? The scikit-learn Package</li><li>? scikit-learn Estimators</li><li>? Models, Estimators, and Predictors</li><li>? Common Distance Metrics</li><li>? The Euclidean Metric</li><li>? The LIBSVM format</li><li>?  Scaling of the Features</li><li>? The Curse of Dimensionality</li><li>? Supervised vs Unsupervised Machine Learning</li><li>? Supervised Machine Learning Algorithms</li><li>? Unsupervised Machine Learning Algorithms</li><li>? Choose the Right Algorithm</li><li>? Life-cycles of Machine Learning Development</li><li>? Data Split for Training and Test Data Sets</li><li>? Data Splitting in scikit-learn</li><li>? Hands-on Exercise</li><li>? Classification Examples</li><li>? Classifying with k-Nearest Neighbors (SL)</li><li>? k-Nearest Neighbors Algorithm</li><li>? k-Nearest Neighbors Algorithm</li><li>? The Error Rate</li><li>? Hands-on Exercise</li><li>? Dimensionality Reduction</li><li>? The Advantages of Dimensionality Reduction</li><li>? Principal component analysis (PCA)</li><li>? Hands-on Exercise</li><li>? Data Blending</li><li>? Decision Trees (SL)</li><li>? Decision Tree Terminology</li><li>? Decision Tree Classification in Context of Information Theory</li><li>? Information Entropy Defined</li><li>? The Shannon Entropy Formula</li><li>? The Simplified Decision Tree Algorithm</li><li>? Using Decision Trees</li><li>? Random Forests</li><li>? SVM</li><li>? Naive Bayes Classifier (SL)</li><li>? Naive Bayesian Probabilistic Model in a Nutshell</li><li>? Bayes Formula</li><li>? Classification of Documents with Naive Bayes</li><li>? Unsupervised Learning Type: Clustering</li><li>? Clustering Examples</li><li>? k-Means Clustering (UL)</li><li>? k-Means Clustering in a Nutshell</li><li>? k-Means Characteristics</li><li>? Regression Analysis</li><li>? Simple Linear Regression Model</li><li>? Linear vs Non-Linear Regression</li><li>? Linear Regression Illustration</li><li>? Major Underlying Assumptions for Regression Analysis</li><li>? Least-Squares Method (LSM)</li><li>? Locally Weighted Linear Regression</li><li>? Regression Models in Excel</li><li>? Multiple Regression Analysis</li><li>? Logistic Regression</li><li>? Regression vs Classification</li><li>? Time-Series Analysis</li><li>? Decomposing Time-Series</li><li>? Summary</li></ul><h4>Lab Exercises</h4><ul><li>Lab 1 - Learning the Lab Environment     </li><li>Lab 2 -  Using Jupyter Notebook </li><li>Lab 3 -  Repairing and Normalizing Data  </li><li>Lab 4 -  Computing Descriptive Statistics </li><li>Lab 5 - Data Grouping and Aggregation   </li><li>Lab 6 - Data Visualization with matplotlib              </li><li>Lab 7 - Data Splitting       </li><li>Lab 8 -  k-Nearest Neighbors Algorithm  </li><li>Lab 9 -  The k-means Algorithm  </li><li>Lab 10 -  The Random Forest Algorithm </li></ul></div>

Duration
2 Days
12 CPD hours
This course is intended for
Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming
Also familiar with Python
Overview
? NumPy, pandas, Matplotlib, scikit-learn
? Python REPLs
? Jupyter Notebooks
? Data analytics life-cycle phases
? Data repairing and normalizing
? Data aggregation and grouping
? Data visualization
? Data science algorithms for supervised and unsupervised machine learning
Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases.
Python for Data Science
? Using Modules
? Listing Methods in a Module
? Creating Your Own Modules
? List Comprehension
? Dictionary Comprehension
? String Comprehension
? Python 2 vs Python 3
? Sets (Python 3+)
? Python Idioms
? Python Data Science ?Ecosystem?
? NumPy
? NumPy Arrays
? NumPy Idioms
? pandas
? Data Wrangling with pandas' DataFrame
? SciPy
? Scikit-learn
? SciPy or scikit-learn?
? Matplotlib
? Python vs R
? Python on Apache Spark
? Python Dev Tools and REPLs
? Anaconda
? IPython
? Visual Studio Code
? Jupyter
? Jupyter Basic Commands
? Summary
Applied Data Science
? What is Data Science?
? Data Science Ecosystem
? Data Mining vs. Data Science
? Business Analytics vs. Data Science
? Data Science, Machine Learning, AI?
? Who is a Data Scientist?
? Data Science Skill Sets Venn Diagram
? Data Scientists at Work
? Examples of Data Science Projects
? An Example of a Data Product
? Applied Data Science at Google
? Data Science Gotchas
? Summary
Data Analytics Life-cycle Phases
? Big Data Analytics Pipeline
? Data Discovery Phase
? Data Harvesting Phase
? Data Priming Phase
? Data Logistics and Data Governance
? Exploratory Data Analysis
? Model Planning Phase
? Model Building Phase
? Communicating the Results
? Production Roll-out
? Summary
Repairing and Normalizing Data
? Repairing and Normalizing Data
? Dealing with the Missing Data
? Sample Data Set
? Getting Info on Null Data
? Dropping a Column
? Interpolating Missing Data in pandas
? Replacing the Missing Values with the Mean Value
? Scaling (Normalizing) the Data
? Data Preprocessing with scikit-learn
? Scaling with the scale() Function
? The MinMaxScaler Object
? Summary
Descriptive Statistics Computing Features in Python
? Descriptive Statistics
? Non-uniformity of a Probability Distribution
? Using NumPy for Calculating Descriptive Statistics Measures
? Finding Min and Max in NumPy
? Using pandas for Calculating Descriptive Statistics Measures
? Correlation
? Regression and Correlation
? Covariance
? Getting Pairwise Correlation and Covariance Measures
? Finding Min and Max in pandas DataFrame
? Summary
Data Aggregation and Grouping
? Data Aggregation and Grouping
? Sample Data Set
? The pandas.core.groupby.SeriesGroupBy Object
? Grouping by Two or More Columns
? Emulating the SQL's WHERE Clause
? The Pivot Tables
? Cross-Tabulation
? Summary
Data Visualization with matplotlib
? Data Visualization
? What is matplotlib?
? Getting Started with matplotlib
? The Plotting Window
? The Figure Options
? The matplotlib.pyplot.plot() Function
? The matplotlib.pyplot.bar() Function
? The matplotlib.pyplot.pie () Function
? Subplots
? Using the matplotlib.gridspec.GridSpec Object
? The matplotlib.pyplot.subplot() Function
? Hands-on Exercise
? Figures
? Saving Figures to File
? Visualization with pandas
? Working with matplotlib in Jupyter Notebooks
? Summary
Data Science and ML Algorithms in scikit-learn
? Data Science, Machine Learning, AI?
? Types of Machine Learning
? Terminology: Features and Observations
? Continuous and Categorical Features (Variables)
? Terminology: Axis
? The scikit-learn Package
? scikit-learn Estimators
? Models, Estimators, and Predictors
? Common Distance Metrics
? The Euclidean Metric
? The LIBSVM format
? Scaling of the Features
? The Curse of Dimensionality
? Supervised vs Unsupervised Machine Learning
? Supervised Machine Learning Algorithms
? Unsupervised Machine Learning Algorithms
? Choose the Right Algorithm
? Life-cycles of Machine Learning Development
? Data Split for Training and Test Data Sets
? Data Splitting in scikit-learn
? Hands-on Exercise
? Classification Examples
? Classifying with k-Nearest Neighbors (SL)
? k-Nearest Neighbors Algorithm
? k-Nearest Neighbors Algorithm
? The Error Rate
? Hands-on Exercise
? Dimensionality Reduction
? The Advantages of Dimensionality Reduction
? Principal component analysis (PCA)
? Hands-on Exercise
? Data Blending
? Decision Trees (SL)
? Decision Tree Terminology
? Decision Tree Classification in Context of Information Theory
? Information Entropy Defined
? The Shannon Entropy Formula
? The Simplified Decision Tree Algorithm
? Using Decision Trees
? Random Forests
? SVM
? Naive Bayes Classifier (SL)
? Naive Bayesian Probabilistic Model in a Nutshell
? Bayes Formula
? Classification of Documents with Naive Bayes
? Unsupervised Learning Type: Clustering
? Clustering Examples
? k-Means Clustering (UL)
? k-Means Clustering in a Nutshell
? k-Means Characteristics
? Regression Analysis
? Simple Linear Regression Model
? Linear vs Non-Linear Regression
? Linear Regression Illustration
? Major Underlying Assumptions for Regression Analysis
? Least-Squares Method (LSM)
? Locally Weighted Linear Regression
? Regression Models in Excel
? Multiple Regression Analysis
? Logistic Regression
? Regression vs Classification
? Time-Series Analysis
? Decomposing Time-Series
? Summary
Lab Exercises
Lab 1 - Learning the Lab Environment
Lab 2 - Using Jupyter Notebook
Lab 3 - Repairing and Normalizing Data
Lab 4 - Computing Descriptive Statistics
Lab 5 - Data Grouping and Aggregation
Lab 6 - Data Visualization with matplotlib
Lab 7 - Data Splitting
Lab 8 - k-Nearest Neighbors Algorithm
Lab 9 - The k-means Algorithm
Lab 10 - The Random Forest Algorithm

Nexus Human

Data Engineering

Machine Learning (ML)

Big Data Analytics

Data Science

Data Mining

Data Skills

Python With Data Science

<div style="color: rgb(57, 54, 79); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(57, 54, 79);"><div data-testid="summary" id="isPasted" fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">This Python Machine Learning online instructor led course is an excellent introduction to popular machine learning algorithms.</strong></p></div><div data-testid="organizerBrief" fr-original-style="" style="box-sizing: border-box;"><div data-testid="creator-signal-main-section" fr-original-style="" style="box-sizing: border-box;"><section fr-original-style="" style="box-sizing: border-box;"></section></div></div><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></strong></p><hr fr-original-style="" style="border-color: var(--border-muted); border-style: solid; clear: both; break-after: page; user-select: none; box-sizing: border-box;"><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Machine Learning 2-day Course</strong></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><em fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Prerequisites:</strong></em></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">Basic knowledge of Python coding is a pre-requisite.</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><em fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Who Should Attend?</strong></em></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">This course is an overview of machine learning and machine learning algorithms in Python SciKitLearn.</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Practical:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">We cover the below listed algorithms, which is only a small collection of what is available. However, it will give you a good understanding, to plan your Machine Learning project</li><li fr-original-style="" style="box-sizing: border-box;">We create, experiment and run machine learning sample code to implement a short selected but representative list of available the algorithms.&nbsp;</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course Outline:</strong></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Supervised Machine Learning:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">Classification Algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine</li><li fr-original-style="" style="box-sizing: border-box;">Regression Algorithms: Linear, Polynomial</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Unsupervised Machine Learning:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">Clustering Algorithms: K-means clustering, Hierarchical Clustering</li><li fr-original-style="" style="box-sizing: border-box;">Dimension Reduction Algorithms: Principal Component Analysis Latent Dirichlet allocation (LDA)</li><li fr-original-style="" style="box-sizing: border-box;">Association Machine Learning Algorithms: Apriori, Euclat</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Other machine learning Algorithms:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">Ensemble Methods ( Stacking, bagging, boosting ) Algorithms: Random Forest, Gradient Boosting</li><li fr-original-style="" style="box-sizing: border-box;">Reinforcement learning Algorithms: Q-Learning</li><li fr-original-style="" style="box-sizing: border-box;">Neural Networks and Deep Leaning Algorithms: Convolutional Network (CNN)</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Exploration and Preprocessing:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">The first part of a Machine Learning project understands the data and the problem at hand.</li><li fr-original-style="" style="box-sizing: border-box;">Data cleaning, data transformation and data pre-processing are covered using Python functions to make data exploration and preprocessing relatively easy.</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">What is included in this Python Machine Learning:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">Python Machine Learning Certificate on completion &nbsp;</li><li fr-original-style="" style="box-sizing: border-box;">Python Machine Learning notes</li><li fr-original-style="" style="box-sizing: border-box;">Practical Python Machine Learning exercises and code examples</li><li fr-original-style="" style="box-sizing: border-box;">After the course, 1 free, online session for questions or revision Python Machine Learning.</li><li fr-original-style="" style="box-sizing: border-box;">Max group size on this Python Machine Learning is 4.</li></ul></div><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></p><div style="color: rgb(30, 10, 60); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(30, 10, 60);"><hr fr-original-style="" style="border-color: var(--border-muted); border-style: solid; clear: both; break-after: page; user-select: none; box-sizing: border-box;"><h2 fr-original-style="" style="box-sizing: border-box;">Refund Policy</h2></div><div style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">No Refunds</div>

This Python Machine Learning online instructor led course is an excellent introduction to popular machine learning algorithms.





Python Machine Learning 2-day Course
Prerequisites:
Basic knowledge of Python coding is a pre-requisite.
Who Should Attend?
This course is an overview of machine learning and machine learning algorithms in Python SciKitLearn.
Practical:
We cover the below listed algorithms, which is only a small collection of what is available. However, it will give you a good understanding, to plan your Machine Learning project
We create, experiment and run machine learning sample code to implement a short selected but representative list of available the algorithms.
Course Outline:
Supervised Machine Learning:
Classification Algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine
Regression Algorithms: Linear, Polynomial
Unsupervised Machine Learning:
Clustering Algorithms: K-means clustering, Hierarchical Clustering
Dimension Reduction Algorithms: Principal Component Analysis Latent Dirichlet allocation (LDA)
Association Machine Learning Algorithms: Apriori, Euclat
Other machine learning Algorithms:
Ensemble Methods ( Stacking, bagging, boosting ) Algorithms: Random Forest, Gradient Boosting
Reinforcement learning Algorithms: Q-Learning
Neural Networks and Deep Leaning Algorithms: Convolutional Network (CNN)
Data Exploration and Preprocessing:
The first part of a Machine Learning project understands the data and the problem at hand.
Data cleaning, data transformation and data pre-processing are covered using Python functions to make data exploration and preprocessing relatively easy.


What is included in this Python Machine Learning:
Python Machine Learning Certificate on completion
Python Machine Learning notes
Practical Python Machine Learning exercises and code examples
After the course, 1 free, online session for questions or revision Python Machine Learning.
Max group size on this Python Machine Learning is 4.





Refund Policy
No Refunds

Python Machine Learning Course, 1-Days, Online Attendance

Python Basics 1-days Online Instructor-Led

<h2 style="color: var(--text-heading); box-sizing: border-box;" id="isPasted" fr-original-style="color: var(--text-heading);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">This course at a glance</span></strong></h2><ul style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Accredited by CPDQE</li><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Learn how to clean, process, wrangle, and manipulate data</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Know how to create a resume for a data science job</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Get a grasp of NumPy by learning how to use it for numerical data</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Uncover the secret of different Python libraries, such as Pandas, Matplotlib, SciPy and many more</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Explore machine learning concepts and algorithms</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Discover developing custom data solutions</span></li></ul><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(38, 99, 82) !important;">You can create various applications for science, math, machine learning, and data science with Python. As a high-level, general-purpose programming language, Python is quite English-like and easy to learn. This&nbsp;<b fr-original-style="" style="box-sizing: border-box;">Python for Data Science and Machine Learning</b>&nbsp;course will show you how to use Python for machine learning and data science.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">During the course, you will master every useful Python skill you need for data science and machine learning. If you find it challenging to write complex Python programs, we&rsquo;ll show you how to do so with our step-by-step instructions based on actual work contexts. You&rsquo;ll also learn how to run the regression, classification, and clustering and the specifics of supervised and unsupervised machine learning. Moreover, this course covers how statistics work for data science, hypothesis testing, and much more.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">An expert instructor designed this&nbsp;<b fr-original-style="" style="box-sizing: border-box;">&ldquo;Python for Data Science and Machine Learning&rdquo;</b>&nbsp;course to provide you with the best learning experience possible. Our goal is for you to learn and apply the lessons from this course to improve your skills.&nbsp;</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">To become a master of Python, Enrol Now!</p><h3 style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Accredited by</span></h3><p style="color: rgb(15, 21, 26); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);">This course is <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CPDQE&nbsp;</strong>accredited, which serves as an impactful mechanism for skill enhancement.&nbsp;</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">Continuing Professional Development (CPD) stands as a crucial, widely acknowledged method that aids individuals, organisations, and entire industries in staying current with their skills and knowledge.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">CPD not only facilitates the elevation of global standards and benchmarks, aligning with the growing forces of globalisation and consumer expectations but also transforms learning into a deliberate and proactive process. It motivates professionals to uphold a consistently high level of performance and demonstrates their unwavering commitment to a specific job role or profession.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><br fr-original-style="" style="box-sizing: border-box;"></p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Course Curriculum</span></strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Python for Data Science &amp; Machine Learning from A-Z</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Science &amp; Machine Learning Concepts</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python For Data Science</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Statistics for Data Science</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Probability and Hypothesis Testing</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">NumPy Data Analysis</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Pandas Data Analysis</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Data Visualization</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Machine Learning</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Loading &amp; Exploration</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Cleaning</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Feature Selecting and Engineering</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Linear and Logistic Regression</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">K Nearest Neighbours</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Decision Trees</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Ensemble Learning and Random Forests</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Support Vector Machines</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">K-Means</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">PCA</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Science Career</strong></li></ul>

This course at a glance
Accredited by CPDQE
Learn how to clean, process, wrangle, and manipulate data
Know how to create a resume for a data science job
Get a grasp of NumPy by learning how to use it for numerical data
Uncover the secret of different Python libraries, such as Pandas, Matplotlib, SciPy and many more
Explore machine learning concepts and algorithms
Discover developing custom data solutions
You can create various applications for science, math, machine learning, and data science with Python. As a high-level, general-purpose programming language, Python is quite English-like and easy to learn. This Python for Data Science and Machine Learning course will show you how to use Python for machine learning and data science.
During the course, you will master every useful Python skill you need for data science and machine learning. If you find it challenging to write complex Python programs, we’ll show you how to do so with our step-by-step instructions based on actual work contexts. You’ll also learn how to run the regression, classification, and clustering and the specifics of supervised and unsupervised machine learning. Moreover, this course covers how statistics work for data science, hypothesis testing, and much more.
An expert instructor designed this “Python for Data Science and Machine Learning” course to provide you with the best learning experience possible. Our goal is for you to learn and apply the lessons from this course to improve your skills. 
To become a master of Python, Enrol Now!
Accredited by
This course is CPDQE accredited, which serves as an impactful mechanism for skill enhancement. 
Continuing Professional Development (CPD) stands as a crucial, widely acknowledged method that aids individuals, organisations, and entire industries in staying current with their skills and knowledge.
CPD not only facilitates the elevation of global standards and benchmarks, aligning with the growing forces of globalisation and consumer expectations but also transforms learning into a deliberate and proactive process. It motivates professionals to uphold a consistently high level of performance and demonstrates their unwavering commitment to a specific job role or profession.


Course Curriculum
Introduction to Python for Data Science & Machine Learning from A-Z
Data Science & Machine Learning Concepts
Python For Data Science
Statistics for Data Science
Probability and Hypothesis Testing
NumPy Data Analysis
Pandas Data Analysis
Python Data Visualization
Introduction to Machine Learning
Data Loading & Exploration
Data Cleaning
Feature Selecting and Engineering
Linear and Logistic Regression
K Nearest Neighbours
Decision Trees
Ensemble Learning and Random Forests
Support Vector Machines
K-Means
PCA
Data Science Career

LearnDrive UK

This course aims to teach you how to use Python for machine learning and data science.



Python for Data Science and Machine Learning

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p></p><li>Network Security Operations</li><br /><li>Workload Application Administrators</li><br /><li>Security Operations</li><br /><li>Field Engineers</li><br /><li>Network Engineers</li><br /><li>Systems Engineers</li><br /><li>Technical Solutions Architects</li><br /><li>Cisco Integrators and Partners</li><p></p>Overview<p>After taking this course, you should be able to:<br /></p><li>Define the Cisco telemetry and analytics approach.</li><br /><li>Explore common scenarios that Cisco Tetration Analytics can solve.</li><br /><li>Describe how the Cisco Tetration Analytics platform collects telemetry and other context information.</li><br /><li>Discuss how relative agents are installed and configured.</li><br /><li>Explore the operational aspects of the Cisco Tetration Analytics platform.</li><br /><li>Describe the Cisco Tetration Analytics support for application visibility or application insight based on the Application Dependency Mapping (ADM) feature.</li><br /><li>List the concepts of the intent-based declarative network management automation model.</li><br /><li>Describe the Cisco Tetration policy enforcement pipeline, components, functions, and implementation of application policy.</li><br /><li>Describe how to use Cisco Tetration Analytics for workload protection in order to provide a secure infrastructure for business-critical applications and data.</li><br /><li>Describe Cisco Tetration Analytics platform use cases in the modern heterogeneous, multicloud data center.</li><br /><li>List the options for the Cisco Tetration Analytics platform enhancements.</li><br /><li>Explain how to perform the Cisco Tetration Analytics administration.</li><p></p><p>This course teaches how to deploy, use, and operate CiscoÂ© Tetration Analytics? platform for comprehensive workload-protection and application and network insights across a multicloud infrastructure. You will learn how the Cisco Tetration Analytics platform uses streaming telemetry, behavioral analysis, unsupervised machine learning, analytical intelligence, and big data analytics to deliver pervasive visibility, automated intent-based policy, workload protection, and performance management.</p></div><div id="course-pills-2"><h4>Exploring Cisco Tetration</h4><ul><li>Data Center Challenges</li><li>Define and Position Cisco Tetration</li><li>Cisco Tetration Features</li><li>Cisco Tetration Architecture</li><li>Cisco Tetration Deployment Models</li><li>Cisco Tetration GUI Overview</li></ul><h4>Implementing and Operating Cisco Tetration</h4><ul><li>Explore Data Collection</li><li>Install the Software Agent</li><li>Install the Hardware Agent</li><li>Import Context Data</li><li>Describe Cisco Tetration Operational Concepts</li></ul><h4>Examining Cisco Tetration ADM and Application Insight</h4><ul><li>Describe Cisco Tetration Application Insight</li><li>Perform ADM</li><li>Interpret ADM Results Application Visibility</li></ul><h4>Examining Cisco Tetration Intent-Based Networking</h4><ul><li>Describe Intent-Based Policy</li><li>Examine Policy Features</li><li>Implement Policies</li></ul><h4>Enforcing Tetration Policy Pipeline and Compliance</h4><ul><li>Examine Policy Enforcement</li><li>Implement Application Policy</li><li>Examine Policy Compliance Verification and Simulation</li></ul><h4>Examining Tetration Security Use Cases</h4><ul><li>Examine Workload Security</li><li>Attack Prevention</li><li>Attack Detection</li><li>Attack Remediation</li></ul><h4>Examining IT Operations Use Cases</h4><ul><li>Key Features and IT Operations Use Cases</li><li>Performing Operations in Neighborhood App-based Use Cases</li></ul><h4>Examining Platform Enhancement Use Cases</h4><ul><li>Integrations and Advanced Features</li><li>Third-party Integration Examples</li><li>Explore Data Platform Capabilities</li></ul><h4>Exploring Cisco Tetration Analytics Administration</h4><ul><li>Examine User Authentication and Authorization</li><li>Examine Cluster Management</li><li>Configure Alerts and Syslog</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Cisco Implementing Cisco Tetration Analytics v1.0 (DCITET) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the Cisco Implementing Cisco Tetration Analytics v1.0 (DCITET) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Duration
3 Days
18 CPD hours
This course is intended for

Network Security Operations
Workload Application Administrators
Security Operations
Field Engineers
Network Engineers
Systems Engineers
Technical Solutions Architects
Cisco Integrators and Partners

Overview
After taking this course, you should be able to:

Define the Cisco telemetry and analytics approach.
Explore common scenarios that Cisco Tetration Analytics can solve.
Describe how the Cisco Tetration Analytics platform collects telemetry and other context information.
Discuss how relative agents are installed and configured.
Explore the operational aspects of the Cisco Tetration Analytics platform.
Describe the Cisco Tetration Analytics support for application visibility or application insight based on the Application Dependency Mapping (ADM) feature.
List the concepts of the intent-based declarative network management automation model.
Describe the Cisco Tetration policy enforcement pipeline, components, functions, and implementation of application policy.
Describe how to use Cisco Tetration Analytics for workload protection in order to provide a secure infrastructure for business-critical applications and data.
Describe Cisco Tetration Analytics platform use cases in the modern heterogeneous, multicloud data center.
List the options for the Cisco Tetration Analytics platform enhancements.
Explain how to perform the Cisco Tetration Analytics administration.

This course teaches how to deploy, use, and operate CiscoÂ© Tetration Analytics? platform for comprehensive workload-protection and application and network insights across a multicloud infrastructure. You will learn how the Cisco Tetration Analytics platform uses streaming telemetry, behavioral analysis, unsupervised machine learning, analytical intelligence, and big data analytics to deliver pervasive visibility, automated intent-based policy, workload protection, and performance management.
Exploring Cisco Tetration
Data Center Challenges
Define and Position Cisco Tetration
Cisco Tetration Features
Cisco Tetration Architecture
Cisco Tetration Deployment Models
Cisco Tetration GUI Overview
Implementing and Operating Cisco Tetration
Explore Data Collection
Install the Software Agent
Install the Hardware Agent
Import Context Data
Describe Cisco Tetration Operational Concepts
Examining Cisco Tetration ADM and Application Insight
Describe Cisco Tetration Application Insight
Perform ADM
Interpret ADM Results Application Visibility
Examining Cisco Tetration Intent-Based Networking
Describe Intent-Based Policy
Examine Policy Features
Implement Policies
Enforcing Tetration Policy Pipeline and Compliance
Examine Policy Enforcement
Implement Application Policy
Examine Policy Compliance Verification and Simulation
Examining Tetration Security Use Cases
Examine Workload Security
Attack Prevention
Attack Detection
Attack Remediation
Examining IT Operations Use Cases
Key Features and IT Operations Use Cases
Performing Operations in Neighborhood App-based Use Cases
Examining Platform Enhancement Use Cases
Integrations and Advanced Features
Third-party Integration Examples
Explore Data Platform Capabilities
Exploring Cisco Tetration Analytics Administration
Examine User Authentication and Authorization
Examine Cluster Management
Configure Alerts and Syslog
Additional course details:
Nexus Humans Cisco Implementing Cisco Tetration Analytics v1.0 (DCITET) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. 

This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts.

Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.

While we feel this is the best course for the Cisco Implementing Cisco Tetration Analytics v1.0 (DCITET) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.

Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.


Cisco Certified Network Associate (CCNA)

Certified Technology Specialist (CTS)

CompTIA Network+

IT Certifications

Cisco Certified Entry Networking Technician (CCENT)

Cisco Certified Internetwork Expert (CCIE)

Cisco Implementing Cisco Tetration Analytics v1.0 (DCITET)

<div id="course-pills-1">Duration<p>2 Days</p><p>12 CPD hours</p>This course is intended for<p>Business Analysts, Technical Managers, and Programmers</p>Overview<p>This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice.</p><p>Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning.</p></div><div id="course-pills-2"><h4>What is R</h4><ul><li>? What is R?</li><li>? Positioning of R in the Data Science Space</li><li>? The Legal Aspects</li><li>? Microsoft R Open</li><li>? R Integrated Development Environments</li><li>? Running R</li><li>? Running RStudio</li><li>? Getting Help</li><li>? General Notes on R Commands and Statements</li><li>? Assignment Operators</li><li>? R Core Data Structures</li><li>? Assignment Example</li><li>? R Objects and Workspace</li><li>? Printing Objects</li><li>? Arithmetic Operators</li><li>? Logical Operators</li><li>? System Date and Time</li><li>? Operations</li><li>? User-defined Functions</li><li>? Control Statements</li><li>? Conditional Execution</li><li>? Repetitive Execution</li><li>? Repetitive execution</li><li>? Built-in Functions</li><li>? Summary</li></ul><h4>Introduction to Functional Programming with R</h4><ul><li>? What is Functional Programming (FP)?</li><li>? Terminology: Higher-Order Functions</li><li>? A Short List of Languages that Support FP</li><li>? Functional Programming in R</li><li>? Vector and Matrix Arithmetic</li><li>? Vector Arithmetic Example</li><li>? More Examples of FP in R</li><li>? Summary</li></ul><h4>Managing Your Environment</h4><ul><li>? Getting and Setting the Working Directory</li><li>? Getting the List of Files in a Directory</li><li>? The R Home Directory</li><li>? Executing External R commands</li><li>? Loading External Scripts in RStudio</li><li>? Listing Objects in Workspace</li><li>? Removing Objects in Workspace</li><li>? Saving Your Workspace in R</li><li>? Saving Your Workspace in RStudio</li><li>? Saving Your Workspace in R GUI</li><li>? Loading Your Workspace</li><li>? Diverting Output to a File</li><li>? Batch (Unattended) Processing</li><li>? Controlling Global Options</li><li>? Summary</li></ul><h4>R Type System and Structures</h4><ul><li>? The R Data Types</li><li>? System Date and Time</li><li>? Formatting Date and Time</li><li>? Using the mode() Function</li><li>? R Data Structures</li><li>? What is the Type of My Data Structure?</li><li>? Creating Vectors</li><li>? Logical Vectors</li><li>? Character Vectors</li><li>? Factorization</li><li>? Multi-Mode Vectors</li><li>? The Length of the Vector</li><li>? Getting Vector Elements</li><li>? Lists</li><li>? A List with Element Names</li><li>? Extracting List Elements</li><li>? Adding to a List</li><li>? Matrix Data Structure</li><li>? Creating Matrices</li><li>? Creating Matrices with cbind() and rbind()</li><li>? Working with Data Frames</li><li>? Matrices vs Data Frames</li><li>? A Data Frame Sample</li><li>? Creating a Data Frame</li><li>? Accessing Data Cells</li><li>? Getting Info About a Data Frame</li><li>? Selecting Columns in Data Frames</li><li>? Selecting Rows in Data Frames</li><li>? Getting a Subset of a Data Frame</li><li>? Sorting (ordering) Data in Data Frames by Attribute(s)</li><li>? Editing Data Frames</li><li>? The str() Function</li><li>? Type Conversion (Coercion)</li><li>? The summary() Function</li><li>? Checking an Object's Type</li><li>? Summary</li></ul><h4>Extending R</h4><ul><li>? The Base R Packages</li><li>? Loading Packages</li><li>? What is the Difference between Package and Library?</li><li>? Extending R</li><li>? The CRAN Web Site</li><li>? Extending R in R GUI</li><li>? Extending R in RStudio</li><li>? Installing and Removing Packages from Command-Line</li><li>? Summary</li></ul><h4>Read-Write and Import-Export Operations in R</h4><ul><li>? Reading Data from a File into a Vector</li><li>? Example of Reading Data from a File into A Vector</li><li>? Writing Data to a File</li><li>? Example of Writing Data to a File</li><li>? Reading Data into A Data Frame</li><li>? Writing CSV Files</li><li>? Importing Data into R</li><li>? Exporting Data from R</li><li>? Summary</li></ul><h4>Statistical Computing Features in R</h4><ul><li>? Statistical Computing Features</li><li>? Descriptive Statistics</li><li>? Basic Statistical Functions</li><li>? Examples of Using Basic Statistical Functions</li><li>? Non-uniformity of a Probability Distribution</li><li>? Writing Your Own skew and kurtosis Functions</li><li>? Generating Normally Distributed Random Numbers</li><li>? Generating Uniformly Distributed Random Numbers</li><li>? Using the summary() Function</li><li>? Math Functions Used in Data Analysis</li><li>? Examples of Using Math Functions</li><li>? Correlations</li><li>? Correlation Example</li><li>? Testing Correlation Coefficient for Significance</li><li>? The cor.test() Function</li><li>? The cor.test() Example</li><li>? Regression Analysis</li><li>? Types of Regression</li><li>? Simple Linear Regression Model</li><li>? Least-Squares Method (LSM)</li><li>? LSM Assumptions</li><li>? Fitting Linear Regression Models in R</li><li>? Example of Using lm()</li><li>? Confidence Intervals for Model Parameters</li><li>? Example of Using lm() with a Data Frame</li><li>? Regression Models in Excel</li><li>? Multiple Regression Analysis</li><li>? Summary</li></ul><h4>Data Manipulation and Transformation in R</h4><ul><li>? Applying Functions to Matrices and Data Frames</li><li>? The apply() Function</li><li>? Using apply()</li><li>? Using apply() with a User-Defined Function</li><li>? apply() Variants</li><li>? Using tapply()</li><li>? Adding a Column to a Data Frame</li><li>? Dropping A Column in a Data Frame</li><li>? The attach() and detach() Functions</li><li>? Sampling</li><li>? Using sample() for Generating Labels</li><li>? Set Operations</li><li>? Example of Using Set Operations</li><li>? The dplyr Package</li><li>? Object Masking (Shadowing) Considerations</li><li>? Getting More Information on dplyr in RStudio</li><li>? The search() or searchpaths() Functions</li><li>? Handling Large Data Sets in R with the data.table Package</li><li>? The fread() and fwrite() functions from the data.table Package</li><li>? Using the Data Table Structure</li><li>? Summary</li></ul><h4>Data Visualization in R</h4><ul><li>? Data Visualization</li><li>? Data Visualization in R</li><li>? The ggplot2 Data Visualization Package</li><li>? Creating Bar Plots in R</li><li>? Creating Horizontal Bar Plots</li><li>? Using barplot() with Matrices</li><li>? Using barplot() with Matrices Example</li><li>? Customizing Plots</li><li>? Histograms in R</li><li>? Building Histograms with hist()</li><li>? Example of using hist()</li><li>? Pie Charts in R</li><li>? Examples of using pie()</li><li>? Generic X-Y Plotting</li><li>? Examples of the plot() function</li><li>? Dot Plots in R</li><li>? Saving Your Work</li><li>? Supported Export Options</li><li>? Plots in RStudio</li><li>? Saving a Plot as an Image</li><li>? Summary</li></ul><h4>Using R Efficiently</h4><ul><li>? Object Memory Allocation Considerations</li><li>? Garbage Collection</li><li>? Finding Out About Loaded Packages</li><li>? Using the conflicts() Function</li><li>? Getting Information About the Object Source Package with the pryr Package</li><li>? Using the where() Function from the pryr Package</li><li>? Timing Your Code</li><li>? Timing Your Code with system.time()</li><li>? Timing Your Code with System.time()</li><li>? Sleeping a Program</li><li>? Handling Large Data Sets in R with the data.table Package</li><li>? Passing System-Level Parameters to R</li><li>? Summary</li></ul><h4>Lab Exercises</h4><ul><li>Lab 1 -  Getting Started with R      </li><li>Lab 2 -  Learning the R Type System and Structures             </li><li>Lab 3 -  Read and Write Operations in R    </li><li>Lab 4 -  Data Import and Export in R           </li><li>Lab 5 -  k-Nearest Neighbors Algorithm    </li><li>Lab 6 - Creating Your Own Statistical Functions     </li><li>Lab 7 - Simple Linear Regression   </li><li>Lab 8 - Monte-Carlo Simulation (Method)               </li><li>Lab 9 -  Data Processing with R     </li><li>Lab 10 - Using R Graphics Package              </li><li>Lab 11 -  Using R Efficiently</li></ul></div>

Duration
2 Days
12 CPD hours
This course is intended for
Business Analysts, Technical Managers, and Programmers
Overview
This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice.
Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning.
What is R
? What is R?
? Positioning of R in the Data Science Space
? The Legal Aspects
? Microsoft R Open
? R Integrated Development Environments
? Running R
? Running RStudio
? Getting Help
? General Notes on R Commands and Statements
? Assignment Operators
? R Core Data Structures
? Assignment Example
? R Objects and Workspace
? Printing Objects
? Arithmetic Operators
? Logical Operators
? System Date and Time
? Operations
? User-defined Functions
? Control Statements
? Conditional Execution
? Repetitive Execution
? Repetitive execution
? Built-in Functions
? Summary
Introduction to Functional Programming with R
? What is Functional Programming (FP)?
? Terminology: Higher-Order Functions
? A Short List of Languages that Support FP
? Functional Programming in R
? Vector and Matrix Arithmetic
? Vector Arithmetic Example
? More Examples of FP in R
? Summary
Managing Your Environment
? Getting and Setting the Working Directory
? Getting the List of Files in a Directory
? The R Home Directory
? Executing External R commands
? Loading External Scripts in RStudio
? Listing Objects in Workspace
? Removing Objects in Workspace
? Saving Your Workspace in R
? Saving Your Workspace in RStudio
? Saving Your Workspace in R GUI
? Loading Your Workspace
? Diverting Output to a File
? Batch (Unattended) Processing
? Controlling Global Options
? Summary
R Type System and Structures
? The R Data Types
? System Date and Time
? Formatting Date and Time
? Using the mode() Function
? R Data Structures
? What is the Type of My Data Structure?
? Creating Vectors
? Logical Vectors
? Character Vectors
? Factorization
? Multi-Mode Vectors
? The Length of the Vector
? Getting Vector Elements
? Lists
? A List with Element Names
? Extracting List Elements
? Adding to a List
? Matrix Data Structure
? Creating Matrices
? Creating Matrices with cbind() and rbind()
? Working with Data Frames
? Matrices vs Data Frames
? A Data Frame Sample
? Creating a Data Frame
? Accessing Data Cells
? Getting Info About a Data Frame
? Selecting Columns in Data Frames
? Selecting Rows in Data Frames
? Getting a Subset of a Data Frame
? Sorting (ordering) Data in Data Frames by Attribute(s)
? Editing Data Frames
? The str() Function
? Type Conversion (Coercion)
? The summary() Function
? Checking an Object's Type
? Summary
Extending R
? The Base R Packages
? Loading Packages
? What is the Difference between Package and Library?
? Extending R
? The CRAN Web Site
? Extending R in R GUI
? Extending R in RStudio
? Installing and Removing Packages from Command-Line
? Summary
Read-Write and Import-Export Operations in R
? Reading Data from a File into a Vector
? Example of Reading Data from a File into A Vector
? Writing Data to a File
? Example of Writing Data to a File
? Reading Data into A Data Frame
? Writing CSV Files
? Importing Data into R
? Exporting Data from R
? Summary
Statistical Computing Features in R
? Statistical Computing Features
? Descriptive Statistics
? Basic Statistical Functions
? Examples of Using Basic Statistical Functions
? Non-uniformity of a Probability Distribution
? Writing Your Own skew and kurtosis Functions
? Generating Normally Distributed Random Numbers
? Generating Uniformly Distributed Random Numbers
? Using the summary() Function
? Math Functions Used in Data Analysis
? Examples of Using Math Functions
? Correlations
? Correlation Example
? Testing Correlation Coefficient for Significance
? The cor.test() Function
? The cor.test() Example
? Regression Analysis
? Types of Regression
? Simple Linear Regression Model
? Least-Squares Method (LSM)
? LSM Assumptions
? Fitting Linear Regression Models in R
? Example of Using lm()
? Confidence Intervals for Model Parameters
? Example of Using lm() with a Data Frame
? Regression Models in Excel
? Multiple Regression Analysis
? Summary
Data Manipulation and Transformation in R
? Applying Functions to Matrices and Data Frames
? The apply() Function
? Using apply()
? Using apply() with a User-Defined Function
? apply() Variants
? Using tapply()
? Adding a Column to a Data Frame
? Dropping A Column in a Data Frame
? The attach() and detach() Functions
? Sampling
? Using sample() for Generating Labels
? Set Operations
? Example of Using Set Operations
? The dplyr Package
? Object Masking (Shadowing) Considerations
? Getting More Information on dplyr in RStudio
? The search() or searchpaths() Functions
? Handling Large Data Sets in R with the data.table Package
? The fread() and fwrite() functions from the data.table Package
? Using the Data Table Structure
? Summary
Data Visualization in R
? Data Visualization
? Data Visualization in R
? The ggplot2 Data Visualization Package
? Creating Bar Plots in R
? Creating Horizontal Bar Plots
? Using barplot() with Matrices
? Using barplot() with Matrices Example
? Customizing Plots
? Histograms in R
? Building Histograms with hist()
? Example of using hist()
? Pie Charts in R
? Examples of using pie()
? Generic X-Y Plotting
? Examples of the plot() function
? Dot Plots in R
? Saving Your Work
? Supported Export Options
? Plots in RStudio
? Saving a Plot as an Image
? Summary
Using R Efficiently
? Object Memory Allocation Considerations
? Garbage Collection
? Finding Out About Loaded Packages
? Using the conflicts() Function
? Getting Information About the Object Source Package with the pryr Package
? Using the where() Function from the pryr Package
? Timing Your Code
? Timing Your Code with system.time()
? Timing Your Code with System.time()
? Sleeping a Program
? Handling Large Data Sets in R with the data.table Package
? Passing System-Level Parameters to R
? Summary
Lab Exercises
Lab 1 - Getting Started with R
Lab 2 - Learning the R Type System and Structures
Lab 3 - Read and Write Operations in R
Lab 4 - Data Import and Export in R
Lab 5 - k-Nearest Neighbors Algorithm
Lab 6 - Creating Your Own Statistical Functions
Lab 7 - Simple Linear Regression
Lab 8 - Monte-Carlo Simulation (Method)
Lab 9 - Data Processing with R
Lab 10 - Using R Graphics Package
Lab 11 - Using R Efficiently

Computing Skills

Programming Fundamentals

R (programming language)

Data Analysis Expressions

Statistical Computing

Introduction to R Programming

<strong>A step-by-step guide that walks you through the fundamentals of Python programming followed using Python libraries to create random forest from scratch. A comprehensive course designed for both beginners with some programming experience or even those who know nothing about ML and random forest!</strong>
        <p>Machine learning is designed to understand and build methods that 'learn' to leverage data to improve performance on a set of tasks. Machine learning algorithms are used in a plethora of applications in medicine, email filtering, speech recognition, and more, where it is challenging to develop conventional algorithms to perform tasks.

The course begins with an introduction to machine learning concepts and explains the motivation for machine learning. The course teaches all major concepts about Python including variables, objects, strings, loops, decision-making statements, classes, and a small project to recap. You will learn to use the power of Python to train your machine and make predictions and implement the ML algorithm 'Random Forest.' Use NumPy with Python for array handling, Pandas data frames for Excel files, and matplotlib for data visualization. You will learn to use Random Forest with sklearn, Matplotlib for Python plotting, and SciKit-Learn for Random Forest.

Upon completion, you will Implement the structure of forest, impurity, information gain, partitions, leaf nodes, and decision nodes using Python and create a complete structure for Random Forest using Python to build one tree that lets you create an entire forest. You will write an accuracy calculator function and implement Random Forest on any dataset.

All resources are available at: https://github.com/PacktPublishing/Machine-Learning-Random-Forest-with-Python-from-Scratch-</p>
        <h2>What You Will Learn</h2><p>Use Random Forest with sklearn and Matplotlib for Python plotting<br>Use SciKit-Learn for Random Forest using the titanic dataset<br>Learn forest structure, impurity, partition, leaf/decision nodes<br>Create a complete Random Forest structure from scratch using Python<br>Build one tree that adds up to create a complete forest<br>Write accuracy calculator functions and implement them on any dataset</p>
        <h2>Audience</h2><p>This course is for you if you want to learn how to program in Python for machine learning or want to make a predictive analysis model.<br><br>This course is for someone who is an absolute beginner and has truly little or even zero ideas of machine learning or wants to learn random forest from zero to hero.</p>
        <h2>Approach</h2><p>This course delivers a step-by-step tutorial with carefully structured video lectures. It builds on what has already been explained and moves one step forward. The course assigns a small task to be solved at the beginning of each lecture, thus keeping you continuously abreast.</p>
        <h2>Key Features</h2><p>Use the power of Python to train your machine to learn like a human and make predictions! * Learn data preprocessing steps to prepare data for machine learning algorithms * Master machine learning concepts and implement the essential ML algorithm, Random Forest</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-Random-Forest-with-Python-from-Scratch-</p>
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.

Their courses have successfully helped more than 100,000 students master AI and data science.</p>
        <h2>Course Outline</h2><h4>1. Introduction to the Course</h4><p>This section briefly outlines the Random Forest machine learning algorithm. The section also details what you will learn in this course and the benefits of enrolling in this course.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction and Instructor</p><p style='margin: 0'>In this video, you will learn about the course content in general, the features and benefits of this course, as well as a brief introduction to the instructor.</p></td></tr><tr><td style='padding: 15px;'><p>2. Motivation for the Course</p><p style='margin: 0'>This lecture provides an overview of the benefits of enrolling in this course.</p></td></tr><tr><td style='padding: 15px;'><p>3. Past, Present, and Future of Machine Learning</p><p style='margin: 0'>This lecture illustrates why machine learning has started evolving recently and the future that beholds machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>4. Course Overview</p><p style='margin: 0'>This video will introduce you to Python, machine learning, and Random Forest and discuss the live implementations, quizzes, and projects.</p></td></tr></tbody></table><br><h4>2. Introduction to Python</h4><p>This section focuses on the Python programming language. This section will delve deep into datatypes, numbers, strings, lists, sets, dictionaries, and operators used in Python. You will also learn about loops and functions and work on a calculator project.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Hello World</p><p style='margin: 0'>In this video, we will understand the importance of Python for machine learning, use an IDE (jupyter), and create a Hello World program.</p></td></tr><tr><td style='padding: 15px;'><p>2. Introduction to Data Types</p><p style='margin: 0'>This video introduces us to Python's six data types: numbers, strings, lists, dictionaries, tuples, and sets.</p></td></tr><tr><td style='padding: 15px;'><p>3. Numbers</p><p style='margin: 0'>This lesson will teach us about numbers, the first standard datatype in Python, used for arithmetic operations and storing information.</p></td></tr><tr><td style='padding: 15px;'><p>4. Strings</p><p style='margin: 0'>Here, we will look at the second datatype, strings, which are characters, used to represent text instead of numbers.</p></td></tr><tr><td style='padding: 15px;'><p>5. Tuples</p><p style='margin: 0'>In this topic on tuples, you will learn how to get a starting index of a substring and write a simple program to find the starting index of the word WORLD.</p></td></tr><tr><td style='padding: 15px;'><p>6. Lists</p><p style='margin: 0'>We shall look at the following datatype: lists, mutable (changeable) after creation.</p></td></tr><tr><td style='padding: 15px;'><p>7. Sets</p><p style='margin: 0'>Our next datatype is a set, an unordered collection of iterable and mutable data.</p></td></tr><tr><td style='padding: 15px;'><p>8. Dictionaries</p><p style='margin: 0'>Let's look at the final datatype, dictionaries, indexed, changeable, and unordered data collection.</p></td></tr><tr><td style='padding: 15px;'><p>9. Comparison Operators</p><p style='margin: 0'>Here, you will learn about Python's two types of operators, the comparison operator, and the logical operator.</p></td></tr><tr><td style='padding: 15px;'><p>10. Logical Operators, User Input, Game</p><p style='margin: 0'>This video will teach us about the three types of logical operators: AND, OR, and Not(!).</p></td></tr><tr><td style='padding: 15px;'><p>11. Decision Making (if, else, elif)</p><p style='margin: 0'>Let's understand the fundamental element of any programming language: decision-making. We will look at the if, else, and elif statements.</p></td></tr><tr><td style='padding: 15px;'><p>12. Decision Making (nested if)</p><p style='margin: 0'>Continuing with the decision-making lesson, we will understand what a nested if statement is.</p></td></tr><tr><td style='padding: 15px;'><p>13. Better Coding Practice, Completing the Game</p><p style='margin: 0'>In this lecture, we will complete the game we left in the previous lesson.</p></td></tr><tr><td style='padding: 15px;'><p>14. For Loop</p><p style='margin: 0'>Let's understand the for loop and how it is used to iterate a sequence, which is a list, tuple, string, dictionary, or set.</p></td></tr><tr><td style='padding: 15px;'><p>15. While Loop</p><p style='margin: 0'>Let's understand the while loop, a conditional loop that runs until a given condition is fulfilled.</p></td></tr><tr><td style='padding: 15px;'><p>16. Simple Functions</p><p style='margin: 0'>Here, we will understand what a function is, how to pass data or parameters into a function, and understand the different types of functions.</p></td></tr><tr><td style='padding: 15px;'><p>17. Boolean and Value Returning Function</p><p style='margin: 0'>In this video, we will discuss a function that returns values as true or false, also called the Boolean function.</p></td></tr><tr><td style='padding: 15px;'><p>18. Calculator Project</p><p style='margin: 0'>This video demonstrates how to create our calculator using addition and subtraction functions.</p></td></tr></tbody></table><br><h4>3. Introduction to Machine Learning</h4><p>This section delves deep into the core concepts of machine learning. You will learn how to train our machine, create labels and features, learn about the various data formats, and understand classification, regression, and clustering.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Let's Introduce Machine Learning</p><p style='margin: 0'>This brief video outlines machine learning, its importance, and how it can be used in various applications to make life easier.</p></td></tr><tr><td style='padding: 15px;'><p>2. Kids versus Computer Learning</p><p style='margin: 0'>In this lesson, we will compare their learning features to understand the fundamental difference between a kid and a computer.</p></td></tr><tr><td style='padding: 15px;'><p>3. Dataset</p><p style='margin: 0'>In this lesson, we will dive into the hardcore process of machine learning and the fundamental elements used in machine learning, like datasets, training and testing data, outliers, models, and so on.</p></td></tr><tr><td style='padding: 15px;'><p>4. Labels and Features</p><p style='margin: 0'>In this lesson, we will look at a target or label as output data and feature, a measurable property or characteristic of an element.</p></td></tr><tr><td style='padding: 15px;'><p>5. Outliers</p><p style='margin: 0'>Let's look at outliers, the data points of a dataset that differ from others and are usually excluded by visualizing a dataset.</p></td></tr><tr><td style='padding: 15px;'><p>6. Model and Training</p><p style='margin: 0'>Here, we will look at a machine learning model, an arithmetic expression or equation that fits and learns to predict data.</p></td></tr><tr><td style='padding: 15px;'><p>7. Overfitting and Underfitting</p><p style='margin: 0'>In this video, you will learn about overfitting, a modeling error when a model performs well in training but not in testing, and underfitting, where the model neither performs well during training nor during testing.</p></td></tr><tr><td style='padding: 15px;'><p>8. Accuracy and Error</p><p style='margin: 0'>This lecture explains accuracy and error in data when the predicted outcome is close to the expected result and when it is not defined as an error.</p></td></tr><tr><td style='padding: 15px;'><p>9. Formats of Data</p><p style='margin: 0'>You will learn about the different data formats in machine learning, including structured (labeled or unlabeled) and unstructured data, and how to choose the best format.</p></td></tr><tr><td style='padding: 15px;'><p>10. Types of Learning</p><p style='margin: 0'>Here, we will understand the learning types, including supervised and unsupervised machine learning algorithms.</p></td></tr><tr><td style='padding: 15px;'><p>11. Classification versus Regression</p><p style='margin: 0'>Let's learn about the three modes of machine learning: classification, regression, and clustering.</p></td></tr><tr><td style='padding: 15px;'><p>12. Clustering</p><p style='margin: 0'>In this lesson, you will learn about an unsupervised branch of learning called clustering, which involves grouping elements with no labels to classify them.</p></td></tr><tr><td style='padding: 15px;'><p>13. Recap, Flow of Machine Learning Project</p><p style='margin: 0'>This video is a quick recap of what we have learned thus far, and we will also be working on a project called the Flow of Machine Learning.</p></td></tr></tbody></table><br><h4>4. Random Forest Step-by-Step</h4><p>This section focuses on the Random Forest concepts that deal with reading and manipulating datasets; you will learn about the pros and cons of Random Forest and use pandas for the Random Forest.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction and Motivation</p><p style='margin: 0'>This video outlines the concepts of Random Forest when we can use the Random Forest, and its benefits and features.</p></td></tr><tr><td style='padding: 15px;'><p>2. How Decision Trees and Random Forest Work</p><p style='margin: 0'>We will understand what a decision tree is and create a decision tree and get a prediction result from the decision tree.</p></td></tr><tr><td style='padding: 15px;'><p>3. Pros and Cons of Random Forest</p><p style='margin: 0'>In this video, we will look at the benefits and limitations of Random Forest and the complexities involved in decision-making using Random Forest.</p></td></tr><tr><td style='padding: 15px;'><p>4. Introduction to the Final Project</p><p style='margin: 0'>In this video, we will discuss our final project, which classifies the titanic dataset using Random Forest.</p></td></tr><tr><td style='padding: 15px;'><p>5. Using NumPy for Random Forest</p><p style='margin: 0'>In this lesson, you will learn about the NumPy tool available in the Python library and look at the advantages of using NumPy.</p></td></tr><tr><td style='padding: 15px;'><p>6. Using Pandas for Random Forest (1)</p><p style='margin: 0'>This video introduces us to Pandas data structures and analysis tools, which help make data easy to handle and intuitive.</p></td></tr><tr><td style='padding: 15px;'><p>7. Using Pandas for Random Forest (2)</p><p style='margin: 0'>This is a continuation of the previous lesson, and here we will look at conditionally selecting values from a dataset.</p></td></tr><tr><td style='padding: 15px;'><p>8. Reading and Manipulating Dataset</p><p style='margin: 0'>Previously, you learned how to read a dataset; now, we will look at manipulating the data and using a sample dataset in our code.</p></td></tr><tr><td style='padding: 15px;'><p>9. Using Matplotlib for Data Visualization (1)</p><p style='margin: 0'>This video demonstrates data visualization using the Matplotlib function and explains data cleaning by removing outliers and filling in missing values.</p></td></tr><tr><td style='padding: 15px;'><p>10. Using Matplotlib for Data Visualization (2)</p><p style='margin: 0'>In this continuation lecture, we will focus on completing the assignment that we started on in the last lesson.</p></td></tr><tr><td style='padding: 15px;'><p>11. Dealing with Missing Values</p><p style='margin: 0'>Let's look at the first step involved in the data cleaning process, which is filling or removing missing values from a dataset.</p></td></tr><tr><td style='padding: 15px;'><p>12. Outliers Removal</p><p style='margin: 0'>In the second part of the data cleaning process, we will look at an outlier in detail and learn how to correct or remove the outlier.</p></td></tr><tr><td style='padding: 15px;'><p>13. Categorical to Numeric Conversion</p><p style='margin: 0'>In a machine, it understands values in the form of numbers. You will learn how to convert non-numeric data to numeric without changing the feature of the value.</p></td></tr><tr><td style='padding: 15px;'><p>14. Quick Implementation of Random Forest Model</p><p style='margin: 0'>Let's quickly implement Random Forest using the sklearn Random Forest model to tune the model's performance according to the project.</p></td></tr><tr><td style='padding: 15px;'><p>15. Feature Importance</p><p style='margin: 0'>After understanding features in a previous lesson, we will look at finding the most critical features' probability.</p></td></tr><tr><td style='padding: 15px;'><p>16. Recursion</p><p style='margin: 0'>In this video, you will learn about a complete implementation of the Random Forest using only Pandas to read the data and look at the results of the sklearn function.</p></td></tr><tr><td style='padding: 15px;'><p>17. Structure</p><p style='margin: 0'>In this video, we will discuss the structures of a Random Forest, namely forest, tree, leaf node, and decision node.</p></td></tr><tr><td style='padding: 15px;'><p>18. Importing Data, Helper Functions</p><p style='margin: 0'>Before creating a decision tree, we will first learn to import our dataset using Pandas.</p></td></tr><tr><td style='padding: 15px;'><p>19. Question and Partition</p><p style='margin: 0'>In this video, you will learn to create two more helper functions, question, and partition, which define statements for querying and retrieving data.</p></td></tr><tr><td style='padding: 15px;'><p>20. Impurity</p><p style='margin: 0'>Like all dataset characteristics, we will look at the impurities in a dataset and how they should be minimum in a good dataset.</p></td></tr><tr><td style='padding: 15px;'><p>21. Information Gain</p><p style='margin: 0'>In this video, we will define columns for questioning and determine how much information can be gained by splitting a column.</p></td></tr><tr><td style='padding: 15px;'><p>22. Best Slip</p><p style='margin: 0'>Here, we will determine the best split at any decision node where information is maximum and split into two branches, true and false.</p></td></tr><tr><td style='padding: 15px;'><p>23. Leaf and Decision Node</p><p style='margin: 0'>In this lesson, you will learn to create two classes, a leaf node and a decision node, with a constructor.</p></td></tr><tr><td style='padding: 15px;'><p>24. How to Build a Tree</p><p style='margin: 0'>After creating the decision node and leaf node classes, we will build our tree to add the nodes.</p></td></tr><tr><td style='padding: 15px;'><p>25. How to Classify</p><p style='margin: 0'>Let's learn to write a classification method that will train our module and help us get predictions.</p></td></tr><tr><td style='padding: 15px;'><p>26. Accuracy and Error</p><p style='margin: 0'>In this video, you will learn to implement the accuracy method to help determine our model's performance.</p></td></tr></tbody></table><br><h4>5. Conclusion</h4><p>This section focuses on all the concepts learned so far and summarizes them succinctly.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Concluding remarks</p><p style='margin: 0'>In this video, we will look at the concluding remarks of the course and recap what we learned through the course, briefly.</p></td></tr></tbody></table>

A step-by-step guide that walks you through the fundamentals of Python programming followed using Python libraries to create random forest from scratch. A comprehensive course designed for both beginners with some programming experience or even those who know nothing about ML and random forest!
Machine learning is designed to understand and build methods that 'learn' to leverage data to improve performance on a set of tasks. Machine learning algorithms are used in a plethora of applications in medicine, email filtering, speech recognition, and more, where it is challenging to develop conventional algorithms to perform tasks. The course begins with an introduction to machine learning concepts and explains the motivation for machine learning. The course teaches all major concepts about Python including variables, objects, strings, loops, decision-making statements, classes, and a small project to recap. You will learn to use the power of Python to train your machine and make predictions and implement the ML algorithm 'Random Forest.' Use NumPy with Python for array handling, Pandas data frames for Excel files, and matplotlib for data visualization. You will learn to use Random Forest with sklearn, Matplotlib for Python plotting, and SciKit-Learn for Random Forest. Upon completion, you will Implement the structure of forest, impurity, information gain, partitions, leaf nodes, and decision nodes using Python and create a complete structure for Random Forest using Python to build one tree that lets you create an entire forest. You will write an accuracy calculator function and implement Random Forest on any dataset. All resources are available at: https://github.com/PacktPublishing/Machine-Learning-Random-Forest-with-Python-from-Scratch-
What You Will Learn
Use Random Forest with sklearn and Matplotlib for Python plotting
Use SciKit-Learn for Random Forest using the titanic dataset
Learn forest structure, impurity, partition, leaf/decision nodes
Create a complete Random Forest structure from scratch using Python
Build one tree that adds up to create a complete forest
Write accuracy calculator functions and implement them on any dataset
Audience
This course is for you if you want to learn how to program in Python for machine learning or want to make a predictive analysis model.

This course is for someone who is an absolute beginner and has truly little or even zero ideas of machine learning or wants to learn random forest from zero to hero.
Approach
This course delivers a step-by-step tutorial with carefully structured video lectures. It builds on what has already been explained and moves one step forward. The course assigns a small task to be solved at the beginning of each lecture, thus keeping you continuously abreast.
Key Features
Use the power of Python to train your machine to learn like a human and make predictions! * Learn data preprocessing steps to prepare data for machine learning algorithms * Master machine learning concepts and implement the essential ML algorithm, Random Forest
Github Repo
https://github.com/PacktPublishing/Machine-Learning-Random-Forest-with-Python-from-Scratch-
About the Author
AI Sciences
AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences. Their courses have successfully helped more than 100,000 students master AI and data science.
Course Outline
1. Introduction to the Course
This section briefly outlines the Random Forest machine learning algorithm. The section also details what you will learn in this course and the benefits of enrolling in this course.
1. Introduction and Instructor
In this video, you will learn about the course content in general, the features and benefits of this course, as well as a brief introduction to the instructor.
2. Motivation for the Course
This lecture provides an overview of the benefits of enrolling in this course.
3. Past, Present, and Future of Machine Learning
This lecture illustrates why machine learning has started evolving recently and the future that beholds machine learning.
4. Course Overview
This video will introduce you to Python, machine learning, and Random Forest and discuss the live implementations, quizzes, and projects.
2. Introduction to Python
This section focuses on the Python programming language. This section will delve deep into datatypes, numbers, strings, lists, sets, dictionaries, and operators used in Python. You will also learn about loops and functions and work on a calculator project.
1. Hello World
In this video, we will understand the importance of Python for machine learning, use an IDE (jupyter), and create a Hello World program.
2. Introduction to Data Types
This video introduces us to Python's six data types: numbers, strings, lists, dictionaries, tuples, and sets.
3. Numbers
This lesson will teach us about numbers, the first standard datatype in Python, used for arithmetic operations and storing information.
4. Strings
Here, we will look at the second datatype, strings, which are characters, used to represent text instead of numbers.
5. Tuples
In this topic on tuples, you will learn how to get a starting index of a substring and write a simple program to find the starting index of the word WORLD.
6. Lists
We shall look at the following datatype: lists, mutable (changeable) after creation.
7. Sets
Our next datatype is a set, an unordered collection of iterable and mutable data.
8. Dictionaries
Let's look at the final datatype, dictionaries, indexed, changeable, and unordered data collection.
9. Comparison Operators
Here, you will learn about Python's two types of operators, the comparison operator, and the logical operator.
10. Logical Operators, User Input, Game
This video will teach us about the three types of logical operators: AND, OR, and Not(!).
11. Decision Making (if, else, elif)
Let's understand the fundamental element of any programming language: decision-making. We will look at the if, else, and elif statements.
12. Decision Making (nested if)
Continuing with the decision-making lesson, we will understand what a nested if statement is.
13. Better Coding Practice, Completing the Game
In this lecture, we will complete the game we left in the previous lesson.
14. For Loop
Let's understand the for loop and how it is used to iterate a sequence, which is a list, tuple, string, dictionary, or set.
15. While Loop
Let's understand the while loop, a conditional loop that runs until a given condition is fulfilled.
16. Simple Functions
Here, we will understand what a function is, how to pass data or parameters into a function, and understand the different types of functions.
17. Boolean and Value Returning Function
In this video, we will discuss a function that returns values as true or false, also called the Boolean function.
18. Calculator Project
This video demonstrates how to create our calculator using addition and subtraction functions.
3. Introduction to Machine Learning
This section delves deep into the core concepts of machine learning. You will learn how to train our machine, create labels and features, learn about the various data formats, and understand classification, regression, and clustering.
1. Let's Introduce Machine Learning
This brief video outlines machine learning, its importance, and how it can be used in various applications to make life easier.
2. Kids versus Computer Learning
In this lesson, we will compare their learning features to understand the fundamental difference between a kid and a computer.
3. Dataset
In this lesson, we will dive into the hardcore process of machine learning and the fundamental elements used in machine learning, like datasets, training and testing data, outliers, models, and so on.
4. Labels and Features
In this lesson, we will look at a target or label as output data and feature, a measurable property or characteristic of an element.
5. Outliers
Let's look at outliers, the data points of a dataset that differ from others and are usually excluded by visualizing a dataset.
6. Model and Training
Here, we will look at a machine learning model, an arithmetic expression or equation that fits and learns to predict data.
7. Overfitting and Underfitting
In this video, you will learn about overfitting, a modeling error when a model performs well in training but not in testing, and underfitting, where the model neither performs well during training nor during testing.
8. Accuracy and Error
This lecture explains accuracy and error in data when the predicted outcome is close to the expected result and when it is not defined as an error.
9. Formats of Data
You will learn about the different data formats in machine learning, including structured (labeled or unlabeled) and unstructured data, and how to choose the best format.
10. Types of Learning
Here, we will understand the learning types, including supervised and unsupervised machine learning algorithms.
11. Classification versus Regression
Let's learn about the three modes of machine learning: classification, regression, and clustering.
12. Clustering
In this lesson, you will learn about an unsupervised branch of learning called clustering, which involves grouping elements with no labels to classify them.
13. Recap, Flow of Machine Learning Project
This video is a quick recap of what we have learned thus far, and we will also be working on a project called the Flow of Machine Learning.
4. Random Forest Step-by-Step
This section focuses on the Random Forest concepts that deal with reading and manipulating datasets; you will learn about the pros and cons of Random Forest and use pandas for the Random Forest.
1. Introduction and Motivation
This video outlines the concepts of Random Forest when we can use the Random Forest, and its benefits and features.
2. How Decision Trees and Random Forest Work
We will understand what a decision tree is and create a decision tree and get a prediction result from the decision tree.
3. Pros and Cons of Random Forest
In this video, we will look at the benefits and limitations of Random Forest and the complexities involved in decision-making using Random Forest.
4. Introduction to the Final Project
In this video, we will discuss our final project, which classifies the titanic dataset using Random Forest.
5. Using NumPy for Random Forest
In this lesson, you will learn about the NumPy tool available in the Python library and look at the advantages of using NumPy.
6. Using Pandas for Random Forest (1)
This video introduces us to Pandas data structures and analysis tools, which help make data easy to handle and intuitive.
7. Using Pandas for Random Forest (2)
This is a continuation of the previous lesson, and here we will look at conditionally selecting values from a dataset.
8. Reading and Manipulating Dataset
Previously, you learned how to read a dataset; now, we will look at manipulating the data and using a sample dataset in our code.
9. Using Matplotlib for Data Visualization (1)
This video demonstrates data visualization using the Matplotlib function and explains data cleaning by removing outliers and filling in missing values.
10. Using Matplotlib for Data Visualization (2)
In this continuation lecture, we will focus on completing the assignment that we started on in the last lesson.
11. Dealing with Missing Values
Let's look at the first step involved in the data cleaning process, which is filling or removing missing values from a dataset.
12. Outliers Removal
In the second part of the data cleaning process, we will look at an outlier in detail and learn how to correct or remove the outlier.
13. Categorical to Numeric Conversion
In a machine, it understands values in the form of numbers. You will learn how to convert non-numeric data to numeric without changing the feature of the value.
14. Quick Implementation of Random Forest Model
Let's quickly implement Random Forest using the sklearn Random Forest model to tune the model's performance according to the project.
15. Feature Importance
After understanding features in a previous lesson, we will look at finding the most critical features' probability.
16. Recursion
In this video, you will learn about a complete implementation of the Random Forest using only Pandas to read the data and look at the results of the sklearn function.
17. Structure
In this video, we will discuss the structures of a Random Forest, namely forest, tree, leaf node, and decision node.
18. Importing Data, Helper Functions
Before creating a decision tree, we will first learn to import our dataset using Pandas.
19. Question and Partition
In this video, you will learn to create two more helper functions, question, and partition, which define statements for querying and retrieving data.
20. Impurity
Like all dataset characteristics, we will look at the impurities in a dataset and how they should be minimum in a good dataset.
21. Information Gain
In this video, we will define columns for questioning and determine how much information can be gained by splitting a column.
22. Best Slip
Here, we will determine the best split at any decision node where information is maximum and split into two branches, true and false.
23. Leaf and Decision Node
In this lesson, you will learn to create two classes, a leaf node and a decision node, with a constructor.
24. How to Build a Tree
After creating the decision node and leaf node classes, we will build our tree to add the nodes.
25. How to Classify
Let's learn to write a classification method that will train our module and help us get predictions.
26. Accuracy and Error
In this video, you will learn to implement the accuracy method to help determine our model's performance.
5. Conclusion
This section focuses on all the concepts learned so far and summarizes them succinctly.
1. Concluding remarks
In this video, we will look at the concluding remarks of the course and recap what we learned through the course, briefly.

A step-by-step guide that walks you through the fundamentals of Python programming followed using Python libraries to create random forest from scratch. A comprehensive course designed for both beginners with some programming experience or even those who know nothing about ML and random forest!

School of Business and Technology London

NVQ Assessor

Certificate in Further Education & Training (Cert Ed)

Diploma for School Business Leaders (DSBM)

Diploma in Management Studies (DMS)

Diploma in Professional Studies (DPS)

QUALIFI Level 3 Diploma in Data Science

<strong>In this course, we will process massive streams of real-time data using Spark Streaming and create Spark applications using the Scala programming language (v2.12). We will also get our hands-on with some real live Twitter data, simulated streams of Apache access logs, and even data used to train machine learning models.</strong>
        <p>In this course, you will learn the basics of the Scala programming language; learn how Apache Spark operates on a cluster; set up discretized streams with Spark Streaming and transform them as data is received; analyze streaming data over sliding windows of time; maintain stateful information across streams of data; connect Spark Streaming with highly scalable sources of data, including Kafka, Flume, and Kinesis; dump streams of data in real-time to NoSQL databases such as Cassandra; run SQL queries on streamed data in real-time; train machine learning models in real-time with streaming data, and use them to make predictions that keep getting better over time; and also, package, deploy, and run self-contained Spark Streaming code to a real Hadoop cluster using Amazon Elastic MapReduce.

This course is very hands-on, filled with achievable activities and exercises to reinforce your learning. By the end of this course, you will be confidently creating Spark Streaming scripts in Scala and be prepared to tackle massive streams of data in a whole new way. You will be surprised at how easy Spark Streaming makes it!

All the codes and supporting files for this course are available at https://github.com/packtpublishing/streaming-big-data-with-spark-streaming-scala-and-spark-3-</p>
        <h2>What You Will Learn</h2><p>Process large amounts of real-time data using the Spark Streaming module<br>Create efficient Spark applications using the Scala programming language<br>Integrate Spark Streaming with various data sources<br>Integrate Spark Streaming with Spark SQL to query your data in real time<br>Train machine learning models with streaming data, and use for real-time predictions<br>Maintain stateful data across a continuous stream of input data</p>
        <h2>Audience</h2><p>If you are a student who wants to learn how to use Apache Spark or a big data professional who wants to process large amounts of data on a real-time basis, this course is for you. Some basic programming and scripting experience is required to get the most out of the course.</p>
        <h2>Approach</h2><p>This course is very hands-on, filled with achievable activities and exercises to reinforce your learning. In this course, we will cover some real live Twitter data, simulated streams of Apache access logs, and even data used to train machine learning models! You will write and run real Spark Streaming jobs right at home on your own PC.</p>
        <h2>Key Features</h2><p>Process streams of real-time data from various sources with Spark Streaming * Query your streaming data in real-time using Spark SQL * A comprehensive tutorial with practical examples to help you develop real-time Spark applications</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/streaming-big-data-with-spark-streaming-scala-and-spark-3-</p>
        <h2>About the Author</h2><h3>Frank Kane</h3><p>Frank Kane has spent nine years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers all the time. He holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology and teaches others about big data analysis.</p>
        <h2>Course Outline</h2><h4>1. Getting Started</h4><p>In this first section, we will introduce the course and set up the development environment for Spark and Scala. We will then create our first Spark Streaming application to display live Twitter feeds.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction, and Getting Set Up</p><p style='margin: 0'>A brief introduction to the course, and then we will get your development environment for Spark and Scala all set up on your desktop. A quick test application will confirm Spark is working on your system!</p></td></tr><tr><td style='padding: 15px;'><p>2. [Activity] Stream Live Tweets with Spark Streaming!</p><p style='margin: 0'>Get set up with a Twitter developer account and run your first Spark Streaming application to listen to and print out live Tweets as they happen!</p></td></tr></tbody></table><br><h4>2. A Crash Course in Scala</h4><p>In this section, we will take a deep dive into Scala. Even if you've never used Scala before, we will cover all of the syntax and structure that you will need to know in order to begin mining your data. We will run through flow control, functions, and data structures with numerous examples throughout so that you can see everything in action and make sure you're understanding it.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. [Activity] Scala Basics</p><p style='margin: 0'>We start our crash course in the Scala programming language by covering some basics of the language: types and variables, printing, and Boolean comparisons.</p></td></tr><tr><td style='padding: 15px;'><p>2. [Exercise] Flow Control in Scala</p><p style='margin: 0'>Our Scala crash course continues, illustrating various means of flow control in Scala. For loops, do/while loops, while loops and so on.</p></td></tr><tr><td style='padding: 15px;'><p>3. [Exercise] Functions in Scala</p><p style='margin: 0'>Scala is a functional programming language, and so understanding how functions work and are treated in Scala is hugely important! This lecture covers the fundamentals, and lets you put it into practice.</p></td></tr><tr><td style='padding: 15px;'><p>4. [Exercise] Data Structures in Scala</p><p style='margin: 0'>We wrap up our Scala crash course with commonly used data structures using in Spark with Scala. Tuples, lists, and maps.</p></td></tr></tbody></table><br><h4>3. Spark Streaming Concepts</h4><p>In this section, we will introduce the core concepts of Spark and Spark Streaming. We will define and work with RDDs (Resilient Distributed Datasets) and show you examples of what they can do. RDDs are the key to understanding Spark, and therefore also Spark Streaming. This is also where we will dive deeper into Spark Streaming itself, understanding its inner workings and how the concept of windowing will enable you to aggregate and analyze data over time. Finally, we will take a look at reliability and fault tolerance with regards to Spark Streaming and a few ways in which you can maximize reliability.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Spark</p><p style='margin: 0'>Before you can learn about Spark Streaming, you need to understand how Spark itself works at a high level! This video covers the why and how of Apache Spark, of which Spark Streaming is a component.</p></td></tr><tr><td style='padding: 15px;'><p>2. The Resilient Distributed Dataset (RDD)</p><p style='margin: 0'>The fundamental object of Spark programming is the Resilient Distributed Dataset (RDD), and this is used not just in Spark but also within Spark Streaming scripts. This lecture explains what they are and what you can do with them.</p></td></tr><tr><td style='padding: 15px;'><p>3. [Activity] RDD's in Action: Simple Word Count Application</p><p style='margin: 0'>Let's walk through and actually run a simple Spark script that counts the number of occurrences of each word in a book.</p></td></tr><tr><td style='padding: 15px;'><p>4. Introduction to Spark Streaming</p><p style='margin: 0'>We finally have all the pr-requisite knowledge to start talking about Spark Streaming itself in more detail! We will cover how it works, what it's for, and its architecture.</p></td></tr><tr><td style='padding: 15px;'><p>5. [Activity] Revisiting the PrintTweets application</p><p style='margin: 0'>Now that we know more, let's go revisit that first Spark Streaming application we ran in lecture two, and dive into how it really works.</p></td></tr><tr><td style='padding: 15px;'><p>6. Windowing: Aggregating data over longer time spans</p><p style='margin: 0'>Windowing allows you to analyze streaming data over a sliding window of time, which lets you do much more than just transform streaming data and store it someplace else. We will cover the concepts of the batch, window, and slide intervals, and how they work together to let you aggregate streaming data over some period of time.</p></td></tr><tr><td style='padding: 15px;'><p>7. Fault Tolerance in Spark Streaming</p><p style='margin: 0'>How can Spark Streaming do so much work continuously in a reliable manner? We will uncover some of its tricks for reliability, as well as tips for configuring Spark Streaming to be as reliable as possible.</p></td></tr></tbody></table><br><h4>4. Spark Streaming Examples with Twitter</h4><p>Now that we have seen the theory behind Spark Streaming, this section is dedicated to putting all of that into action. We will look at three practical examples of taking Twitter data and applying our new learning to it with file output, windowing, and live tracking, and analysis.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. [Exercise] Saving Tweets to Disk</p><p style='margin: 0'>We will build on our "print tweets" example to actually store the incoming tweets to disk and illustrate how Spark Streaming can handle file output.</p></td></tr><tr><td style='padding: 15px;'><p>2. [Exercise] Tracking the Average Tweet Length</p><p style='margin: 0'>Compute the average length of a tweet, using windowing in Spark Streaming.</p></td></tr><tr><td style='padding: 15px;'><p>3. [Exercise] Tracking the Most Popular Hashtags</p><p style='margin: 0'>This is a fun one! We will track the most popular hashtags in Twitter over time and watch how they change in real time!</p></td></tr></tbody></table><br><h4>5. Spark Streaming Examples with Clickstream / Apache Access Log Data</h4><p>This section will continue to look at some of the more advanced aspects of Spark Streaming, this time using Apache access logs to monitor website traffic in real time, showing the most accessed pages and flagging up and log errors as they occur. We will also use SparkSQL to query our data live. Finally, we will look at Spark 2's new structured streaming approach, and how we can use DataSets to streamline our analysis.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. [Exercise] Tracking the Top URL's Requested</p><p style='margin: 0'>We will simulate an incoming stream of Apache access logs and use Spark Streaming to keep track of the most-requested web pages in real time!</p></td></tr><tr><td style='padding: 15px;'><p>2. [Exercise] Alarming on Log Errors</p><p style='margin: 0'>This example will listen to an Apache access log stream and raise an alarm if too many errors are returned by the server in real time.</p></td></tr><tr><td style='padding: 15px;'><p>3. [Exercise] Integrating Spark Streaming with Spark SQL</p><p style='margin: 0'>We will integrate Spark Streaming with Spark SQL, allowing us to run SQL queries on data as it is streamed in! Again, we will use Apache logs as an example.</p></td></tr><tr><td style='padding: 15px;'><p>4. Introduction to Structured Streaming</p><p style='margin: 0'>Spark 2.0 introduced experimental support for Structured Streaming, a new DataSet-based API for Spark Streaming that is bound to become increasingly important. Learn how it works.</p></td></tr><tr><td style='padding: 15px;'><p>5. [Activity] Analyzing Apache Log files with Structured Streaming</p><p style='margin: 0'>As an example, we will stream Apache access logs in from a directory and use Structured Streaming to count up status codes over a one-hour moving window.</p></td></tr></tbody></table><br><h4>6. Integrating with Other Systems</h4><p>In this section, we will examine how Spark Streaming can be integrated with other popular data science systems. We will see how it can work with Apache Kafka to listen to and process messages in real time; how Apache Flume can work with Spark Streaming to process huge amounts of log data; using Amazon Kinesis with Spark Streaming to process live data on an AWS/EC2 cluster; and using Cassandra to store massive datasets and analyze them at speed. We will also cover how to write your own Data Receiver class in order to connect Spark Streaming to other proprietary systems.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Integrating with Apache Kafka</p><p style='margin: 0'>Apache Kafka is a popular and robust technology for publishing messages across a cluster on a large scale. We will show how to get Spark Streaming to listen to Kafka topics and process them in real time.</p></td></tr><tr><td style='padding: 15px;'><p>2. Integrating with Apache Flume</p><p style='margin: 0'>Flume is a popular technology for publishing log information at large scale, especially on a Hadoop cluster. We will illustrate how to set up both push-based and pull-based Flume configurations with Spark Streaming and discuss the tradeoffs of each.</p></td></tr><tr><td style='padding: 15px;'><p>3. Integrating with Amazon Kinesis</p><p style='margin: 0'>Amazon's Kinesis Streaming service is basically Kafka on AWS. If you're working with an AWS/EC2 cluster, you will want to know how to integrate Spark Streaming with Kinesis - and that's what this lecture covers.</p></td></tr><tr><td style='padding: 15px;'><p>4. [Activity] Writing Custom Data Receivers</p><p style='margin: 0'>What if you need to integrate Spark Streaming with some proprietary system that does not have an existing connection library? Well, you can always write your own Receiver class. This example shows you how and actually lets you build and run one.</p></td></tr><tr><td style='padding: 15px;'><p>5. Integrating with Cassandra</p><p style='margin: 0'>Cassandra is a popular "NoSQL" database that can be used to provide fast access to massive data sets to real-time applications. Dumping data transformed by Spark Streaming into a Cassandra database can expose that data to your larger, real-time services. We will show you how, and actually run a simple example.</p></td></tr></tbody></table><br><h4>7. Advanced Spark Streaming Examples</h4><p>In this section, we will run through some more advanced real-world examples of Spark Streaming. We will examine tracking stateful information, machine learning with K-Means clustering, and feeding data into linear regression models.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. [Exercise] Stateful Information in Spark Streams</p><p style='margin: 0'>Spark has the ability to track arbitrary state across streams of data as they come in, such as web sessions, running totals and so on. This example shows you how it all works, and challenges you to track your own state using our example as a baseline.</p></td></tr><tr><td style='padding: 15px;'><p>2. [Activity] Streaming K-Means Clustering</p><p style='margin: 0'>Spark Streaming integrates with some of Spark's MLLib (Machine Learning Library) capabilities. This example creates a real-time K-Means clustering example; it does unsupervised machine learning that continually gets better as more training data feeds into it.</p></td></tr><tr><td style='padding: 15px;'><p>3. [Activity] Streaming Linear Regression</p><p style='margin: 0'>Spark Streaming can also feed data in real-time to linear regression models, that get better over time as more data is fed into them. This example shows linear regression in action with Spark Streaming.</p></td></tr></tbody></table><br><h4>8. Spark Streaming in Production</h4><p>This section covers how to move your applications into a production environment, using the spark-submit command to process scripts from the command line. We will also look at packaging scripts and dependencies with SBT and running code on a real cluster with Amazon's Elastic MapReduce (EMR). Finally, we will troubleshoot some code. We will see what can go wrong, and how you can identify and debug issues and tune your Spark applications for peak performance.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. [Activity] Packaging and Running Spark Code in Production</p><p style='margin: 0'>Your production applications won't be run from within the Scala IDE; you will need to run them from a command line, and potentially on a cluster. The spark-submit command is used for this. We will show you how to package up your application and run it using spark-submit from a command prompt.</p></td></tr><tr><td style='padding: 15px;'><p>2. [Activity] Packaging Your Code with SBT</p><p style='margin: 0'>If your Spark Streaming application has external library dependencies that might not be already present on every machine in your cluster, the SBT tool can manage those dependencies for you, and package them into the JAR file you run with spark-submit. We will show you how it works with a real example.</p></td></tr><tr><td style='padding: 15px;'><p>3. Running on a Real Hadoop Cluster with EMR</p><p style='margin: 0'>We will run our simple word count example on a real cluster, using Amazon's Elastic MapReduce service! This just shows you what's involved in running a Spark Streaming job on a real cluster as opposed to your desktop; there are a few parameters to Spark-submit you need to worry about and getting your scripts and data in the right place is also something you need to deal with.</p></td></tr><tr><td style='padding: 15px;'><p>4. Troubleshooting and Tuning Spark Jobs</p><p style='margin: 0'>Spark jobs rarely run perfectly, if at all, on the first try - some tuning and debugging is usually required and arriving at the right scale of your cluster is also necessary. We will cover some performance tips, and how to troubleshoot what's going on with a Spark Streaming job running on a cluster.</p></td></tr></tbody></table><br><h4>9. You Made It!</h4><p>This final section runs through some recommendations on where to learn more about Spark Streaming.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning More</p><p style='margin: 0'>Want to learn more about Spark Streaming? Here are a few books and other resources I've found valuable.</p></td></tr></tbody></table>

In this course, we will process massive streams of real-time data using Spark Streaming and create Spark applications using the Scala programming language (v2.12). We will also get our hands-on with some real live Twitter data, simulated streams of Apache access logs, and even data used to train machine learning models.
In this course, you will learn the basics of the Scala programming language; learn how Apache Spark operates on a cluster; set up discretized streams with Spark Streaming and transform them as data is received; analyze streaming data over sliding windows of time; maintain stateful information across streams of data; connect Spark Streaming with highly scalable sources of data, including Kafka, Flume, and Kinesis; dump streams of data in real-time to NoSQL databases such as Cassandra; run SQL queries on streamed data in real-time; train machine learning models in real-time with streaming data, and use them to make predictions that keep getting better over time; and also, package, deploy, and run self-contained Spark Streaming code to a real Hadoop cluster using Amazon Elastic MapReduce. This course is very hands-on, filled with achievable activities and exercises to reinforce your learning. By the end of this course, you will be confidently creating Spark Streaming scripts in Scala and be prepared to tackle massive streams of data in a whole new way. You will be surprised at how easy Spark Streaming makes it! All the codes and supporting files for this course are available at https://github.com/packtpublishing/streaming-big-data-with-spark-streaming-scala-and-spark-3-
What You Will Learn
Process large amounts of real-time data using the Spark Streaming module
Create efficient Spark applications using the Scala programming language
Integrate Spark Streaming with various data sources
Integrate Spark Streaming with Spark SQL to query your data in real time
Train machine learning models with streaming data, and use for real-time predictions
Maintain stateful data across a continuous stream of input data
Audience
If you are a student who wants to learn how to use Apache Spark or a big data professional who wants to process large amounts of data on a real-time basis, this course is for you. Some basic programming and scripting experience is required to get the most out of the course.
Approach
This course is very hands-on, filled with achievable activities and exercises to reinforce your learning. In this course, we will cover some real live Twitter data, simulated streams of Apache access logs, and even data used to train machine learning models! You will write and run real Spark Streaming jobs right at home on your own PC.
Key Features
Process streams of real-time data from various sources with Spark Streaming * Query your streaming data in real-time using Spark SQL * A comprehensive tutorial with practical examples to help you develop real-time Spark applications
Github Repo
https://github.com/packtpublishing/streaming-big-data-with-spark-streaming-scala-and-spark-3-
About the Author
Frank Kane
Frank Kane has spent nine years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers all the time. He holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology and teaches others about big data analysis.
Course Outline
1. Getting Started
In this first section, we will introduce the course and set up the development environment for Spark and Scala. We will then create our first Spark Streaming application to display live Twitter feeds.
1. Introduction, and Getting Set Up
A brief introduction to the course, and then we will get your development environment for Spark and Scala all set up on your desktop. A quick test application will confirm Spark is working on your system!
2. [Activity] Stream Live Tweets with Spark Streaming!
Get set up with a Twitter developer account and run your first Spark Streaming application to listen to and print out live Tweets as they happen!
2. A Crash Course in Scala
In this section, we will take a deep dive into Scala. Even if you've never used Scala before, we will cover all of the syntax and structure that you will need to know in order to begin mining your data. We will run through flow control, functions, and data structures with numerous examples throughout so that you can see everything in action and make sure you're understanding it.
1. [Activity] Scala Basics
We start our crash course in the Scala programming language by covering some basics of the language: types and variables, printing, and Boolean comparisons.
2. [Exercise] Flow Control in Scala
Our Scala crash course continues, illustrating various means of flow control in Scala. For loops, do/while loops, while loops and so on.
3. [Exercise] Functions in Scala
Scala is a functional programming language, and so understanding how functions work and are treated in Scala is hugely important! This lecture covers the fundamentals, and lets you put it into practice.
4. [Exercise] Data Structures in Scala
We wrap up our Scala crash course with commonly used data structures using in Spark with Scala. Tuples, lists, and maps.
3. Spark Streaming Concepts
In this section, we will introduce the core concepts of Spark and Spark Streaming. We will define and work with RDDs (Resilient Distributed Datasets) and show you examples of what they can do. RDDs are the key to understanding Spark, and therefore also Spark Streaming. This is also where we will dive deeper into Spark Streaming itself, understanding its inner workings and how the concept of windowing will enable you to aggregate and analyze data over time. Finally, we will take a look at reliability and fault tolerance with regards to Spark Streaming and a few ways in which you can maximize reliability.
1. Introduction to Spark
Before you can learn about Spark Streaming, you need to understand how Spark itself works at a high level! This video covers the why and how of Apache Spark, of which Spark Streaming is a component.
2. The Resilient Distributed Dataset (RDD)
The fundamental object of Spark programming is the Resilient Distributed Dataset (RDD), and this is used not just in Spark but also within Spark Streaming scripts. This lecture explains what they are and what you can do with them.
3. [Activity] RDD's in Action: Simple Word Count Application
Let's walk through and actually run a simple Spark script that counts the number of occurrences of each word in a book.
4. Introduction to Spark Streaming
We finally have all the pr-requisite knowledge to start talking about Spark Streaming itself in more detail! We will cover how it works, what it's for, and its architecture.
5. [Activity] Revisiting the PrintTweets application
Now that we know more, let's go revisit that first Spark Streaming application we ran in lecture two, and dive into how it really works.
6. Windowing: Aggregating data over longer time spans
Windowing allows you to analyze streaming data over a sliding window of time, which lets you do much more than just transform streaming data and store it someplace else. We will cover the concepts of the batch, window, and slide intervals, and how they work together to let you aggregate streaming data over some period of time.
7. Fault Tolerance in Spark Streaming
How can Spark Streaming do so much work continuously in a reliable manner? We will uncover some of its tricks for reliability, as well as tips for configuring Spark Streaming to be as reliable as possible.
4. Spark Streaming Examples with Twitter
Now that we have seen the theory behind Spark Streaming, this section is dedicated to putting all of that into action. We will look at three practical examples of taking Twitter data and applying our new learning to it with file output, windowing, and live tracking, and analysis.
1. [Exercise] Saving Tweets to Disk
We will build on our "print tweets" example to actually store the incoming tweets to disk and illustrate how Spark Streaming can handle file output.
2. [Exercise] Tracking the Average Tweet Length
Compute the average length of a tweet, using windowing in Spark Streaming.
3. [Exercise] Tracking the Most Popular Hashtags
This is a fun one! We will track the most popular hashtags in Twitter over time and watch how they change in real time!
5. Spark Streaming Examples with Clickstream / Apache Access Log Data
This section will continue to look at some of the more advanced aspects of Spark Streaming, this time using Apache access logs to monitor website traffic in real time, showing the most accessed pages and flagging up and log errors as they occur. We will also use SparkSQL to query our data live. Finally, we will look at Spark 2's new structured streaming approach, and how we can use DataSets to streamline our analysis.
1. [Exercise] Tracking the Top URL's Requested
We will simulate an incoming stream of Apache access logs and use Spark Streaming to keep track of the most-requested web pages in real time!
2. [Exercise] Alarming on Log Errors
This example will listen to an Apache access log stream and raise an alarm if too many errors are returned by the server in real time.
3. [Exercise] Integrating Spark Streaming with Spark SQL
We will integrate Spark Streaming with Spark SQL, allowing us to run SQL queries on data as it is streamed in! Again, we will use Apache logs as an example.
4. Introduction to Structured Streaming
Spark 2.0 introduced experimental support for Structured Streaming, a new DataSet-based API for Spark Streaming that is bound to become increasingly important. Learn how it works.
5. [Activity] Analyzing Apache Log files with Structured Streaming
As an example, we will stream Apache access logs in from a directory and use Structured Streaming to count up status codes over a one-hour moving window.
6. Integrating with Other Systems
In this section, we will examine how Spark Streaming can be integrated with other popular data science systems. We will see how it can work with Apache Kafka to listen to and process messages in real time; how Apache Flume can work with Spark Streaming to process huge amounts of log data; using Amazon Kinesis with Spark Streaming to process live data on an AWS/EC2 cluster; and using Cassandra to store massive datasets and analyze them at speed. We will also cover how to write your own Data Receiver class in order to connect Spark Streaming to other proprietary systems.
1. Integrating with Apache Kafka
Apache Kafka is a popular and robust technology for publishing messages across a cluster on a large scale. We will show how to get Spark Streaming to listen to Kafka topics and process them in real time.
2. Integrating with Apache Flume
Flume is a popular technology for publishing log information at large scale, especially on a Hadoop cluster. We will illustrate how to set up both push-based and pull-based Flume configurations with Spark Streaming and discuss the tradeoffs of each.
3. Integrating with Amazon Kinesis
Amazon's Kinesis Streaming service is basically Kafka on AWS. If you're working with an AWS/EC2 cluster, you will want to know how to integrate Spark Streaming with Kinesis - and that's what this lecture covers.
4. [Activity] Writing Custom Data Receivers
What if you need to integrate Spark Streaming with some proprietary system that does not have an existing connection library? Well, you can always write your own Receiver class. This example shows you how and actually lets you build and run one.
5. Integrating with Cassandra
Cassandra is a popular "NoSQL" database that can be used to provide fast access to massive data sets to real-time applications. Dumping data transformed by Spark Streaming into a Cassandra database can expose that data to your larger, real-time services. We will show you how, and actually run a simple example.
7. Advanced Spark Streaming Examples
In this section, we will run through some more advanced real-world examples of Spark Streaming. We will examine tracking stateful information, machine learning with K-Means clustering, and feeding data into linear regression models.
1. [Exercise] Stateful Information in Spark Streams
Spark has the ability to track arbitrary state across streams of data as they come in, such as web sessions, running totals and so on. This example shows you how it all works, and challenges you to track your own state using our example as a baseline.
2. [Activity] Streaming K-Means Clustering
Spark Streaming integrates with some of Spark's MLLib (Machine Learning Library) capabilities. This example creates a real-time K-Means clustering example; it does unsupervised machine learning that continually gets better as more training data feeds into it.
3. [Activity] Streaming Linear Regression
Spark Streaming can also feed data in real-time to linear regression models, that get better over time as more data is fed into them. This example shows linear regression in action with Spark Streaming.
8. Spark Streaming in Production
This section covers how to move your applications into a production environment, using the spark-submit command to process scripts from the command line. We will also look at packaging scripts and dependencies with SBT and running code on a real cluster with Amazon's Elastic MapReduce (EMR). Finally, we will troubleshoot some code. We will see what can go wrong, and how you can identify and debug issues and tune your Spark applications for peak performance.
1. [Activity] Packaging and Running Spark Code in Production
Your production applications won't be run from within the Scala IDE; you will need to run them from a command line, and potentially on a cluster. The spark-submit command is used for this. We will show you how to package up your application and run it using spark-submit from a command prompt.
2. [Activity] Packaging Your Code with SBT
If your Spark Streaming application has external library dependencies that might not be already present on every machine in your cluster, the SBT tool can manage those dependencies for you, and package them into the JAR file you run with spark-submit. We will show you how it works with a real example.
3. Running on a Real Hadoop Cluster with EMR
We will run our simple word count example on a real cluster, using Amazon's Elastic MapReduce service! This just shows you what's involved in running a Spark Streaming job on a real cluster as opposed to your desktop; there are a few parameters to Spark-submit you need to worry about and getting your scripts and data in the right place is also something you need to deal with.
4. Troubleshooting and Tuning Spark Jobs
Spark jobs rarely run perfectly, if at all, on the first try - some tuning and debugging is usually required and arriving at the right scale of your cluster is also necessary. We will cover some performance tips, and how to troubleshoot what's going on with a Spark Streaming job running on a cluster.
9. You Made It!
This final section runs through some recommendations on where to learn more about Spark Streaming.
1. Learning More
Want to learn more about Spark Streaming? Here are a few books and other resources I've found valuable.

Scala

Apache Spark

Big Data

In this course, we will process massive streams of real-time data using Spark Streaming and create Spark applications using the Scala programming language (v2.12). We will also get our hands-on with some real live Twitter data, simulated streams of Apache access logs, and even data used to train machine learning models.

Educators providing Unsupervised Machine Learning courses in Manchester delivered Online