Description

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

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The Complete Machine Learning Course with Python

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