Name: Machine Learning for Apps Level 4
Price: 319 GBP
Availability: InStock

<strong>This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.</strong>
        <p>Instead of using hard-coded rules for performing something, artificial intelligence (AI) and machine learning (ML) prepare the machines to learn things from data to decipher the complex patterns automatically and then use it for building multiple use cases. If you too want to make applications and solve complex problems using AI and ML, then this course will help you to get started with the basics.

The course starts by highlighting the learning objectives and explains the rise of AI in today's world. Next, you will be introduced to the concepts of ML. You will learn to understand ML terminologies and learn how to train a model. Towards the end, you will become familiar with the classification of ML systems, including the topics of supervised and unsupervised learning, regression, clustering, and reinforcement.

By the end of this course, you will have a fundamental knowledge of AI and ML.

All the resource for this course are available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1</p>
        <h2>What You Will Learn</h2><p>Find out why machine learning (ML) is called a black box<br>Get to grips with the features and labels of ML<br>Uncover techniques for training ML models<br>Find out the differences between supervised and unsupervised learning<br>Understand regression, clustering, and dimension reduction</p>
        <h2>Audience</h2><p>If you are a developer, product or project manager, engineer, or a data science enthusiast, who is looking to learn the fundamentals of artificial intelligence (AI) and machine learning (ML), this course is for you. There is no prerequisite to start this course as it is specially designed for absolute beginners.</p>
        <h2>Approach</h2><p>With the help of simple narration, interesting examples, and practical tips, this course helps you to get started with artificial intelligence (AI) and machine learning (ML).</p>
        <h2>Key Features</h2><p>Learn the basics of artificial intelligence (AI), machine learning (ML), and deep learning * Find out how ML helps in training a model * Discover how ML systems are classified</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1</p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers.

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started with Level 1!</h4><p></p><br><h4>2. The Rise of Artificial Intelligence</h4><p></p><br><h4>3. Introduction to Machine Learning</h4><p></p><br><h4>4. Classification of ML Systems</h4><p></p><br><h4>5. Course Summary</h4><p></p>

This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.
Instead of using hard-coded rules for performing something, artificial intelligence (AI) and machine learning (ML) prepare the machines to learn things from data to decipher the complex patterns automatically and then use it for building multiple use cases. If you too want to make applications and solve complex problems using AI and ML, then this course will help you to get started with the basics. The course starts by highlighting the learning objectives and explains the rise of AI in today's world. Next, you will be introduced to the concepts of ML. You will learn to understand ML terminologies and learn how to train a model. Towards the end, you will become familiar with the classification of ML systems, including the topics of supervised and unsupervised learning, regression, clustering, and reinforcement. By the end of this course, you will have a fundamental knowledge of AI and ML. All the resource for this course are available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1
What You Will Learn
Find out why machine learning (ML) is called a black box
Get to grips with the features and labels of ML
Uncover techniques for training ML models
Find out the differences between supervised and unsupervised learning
Understand regression, clustering, and dimension reduction
Audience
If you are a developer, product or project manager, engineer, or a data science enthusiast, who is looking to learn the fundamentals of artificial intelligence (AI) and machine learning (ML), this course is for you. There is no prerequisite to start this course as it is specially designed for absolute beginners.
Approach
With the help of simple narration, interesting examples, and practical tips, this course helps you to get started with artificial intelligence (AI) and machine learning (ML).
Key Features
Learn the basics of artificial intelligence (AI), machine learning (ML), and deep learning * Find out how ML helps in training a model * Discover how ML systems are classified
Github Repo
https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1
About the Author
Idan Gabrieli
Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies. Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers. Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.
Course Outline
1. Getting Started with Level 1!

2. The Rise of Artificial Intelligence

3. Introduction to Machine Learning

4. Classification of ML Systems

5. Course Summary


Packt

Deep Learning

This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.

Machine Learning for Absolute Beginners - Level 1

General Admission

<strong>This course covers the basic concepts of machine learning (ML) that are crucial for getting started on the journey of becoming a skilled ML developer. You will become familiar with different algorithms and networks, such as supervised, unsupervised, neural networks, Convolutional Neural Network (CNN), and Super-Resolution Convolutional Neural Network (SRCNN), needed to develop effective ML solutions.</strong>
        <p>From self-driving cars to artificial intelligence (AI) bots, machine learning (ML) is slowly spreading its reach and making our devices smarter. If you have ever wanted to play a role in the future of technology development, then here is your chance to get started with ML. This course breaks the complex topics of ML into simple concepts that are easier to understand.
The course starts with an introduction to ML, explaining its applications in the real-world and how it is different from AI. Next, you will learn supervised and unsupervised algorithms and understand the role of neural networks in ML. Once you understand the ML algorithms, you will dive into building interesting projects to consolidate your learning. You will learn how to build a board game review prediction model, how to build a credit card fraud detection model, how to tokenize word and sentences using natural language processing), how to build an object recognition model, how to build an image quality improvement model, how to build a text classification model, how to build an image analysis model, and how to build a data compression model.
By the end of this course, you will have gained the skills to create real-world ML solutions.
All the recourses for this course are avialable at https://github.com/PacktPublishing/Projects-in-Machine-Learning-From-Beginner-to-Professional</p>
        <h2>What You Will Learn</h2><p>Detect credit card fraud by using probability densities<br>Become familiar with the natural language processing methodology<br>Use the Canadian Institute for Advanced Research-10 (CIFAR-10) object recognition dataset to implement a deep neural network<br>Improve image quality using Super-Resolution Convolutional Neural Network (SRCNN)<br>Solve a text classification task using multiple classification algorithms<br>Use K-means clustering in an unsupervised algorithm</p>
        <h2>Audience</h2><p>If you want to understand machine learning (ML) algorithms and concepts to build effective ML solutions for the modern world, this course is for you. Basic Python skills and a good understanding of mathematics are needed to get started with this course.</p>
        <h2>Approach</h2><p>With the help of engaging project building activities, examples, and quizzes, this course helps you to master the concepts of machine learning and gives you the confidence to build real-world solutions using supervised and unsupervised algorithms, neural networks, and a lot more.</p>
        <h2>Key Features</h2><p>Grasp the core concepts of machine learning (ML) * Find out how to use neural networks in ML projects * Learn how to build real-world projects using supervised and unsupervised learning algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Projects-in-Machine-Learning-From-Beginner-to-Professional</p>
        <h2>About the Author</h2><h3>Eduonix Learning Solutions</h3><p>Eduonix learning Solutions is a premier training and skill development organization which was started with a vision to bring world class training content, pedagogy and best learning practices to everyone's doorsteps . Eduonix aims to identify and provide the best learning and training environment. It identifies industry veterans and content creators around the globe and bring it to the global audience using number of intuitive platforms for easy and affordable access to quality content. Eduonix offers easy to understand online courses and workshops for everyday people. If you have ever wanted to learn a new skill, but don't want to attend four years of college to do it, we have a solution for you.</p>
        <h2>Course Outline</h2><h4>1. Introduction to Machine Learning (ML)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to this course.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Machine Learning (ML)?</p><p style='margin: 0'>This video explains the concept of ML.</p></td></tr><tr><td style='padding: 15px;'><p>3. Types and Applications of Machine Learning (ML)</p><p style='margin: 0'>This video explains the various types and applications of ML.</p></td></tr><tr><td style='padding: 15px;'><p>4. Artificial Intelligence (AI) versus Machine Learning (ML)</p><p style='margin: 0'>This video illustrates the difference between AI and ML.</p></td></tr><tr><td style='padding: 15px;'><p>5. Essential Mathematics for Machine Learning (ML) and Artificial Intelligence (AI)</p><p style='margin: 0'>This video highlights some mathematical concepts that are needed to understand ML and AI.</p></td></tr></tbody></table><br><h4>2. Supervised Learning - Part 1</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Supervised Learning</p><p style='margin: 0'>This video provides an introduction to supervised learning.</p></td></tr><tr><td style='padding: 15px;'><p>2. Linear Methods for Classification</p><p style='margin: 0'>This video explains the liner methods for classification.</p></td></tr><tr><td style='padding: 15px;'><p>3. Linear Methods for Regression</p><p style='margin: 0'>This video explains linear methods for regression.</p></td></tr><tr><td style='padding: 15px;'><p>4. Support Vector Machines (SVM)</p><p style='margin: 0'>This video explains the concept of SVM.</p></td></tr><tr><td style='padding: 15px;'><p>5. Basic Expansions</p><p style='margin: 0'>This video explains some basic expansions.</p></td></tr><tr><td style='padding: 15px;'><p>6. Model Selection Procedures</p><p style='margin: 0'>This video focuses on model selection procedures.</p></td></tr><tr><td style='padding: 15px;'><p>7. Bonus! Supervised Learning Project in Python - Part 1</p><p style='margin: 0'>This is the first part of a two-part video that presents a supervised learning project in Python.</p></td></tr><tr><td style='padding: 15px;'><p>8. Bonus! Supervised Learning Project in Python - Part 2</p><p style='margin: 0'>This is the second part of a two-part video that presents a supervised learning project in Python.</p></td></tr></tbody></table><br><h4>3. Unsupervised Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Unsupervised Learning</p><p style='margin: 0'>This video provides an introduction to unsupervised learning.</p></td></tr><tr><td style='padding: 15px;'><p>2. Association Rules</p><p style='margin: 0'>This video focuses on the association rule learning method.</p></td></tr><tr><td style='padding: 15px;'><p>3. Cluster Analysis</p><p style='margin: 0'>This video explains the concept of cluster analysis.</p></td></tr><tr><td style='padding: 15px;'><p>4. Reinforcement Learning</p><p style='margin: 0'>This video explains the concept of reinforcement learning.</p></td></tr><tr><td style='padding: 15px;'><p>5. Bonus! K-Means Clustering Project</p><p style='margin: 0'>This video presents a project on K-means clustering.</p></td></tr></tbody></table><br><h4>4. Neural Networks</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Neural Networks</p><p style='margin: 0'>This video provides an introduction to neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>2. Perceptron</p><p style='margin: 0'>This video explains the concept of perceptron in neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>3. Backpropagation Algorithm</p><p style='margin: 0'>This video explains the backpropagation algorithm in neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>4. Training Procedures</p><p style='margin: 0'>This video focuses on training procedures in neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>5. Convolutional Neural Network (CNN)</p><p style='margin: 0'>This video explains the concept of CNN.</p></td></tr></tbody></table><br><h4>5. Real-world Machine Learning (ML)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Real-world Machine Learning (ML)</p><p style='margin: 0'>This video provides an introduction to real-world ML.</p></td></tr><tr><td style='padding: 15px;'><p>2. Choosing an Algorithm</p><p style='margin: 0'>This video explains how to choose an algorithm.</p></td></tr><tr><td style='padding: 15px;'><p>3. Design and Analysis of Machine Learning (ML) Experiments</p><p style='margin: 0'>This video focuses on the design and analysis of ML experiments.</p></td></tr><tr><td style='padding: 15px;'><p>4. Common Software for Machine Learning (ML)</p><p style='margin: 0'>This video highlights some common software needed for ML.</p></td></tr></tbody></table><br><h4>6. Final Project</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Setting up OpenAI Gym</p><p style='margin: 0'>This video explains how to set up OpenAI gym.</p></td></tr><tr><td style='padding: 15px;'><p>2. Building and Training the Network - Part 1</p><p style='margin: 0'>This is the first part of a two-part video that demonstrates how to build and train the network.</p></td></tr><tr><td style='padding: 15px;'><p>3. Building and Training the Network - Part 2</p><p style='margin: 0'>This is the second part of a two-part video that demonstrates how to build and train the network.</p></td></tr></tbody></table><br><h4>7. Project 1 - Board Game Review Prediction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Building the Dataset - Part 1</p><p style='margin: 0'>This is the first part of a two-part video that explains how to build the dataset for the project.</p></td></tr><tr><td style='padding: 15px;'><p>3. Building the Dataset - Part 2</p><p style='margin: 0'>This is the second part of the two-part video that explains how to build the dataset for the project.</p></td></tr><tr><td style='padding: 15px;'><p>4. Training Models</p><p style='margin: 0'>This video demonstrates how to train models.</p></td></tr></tbody></table><br><h4>8. Project 2 - Credit Card Fraud Detection t</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Credit Card Fraud Detection - Dataset</p><p style='margin: 0'>This video explains how to build a dataset for the project.</p></td></tr><tr><td style='padding: 15px;'><p>3. Credit Card Fraud Detection - Algorithms</p><p style='margin: 0'>This video demonstrates how to work on algorithms for the project.</p></td></tr></tbody></table><br><h4>9. Project 3 - Getting Started with Natural Language Processing (NLP) in Python</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provided an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Tokenizing, Stopwords, and Stemming</p><p style='margin: 0'>This video explains the concepts of tokenizing, stopwords, and stemming.</p></td></tr><tr><td style='padding: 15px;'><p>3. Tagging, Chunking, and Named Entity Recognition</p><p style='margin: 0'>This video explains the concepts of tagging, chunking, and named entity recognition.</p></td></tr><tr><td style='padding: 15px;'><p>4. Text Classification</p><p style='margin: 0'>This video explains the process of text classification.</p></td></tr></tbody></table><br><h4>10. Project 4 - Obtaining Near State-of-the-art Performance on Object Recognition Tasks Using Deep Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Loading and Preprocessing the Canadian Institute For Advanced Research - 10 (CIFAR-10) Dataset</p><p style='margin: 0'>This video demonstrates how to load and preprocess the CIFAR-10 dataset.</p></td></tr><tr><td style='padding: 15px;'><p>3. Building and Deploying the All-Convolutional Neural Network (CNN) Network - Part 1</p><p style='margin: 0'>This is the first part of a two-part video that demonstrates how to build and deploy the All-CNN network.</p></td></tr><tr><td style='padding: 15px;'><p>4. Building and Deploying the All- Convolutional Neural Network (CNN) Network - Part 2</p><p style='margin: 0'>This is the second part of a two-part video that demonstrates how to build and deploy the All-CNN network.</p></td></tr></tbody></table><br><h4>11. Project 5 - Image Super-resolution with the Super-Resolution Convolution Neural Network (SRCNN)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Quality Metrics and Preprocessing Images</p><p style='margin: 0'>This video focuses on quality metrics and preprocessing images.</p></td></tr><tr><td style='padding: 15px;'><p>3. Image Super-resolution Using Deep Learning</p><p style='margin: 0'>This video explains how to perform image super-resolution using deep learning.</p></td></tr></tbody></table><br><h4>12. Project 6 - Natural Language Processing (NLP): Text Classification</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Feature Engineering</p><p style='margin: 0'>This video explains the concept of feature engineering in Natural Language Processing.</p></td></tr><tr><td style='padding: 15px;'><p>3. Deploying Scikit-learn (Sklearn) Classifiers</p><p style='margin: 0'>This video demonstrates how to deploy sklearn classifiers.</p></td></tr></tbody></table><br><h4>13. Project 7 - K-means Clustering for Image Analysis</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Preprocessing Images for Clustering</p><p style='margin: 0'>This video demonstrates how to preprocess images for clustering.</p></td></tr><tr><td style='padding: 15px;'><p>3. Evaluation and Visualization</p><p style='margin: 0'>This video explains the concept of evaluation and visualization.</p></td></tr></tbody></table><br><h4>14. Project 8 - Data Compression and Visualization Using Principal Component Analysis (PCA)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Elbow Method</p><p style='margin: 0'>This video focuses on the elbow method.</p></td></tr><tr><td style='padding: 15px;'><p>3. Principal Component Analysis (PCA) Compression and Visualization</p><p style='margin: 0'>This video explains how to perform compression and visualization using PCA.</p></td></tr></tbody></table>

This course covers the basic concepts of machine learning (ML) that are crucial for getting started on the journey of becoming a skilled ML developer. You will become familiar with different algorithms and networks, such as supervised, unsupervised, neural networks, Convolutional Neural Network (CNN), and Super-Resolution Convolutional Neural Network (SRCNN), needed to develop effective ML solutions.
From self-driving cars to artificial intelligence (AI) bots, machine learning (ML) is slowly spreading its reach and making our devices smarter. If you have ever wanted to play a role in the future of technology development, then here is your chance to get started with ML. This course breaks the complex topics of ML into simple concepts that are easier to understand.
The course starts with an introduction to ML, explaining its applications in the real-world and how it is different from AI. Next, you will learn supervised and unsupervised algorithms and understand the role of neural networks in ML. Once you understand the ML algorithms, you will dive into building interesting projects to consolidate your learning. You will learn how to build a board game review prediction model, how to build a credit card fraud detection model, how to tokenize word and sentences using natural language processing), how to build an object recognition model, how to build an image quality improvement model, how to build a text classification model, how to build an image analysis model, and how to build a data compression model.
By the end of this course, you will have gained the skills to create real-world ML solutions.
All the recourses for this course are avialable at https://github.com/PacktPublishing/Projects-in-Machine-Learning-From-Beginner-to-Professional
What You Will Learn
Detect credit card fraud by using probability densities
Become familiar with the natural language processing methodology
Use the Canadian Institute for Advanced Research-10 (CIFAR-10) object recognition dataset to implement a deep neural network
Improve image quality using Super-Resolution Convolutional Neural Network (SRCNN)
Solve a text classification task using multiple classification algorithms
Use K-means clustering in an unsupervised algorithm
Audience
If you want to understand machine learning (ML) algorithms and concepts to build effective ML solutions for the modern world, this course is for you. Basic Python skills and a good understanding of mathematics are needed to get started with this course.
Approach
With the help of engaging project building activities, examples, and quizzes, this course helps you to master the concepts of machine learning and gives you the confidence to build real-world solutions using supervised and unsupervised algorithms, neural networks, and a lot more.
Key Features
Grasp the core concepts of machine learning (ML) * Find out how to use neural networks in ML projects * Learn how to build real-world projects using supervised and unsupervised learning algorithms
Github Repo
https://github.com/PacktPublishing/Projects-in-Machine-Learning-From-Beginner-to-Professional
About the Author
Eduonix Learning Solutions
Eduonix learning Solutions is a premier training and skill development organization which was started with a vision to bring world class training content, pedagogy and best learning practices to everyone's doorsteps . Eduonix aims to identify and provide the best learning and training environment. It identifies industry veterans and content creators around the globe and bring it to the global audience using number of intuitive platforms for easy and affordable access to quality content. Eduonix offers easy to understand online courses and workshops for everyday people. If you have ever wanted to learn a new skill, but don't want to attend four years of college to do it, we have a solution for you.
Course Outline
1. Introduction to Machine Learning (ML)

1. Introduction
This video provides an introduction to this course.
2. What is Machine Learning (ML)?
This video explains the concept of ML.
3. Types and Applications of Machine Learning (ML)
This video explains the various types and applications of ML.
4. Artificial Intelligence (AI) versus Machine Learning (ML)
This video illustrates the difference between AI and ML.
5. Essential Mathematics for Machine Learning (ML) and Artificial Intelligence (AI)
This video highlights some mathematical concepts that are needed to understand ML and AI.
2. Supervised Learning - Part 1

1. Introduction to Supervised Learning
This video provides an introduction to supervised learning.
2. Linear Methods for Classification
This video explains the liner methods for classification.
3. Linear Methods for Regression
This video explains linear methods for regression.
4. Support Vector Machines (SVM)
This video explains the concept of SVM.
5. Basic Expansions
This video explains some basic expansions.
6. Model Selection Procedures
This video focuses on model selection procedures.
7. Bonus! Supervised Learning Project in Python - Part 1
This is the first part of a two-part video that presents a supervised learning project in Python.
8. Bonus! Supervised Learning Project in Python - Part 2
This is the second part of a two-part video that presents a supervised learning project in Python.
3. Unsupervised Learning

1. Introduction to Unsupervised Learning
This video provides an introduction to unsupervised learning.
2. Association Rules
This video focuses on the association rule learning method.
3. Cluster Analysis
This video explains the concept of cluster analysis.
4. Reinforcement Learning
This video explains the concept of reinforcement learning.
5. Bonus! K-Means Clustering Project
This video presents a project on K-means clustering.
4. Neural Networks

1. Introduction to Neural Networks
This video provides an introduction to neural networks.
2. Perceptron
This video explains the concept of perceptron in neural networks.
3. Backpropagation Algorithm
This video explains the backpropagation algorithm in neural networks.
4. Training Procedures
This video focuses on training procedures in neural networks.
5. Convolutional Neural Network (CNN)
This video explains the concept of CNN.
5. Real-world Machine Learning (ML)

1. Introduction to Real-world Machine Learning (ML)
This video provides an introduction to real-world ML.
2. Choosing an Algorithm
This video explains how to choose an algorithm.
3. Design and Analysis of Machine Learning (ML) Experiments
This video focuses on the design and analysis of ML experiments.
4. Common Software for Machine Learning (ML)
This video highlights some common software needed for ML.
6. Final Project

1. Setting up OpenAI Gym
This video explains how to set up OpenAI gym.
2. Building and Training the Network - Part 1
This is the first part of a two-part video that demonstrates how to build and train the network.
3. Building and Training the Network - Part 2
This is the second part of a two-part video that demonstrates how to build and train the network.
7. Project 1 - Board Game Review Prediction

1. Introduction
This video provides an introduction to the project.
2. Building the Dataset - Part 1
This is the first part of a two-part video that explains how to build the dataset for the project.
3. Building the Dataset - Part 2
This is the second part of the two-part video that explains how to build the dataset for the project.
4. Training Models
This video demonstrates how to train models.
8. Project 2 - Credit Card Fraud Detection t

1. Introduction
This video provides an introduction to the project.
2. Credit Card Fraud Detection - Dataset
This video explains how to build a dataset for the project.
3. Credit Card Fraud Detection - Algorithms
This video demonstrates how to work on algorithms for the project.
9. Project 3 - Getting Started with Natural Language Processing (NLP) in Python

1. Introduction
This video provided an introduction to the project.
2. Tokenizing, Stopwords, and Stemming
This video explains the concepts of tokenizing, stopwords, and stemming.
3. Tagging, Chunking, and Named Entity Recognition
This video explains the concepts of tagging, chunking, and named entity recognition.
4. Text Classification
This video explains the process of text classification.
10. Project 4 - Obtaining Near State-of-the-art Performance on Object Recognition Tasks Using Deep Learning

1. Introduction
This video provides an introduction to the project.
2. Loading and Preprocessing the Canadian Institute For Advanced Research - 10 (CIFAR-10) Dataset
This video demonstrates how to load and preprocess the CIFAR-10 dataset.
3. Building and Deploying the All-Convolutional Neural Network (CNN) Network - Part 1
This is the first part of a two-part video that demonstrates how to build and deploy the All-CNN network.
4. Building and Deploying the All- Convolutional Neural Network (CNN) Network - Part 2
This is the second part of a two-part video that demonstrates how to build and deploy the All-CNN network.
11. Project 5 - Image Super-resolution with the Super-Resolution Convolution Neural Network (SRCNN)

1. Introduction
This video provides an introduction to the project.
2. Quality Metrics and Preprocessing Images
This video focuses on quality metrics and preprocessing images.
3. Image Super-resolution Using Deep Learning
This video explains how to perform image super-resolution using deep learning.
12. Project 6 - Natural Language Processing (NLP): Text Classification

1. Introduction
This video provides an introduction to the project.
2. Feature Engineering
This video explains the concept of feature engineering in Natural Language Processing.
3. Deploying Scikit-learn (Sklearn) Classifiers
This video demonstrates how to deploy sklearn classifiers.
13. Project 7 - K-means Clustering for Image Analysis

1. Introduction
This video provides an introduction to the project.
2. Preprocessing Images for Clustering
This video demonstrates how to preprocess images for clustering.
3. Evaluation and Visualization
This video explains the concept of evaluation and visualization.
14. Project 8 - Data Compression and Visualization Using Principal Component Analysis (PCA)

1. Introduction
This video provides an introduction to the project.
2. Elbow Method
This video focuses on the elbow method.
3. Principal Component Analysis (PCA) Compression and Visualization
This video explains how to perform compression and visualization using PCA.

Python

TensorFlow

This course covers the basic concepts of machine learning (ML) that are crucial for getting started on the journey of becoming a skilled ML developer. You will become familiar with different algorithms and networks, such as supervised, unsupervised, neural networks, Convolutional Neural Network (CNN), and Super-Resolution Convolutional Neural Network (SRCNN), needed to develop effective ML solutions.

Projects in Machine Learning: From Beginner to Professional

Thinking about learning more about 

Artificial Intelligence?
The BCS Foundation Certificate in Artificial Intelligence is the advanced version of our Essentials Course Artificial Intelligence and includes more detail and insights about algebraic equations, vector calculus and schematics used in artificial intelligence and machine learning for you to learn how this new technology works.
You are not required to memorise or learn algebra just understand how it works.

You will learn human-centric ethical and sustainable human and artificial intelligence, artificial intelligence and robotics, applying the benefits of AI projects - challenges and risks, machine learning theory and practice – building a machine learning toolbox and the management, roles and responsibilities of humans and machines – the future of AI.
Duco Digital Training has been awarded as one of the best training providers in Artificial Intelligence.
Online, self-study, global access to study in any country
COURSE FEATURES
Study time: 40 hours
Gain Industry Skills
12 months course access
Includes exam
Job and Career Support
Tutor support
Learn Online - Study at home or work
Aligned to SFIAPlus IT Industry Framework
Globally Recognised BCS Certificate
Free 1 year BCS Membership*
Includes BCS Artificial Intelligence eBook.
*new BCS members.

This course includes 'Pass Assist', a unique package of support to equip learners with knowledge and confidence to pass your BCS exam, including: 
BCS Official Recommended Artificial Intelligence eBook
Practice exam papers
A series of videos to get the best out of the course, make a learning plan and preparing for the exam
Tutor support - use the discussion forum, email or What’s App to ask questions.
BCS Foundation Certificate in Artificial Intelligence
The BCS Foundation Certificate in Artificial Intelligence is a globally recognised professional qualification which will help develop your knowledge and understanding in these specific subjects:
Course Subjects You Learn
1. Ethical and Sustainable Human and Artificial Intelligence (20%)
Recall the general definition of Human and Artificial Intelligence (AI).
Describe the concept of intelligent agents.Describe a modern approach to Human logical levels of thinking using
Robert Dilt’s Model.
Describe what are Ethics and Trustworthy AI, in particular:
Recall the general definition of ethics.Recall that a Human Centric Ethical Purpose respects fundamental rights,
principles, and values.Recall that Ethical Purpose AI is delivered using Trustworthy AI that is
technically robust.Recall that the Human Centric Ethical Purpose Trustworthy AI is
continually assessed and monitored.
Describe the three fundamental areas of sustainability and the United Nation’s
seventeen sustainability goals.
Describe how AI is part of ‘Universal Design,’ and ‘The Fourth Industrial
Revolution’.
Understand that ML is a significant contribution to the growth of Artificial
Intelligence.
Describe ‘learning from experience’ and how it relates to Machine Learning
(ML) (Tom Mitchell’s explicit definition).
2. Artificial Intelligence and Robotics (20%)
Demonstrate understanding of the AI intelligent agent description, and:
list the four rational agent dependencies.describe agents in terms of performance measure, environment, actuators,
and sensorsdescribe four types of agent: reflex, model-based reflex, goal-based and
utility-based.identify the relationship of AI agents with Machine Learning (ML).
Describe what a robot is and:
Describe robotic paradigms,
Describe what an intelligent robot is and:
Relate intelligent robotics to intelligent agents.
3. Applying the benefits of AI - challenges and risks (15%)
Describe how sustainability relates to human-centric ethical AI and how our values
will drive our use of AI will change humans, society, and organisations
Explain the benefits of Artificial Intelligence by.
list advantages of machine and human and machine systems.
Describe the challenges of Artificial Intelligence, and give;
general ethical challenges AI raises.. general examples of the limitations of AI systems compared to human
systems.
Demonstrate understanding of the risks of AI project, and:
give at least one a general example of the risks of AI,describe a typical AI project team in particular,
describe a domain expert,describe what is ‘fit-of-purpose’,describe the difference between waterfall and agile projects.
List opportunities for AI.
Identify a typical funding source for AI projects and relate to the NASA Technology
Readiness Levels (TRLs).
4. Starting AI how to build a Machine Learning Toolbox - Theory and Practice (30%)
Describe how we learn from data – functionality, software, and hardware,
List common open-source machine learning functionality, software, and
hardware.Describe introductory theory of Machine Learning.Describe typical tasks in the preparation of data.Describe typical types of Machine Learning Algorithms.Describe the typical methods of visualising data.
Recall which typical, narrow AI capability is useful in ML and AI agents’
functionality.
5. The Management, Roles and Responsibilities of humans and machines (15%)
Demonstrate an understanding that Artificial Intelligence (in particular, Machine
Learning) will drive humans and machines to work together.
List future directions of humans and machines working together.
Describe a ‘learning from experience’ Agile approach to projects
Describe the type of team members needed for an Agile project.
Study Easily Online with Duco Digital
• Work through online course content which is easy to follow, understand and in a logical format• Login to continue your course and monitor your progression easily at-a-glance• Book your online exam to take at home or in an office, no need to visit a test centre• Multiple choice exam• Test paper included with practice questions• Tutor support available 9 am - 5 pm weekdays GMT• Start today!
Meet Our Instructor

Darren is the Company Director of Duco Digital Training and a BCS Accredited Trainer in Artificial Intelligence. He has over 30 years of experience in international marketing, computing projects, training students for organisations and governments in AI, marketing and sales in Europe, SE Asia and the Middle East and is currently researching and studying a PhD in AI Ethics.If you have any questions about choosing the right course for your career, please contact us.
Who is BCS?
Recognised globally by corporations, academics and IT professionals, BCS is the British Computer Society, more commonly known as the Chartered Institute for IT.

They have over 60,000 members in 150 countries, and a wider community of business leaders, educators, practitioners and policy-makers all committed to their mission of leading the IT industry through its ethical challenges, to support the people who work in the industry, and to make IT good for society.
Why should I take this course?
This Foundation professional certification in Artificial Intelligence & Machine Learning will help develop your career in Artificial Intelligence and Machine Learning, especially if you are planning to or are now working on an AI or Machine Learning project.

With the Artificial Intelligence sector set to be worth over $15tn by 2030, this is a great time to learn about AI.

Have a question? - contact us
If you have any questions, please contact us or message us on What'sApp.

We're here to help you pass your course and grow your career; study with Duco Digital Training, the UK's leading online BCS accredited training provider.

Duco Digital Training

IT Certifications

IT Skills

IT (Information Technology)

Computing & IT

IT Literacy

Thinking about learning more about Artificial Intelligence?

The BCS Foundation Certificate in Artificial Intelligence is the advanced version of our Essentials Course Artificial Intelligence and includes more detail and insights about algebraic equations, vector calculus and schematics used in artificial intelligence and machine learning for you to learn how this new technology works.

Artificial Intelligence - BCS Foundation Certificate

<p>In many industries, machine learning (ML) for predictive maps using Python and Leaflet is practical. So, it is highly valued in the UK and worldwide. These lessons are available in one convenient location with our QLS-endorsed course, 'Machine Learning for Predictive Maps in Python and Leaflet.'</p><p>The curriculum for the course 'Machine Learning for Predictive Maps in Python and Leaflet' is organised methodically, beginning with an introduction that acquaints students with the goals of the course. The steps for creating the entire application are provided in several parts, which start from writing the Django Server-Side code to the project source code. After that, the course moves via leaflet programming, allowing students to make dynamic, eye-catching maps. Ultimately, students receive a concrete resource for practice and future reference in the project source code.</p><p>This premium QLS-endorsed Machine Learning for Predictive Maps in Python and Leaflet course has intensive study modules, dedicated tutor assistance, customer support, flexible learning opportunities, and a globally recognised certification. Thus, completing our Machine Learning for Predictive Maps in Python and Leaflet course will give you limitless opportunities anywhere and everywhere since the value of QLS-endorsed courses is relatively high!!</p><p>Enrol in this Python and Leaflet course on machine learning for predictive maps now to transform decision-making procedures and influence industry trends through predictive mapping solutions.</p><p><strong>Learning Outcome of the 'Machine Learning for Predictive Maps in Python and Leaflet' Course</strong><strong>:</strong></p><ul><li>Learn to use predictive mapping techniques based on Python to make informed decisions across a range of industries</li><li>Gain a thorough understanding of Leaflet programming to create dynamic and eye-catching maps</li><li>Build a Django server-side framework to lay a solid basis for developing applications and handling data</li><li>Simplify and improve efficiency by automating machine learning pipelines</li><li>Develop your application deployment skills by applying what you have learned to real-world situations</li><li>Access project source code for continued learning and reference, empowering further exploration and refinement of skills</li></ul><p><strong>Why Learn </strong><strong>Machine Learning for Predictive Maps in Python and Leaflet </strong><strong>with us:</strong></p><p>As one of the leading e-learning service providers globally, we strive to provide all our learners with the best e-learning experience possible that can make a real difference in their career progression.</p><p><strong>Our Service for Machine Learning for Predictive Maps in Python and Leaflet</strong><strong>:</strong></p><ul><li>Intensive Study Notes</li><li>Tutor Support</li><li>Customer Support</li><li>Teaching Assistant Assignment Help</li><li>QLS Endorsed Teaching Assistant Certificate</li><li>24/7 Learning Portal Access</li><li>Widely Compatible Teaching Assistant Study Materials</li><li>14 Days Refund Guarantee</li></ul><p><strong>Course Curriculum</strong></p><p><strong>Machine Learning for Predictive Maps in Python and Leaflet</strong></p><ul><li>Section 01: Introduction</li><li>Section 02: Setup and Installations</li><li>Section 03: Writing the Django Server-Side Code</li><li>Section 04: Writing the Application Front-end Code</li><li>Section 05: Machine Learning</li><li>Section 06: Automating the Machine Learning Pipeline</li><li>Section 07: Leaflet Programming</li><li>Section 08: Project Source Code</li></ul><p><strong>Assessment &amp; Certification</strong></p><p>Students must finish and turn in a thorough assignment covering all topics in our curriculum. Our expert tutor will review the assignment to ensure it meets Quality Learning Systems (QLS) standards. Once the assessment is successful and passes a quality check, students will be awarded a certificate for completing the Machine Learning for Predictive Maps in Python and Leaflet Course.</p><p><strong>Endorsed Certificate of Achievement from the Quality Licence Scheme.</strong></p><p>After the course, participants will receive an endorsed certificate as proof of successful completion. The learner may request a certificate if they complete all course assessments.</p><p><strong>Endorsement:</strong></p><p>After the course, participants will receive an endorsed certificate as proof of successful completion. The learner may request a certificate if they complete all course assessments.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        150 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>This Machine Learning for Predictive Maps in Python and Leaflet course is beneficial for a wide range of learners. It is especially suitable for the following learners:</p><ul><li><strong>Data scientists and analysts:</strong> Individuals seeking to broaden their expertise in predictive modelling and spatial analysis</li><li><strong>Professionals in Geographic Information Systems (GIS): </strong>Looking to incorporate machine learning into their workflows for mapping and spatial analysis</li><li><strong>Developers and Programmers:</strong> keen on integrating predictive mapping into their projects or applications.</li><li><strong>Urban Planners and Environmental Researchers:</strong> Investigating predictive mapping in the fields of planning, sustainability, and environmental impact</li><li><strong>Logistics and Supply Chain Analysts:</strong> Using predictive mapping to optimise routes and manage supply chains</li><li><strong>Pupils studying geography or computer science: </strong>Interested in finding out how machine learning and cartography are combining</li><li><strong>Decision-Making Roles Professionals:</strong> Looking for Tools and Insights to Support Strategic Decision-Making with Predictive Mapping</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>No specific requirements exist for enrolling in the Machine Learning for Predictive Maps in Python and Leafletcourse. Therefore, learners do not need previous qualifications to sign up for this course.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>'Machine Learning for Predictive Maps in Python and Leaflet' course is relevant to many trending professions as follows:</p><ul><li>Data Scientist/Spatial Analysis. £40,000-£70,000 per year.</li><li>Python developer (with GIS expertise). £35,000-£60,000 per year.</li><li>Machine learning engineer. £50,000-£80,000 per year.</li><li>Urban Planner/Analyst. £30,000-£50,000 per year.</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Diploma in Machine Learning for Predictive Maps in Python and Leaflet at QLS</h4>
        <p>
            Hard copy certificate -
            £75
        </p>

                        </div>

In many industries, machine learning (ML) for predictive maps using Python and Leaflet is practical. So, it is highly valued in the UK and worldwide. These lessons are available in one convenient location with our QLS-endorsed course, 'Machine Learning for Predictive Maps in Python and Leaflet.'
The curriculum for the course 'Machine Learning for Predictive Maps in Python and Leaflet' is organised methodically, beginning with an introduction that acquaints students with the goals of the course. The steps for creating the entire application are provided in several parts, which start from writing the Django Server-Side code to the project source code. After that, the course moves via leaflet programming, allowing students to make dynamic, eye-catching maps. Ultimately, students receive a concrete resource for practice and future reference in the project source code.
This premium QLS-endorsed Machine Learning for Predictive Maps in Python and Leaflet course has intensive study modules, dedicated tutor assistance, customer support, flexible learning opportunities, and a globally recognised certification. Thus, completing our Machine Learning for Predictive Maps in Python and Leaflet course will give you limitless opportunities anywhere and everywhere since the value of QLS-endorsed courses is relatively high!!
Enrol in this Python and Leaflet course on machine learning for predictive maps now to transform decision-making procedures and influence industry trends through predictive mapping solutions.
Learning Outcome of the 'Machine Learning for Predictive Maps in Python and Leaflet' Course:
Learn to use predictive mapping techniques based on Python to make informed decisions across a range of industries
Gain a thorough understanding of Leaflet programming to create dynamic and eye-catching maps
Build a Django server-side framework to lay a solid basis for developing applications and handling data
Simplify and improve efficiency by automating machine learning pipelines
Develop your application deployment skills by applying what you have learned to real-world situations
Access project source code for continued learning and reference, empowering further exploration and refinement of skills
Why Learn Machine Learning for Predictive Maps in Python and Leaflet with us:
As one of the leading e-learning service providers globally, we strive to provide all our learners with the best e-learning experience possible that can make a real difference in their career progression.
Our Service for Machine Learning for Predictive Maps in Python and Leaflet:
Intensive Study Notes
Tutor Support
Customer Support
Teaching Assistant Assignment Help
QLS Endorsed Teaching Assistant Certificate
24/7 Learning Portal Access
Widely Compatible Teaching Assistant Study Materials
14 Days Refund Guarantee
Course Curriculum
Machine Learning for Predictive Maps in Python and Leaflet
Section 01: Introduction
Section 02: Setup and Installations
Section 03: Writing the Django Server-Side Code
Section 04: Writing the Application Front-end Code
Section 05: Machine Learning
Section 06: Automating the Machine Learning Pipeline
Section 07: Leaflet Programming
Section 08: Project Source Code
Assessment & Certification
Students must finish and turn in a thorough assignment covering all topics in our curriculum. Our expert tutor will review the assignment to ensure it meets Quality Learning Systems (QLS) standards. Once the assessment is successful and passes a quality check, students will be awarded a certificate for completing the Machine Learning for Predictive Maps in Python and Leaflet Course.
Endorsed Certificate of Achievement from the Quality Licence Scheme.
After the course, participants will receive an endorsed certificate as proof of successful completion. The learner may request a certificate if they complete all course assessments.
Endorsement:
After the course, participants will receive an endorsed certificate as proof of successful completion. The learner may request a certificate if they complete all course assessments.
 CPD 
150 CPD hours / points  Accredited by CPD Quality Standards
 Who is this course for? 
This Machine Learning for Predictive Maps in Python and Leaflet course is beneficial for a wide range of learners. It is especially suitable for the following learners:
Data scientists and analysts: Individuals seeking to broaden their expertise in predictive modelling and spatial analysis
Professionals in Geographic Information Systems (GIS): Looking to incorporate machine learning into their workflows for mapping and spatial analysis
Developers and Programmers: keen on integrating predictive mapping into their projects or applications.
Urban Planners and Environmental Researchers: Investigating predictive mapping in the fields of planning, sustainability, and environmental impact
Logistics and Supply Chain Analysts: Using predictive mapping to optimise routes and manage supply chains
Pupils studying geography or computer science: Interested in finding out how machine learning and cartography are combining
Decision-Making Roles Professionals: Looking for Tools and Insights to Support Strategic Decision-Making with Predictive Mapping
 Requirements 
No specific requirements exist for enrolling in the Machine Learning for Predictive Maps in Python and Leafletcourse. Therefore, learners do not need previous qualifications to sign up for this course.
 Career path 
'Machine Learning for Predictive Maps in Python and Leaflet' course is relevant to many trending professions as follows:
Data Scientist/Spatial Analysis. £40,000-£70,000 per year.
Python developer (with GIS expertise). £35,000-£60,000 per year.
Machine learning engineer. £50,000-£80,000 per year.
Urban Planner/Analyst. £30,000-£50,000 per year.
 Certificates 
Diploma in Machine Learning for Predictive Maps in Python and Leaflet at QLS
 Hard copy certificate - £75 

Kingston Open College

Learning & Development

Learning Support Assistant (LSA)

Managed Learning Programme (MLP)

Machine Learning (ML)

ELS (Effective Learning Service)

Learning To Learn

QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support

Machine Learning for Predictive Maps in Python and Leaflet - Level 5 (QLS Endorsed)

Java

Android Development

Mobile

Kotlin

Android

Learn new Android APIs such as RoomDatabase, ML Kit for face recognition, Cloud Firestore, Firebase, Maps, and the Android Studio IDE (integrated development environment)

The Comprehensive Android Developer Bootcamp

<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

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

The Complete Machine Learning Course with Python

<strong>The underlying patterns in your data hold vital insights; unearth them with cutting-edge clustering and classification techniques in R</strong>
        <p>This course is your complete guide to both supervised and unsupervised learning using R. This course covers all the main aspects of practical data science; if you take this course, there is no need to take other courses or buy books on R-based data science. In this age of big data, companies across the Globe use R to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised and supervised learning in R, you can give your company a competitive edge and take your career to the next level.

Over the course of research, the author realized that almost all the R data science courses and books out there do take account of the multidimensional nature of the topic. This course will give you a robust grounding in the main aspects of machine learning: clustering and classification. Unlike other R instructors, the author digs deep into R's machine learning features and give you a one-of-a-kind grounding in data science! You will go all the way from carrying out data reading & cleaning to machine learning, to finally implementing powerful machine learning algorithms and evaluating their performance via R.
The following topics will be covered: - 
&bull; A full introduction to the R Framework for data science 
&bull; Data structures and reading in R, including CSV, Excel, and HTML data 
&bull; How to pre-process and clean data by removing NAs/No data, visualization 
&bull; Machine learning, supervised learning, and unsupervised learning in R 
&bull; Model building and selection and much more!

The course will help you implement methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R-based data science in real life. After taking this course, you'll easily use data science packages such as Caret to work with real data in R. You'll even understand concepts such as unsupervised learning, dimension reduction, and supervised learning. 

All the code and supporting files for this course are available at - https://github.com/PacktPublishing/Clustering-and-Classification-with-Machine-Learning-in-R</p>
        <h2>What You Will Learn</h2><p>Read-in data into the R environment from different sources<br>Carry out basic data pre-processing and wrangling in R Studio<br>Implement unsupervised/clustering techniques such as K-means clustering<br>Implement dimensional reduction techniques (PCA) and feature selection<br>Implement supervised learning techniques/classification such as Random Forests<br>Evaluate model performance and learn the best practices for evaluating machine learning model accuracy</p>
        <h2>Audience</h2><p>This course is for students interested in getting started with data science applications in the R Studio environment. Students wishing to learn how to implement unsupervised learning on real data. Anyone with prior exposure to R who wants to get started with practical data science.</p>
        <h2>Approach</h2><p>Every video is packed with hands-on instructions and clear explanations. Real data has been used to demonstrate how to implement these techniques in real life, on your data.</p>
        <h2>Key Features</h2><p>Provides in-depth training in everything you need to know to get started with practical R data science * Jargon-free and suitable for people who have a non-mathematical background * In-depth coverage of the latest unsupervised and supervised techniques </p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/clustering-and-classification-with-machine-learning-in-r</p>
        <h2>About the Author</h2><h3>Minerva Singh</h3><p>Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a part-time Data Scientist. As part of her research, she must carry out extensive data analysis, including spatial data analysis. For this purpose, she prefers to use a combination of freeware tools: R, QGIS, and Python. She does most of her spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing, and analysis. She also holds an MPhil degree in Geography and Environment from Oxford University. She has honed her statistical and data analysis skills through several MOOCs, including The Analytics Edge and Statistical. In addition to spatial data analysis, she is also proficient in statistical analysis, machine learning, and data mining.</p>
        <h2>Course Outline</h2><h4>1. Introduction to the Course</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome to Clustering & Classification with Machine Learning in R</p><p style='margin: 0'>Introduction to the Course: Welcome to Clustering & Classification with Machine Learning in R</p></td></tr><tr><td style='padding: 15px;'><p>2. Installing R and R Studio</p><p style='margin: 0'>Introduction to the Course: Installing R and R Studio</p></td></tr></tbody></table><br><h4>2. Read in Data from Different Sources in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Read in CSV & Excel Data</p><p style='margin: 0'>Read in Data from Different Sources in R: Read in CSV & Excel Data</p></td></tr><tr><td style='padding: 15px;'><p>2. Read in Unzipped Folder</p><p style='margin: 0'>Read in Data from Different Sources in R: Read in Unzipped Folder</p></td></tr><tr><td style='padding: 15px;'><p>3. Read in Online CSV</p><p style='margin: 0'>Read in Data from Different Sources in R: Read in Online CSV</p></td></tr><tr><td style='padding: 15px;'><p>4. Read in Googlesheets</p><p style='margin: 0'>Read in Data from Different Sources in R: Read in Googlesheets</p></td></tr><tr><td style='padding: 15px;'><p>5. Read in Data from Online HTML Tables-Part 1</p><p style='margin: 0'>Read in Data from Different Sources in R: Read in Data from Online HTML Tables-Part 1</p></td></tr><tr><td style='padding: 15px;'><p>6. Read in Data from Online HTML Tables-Part 2</p><p style='margin: 0'>Read in Data from Different Sources in R: Read in Data from Online HTML Tables-Part 2</p></td></tr><tr><td style='padding: 15px;'><p>7. Read Data from a Database</p><p style='margin: 0'>Read in Data from Different Sources in R: Read Data from a Database</p></td></tr></tbody></table><br><h4>3. Data Pre-processing and Visualization</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Remove Missing Values</p><p style='margin: 0'>Data Pre-processing and Visualization: Remove Missing Values</p></td></tr><tr><td style='padding: 15px;'><p>2. More Data Cleaning</p><p style='margin: 0'>Data Pre-processing and Visualization: More Data Cleaning</p></td></tr><tr><td style='padding: 15px;'><p>3. Introduction to dplyr for Data Summarizing-Part 1</p><p style='margin: 0'>Data Pre-processing and Visualization: Introduction to dplyr for Data Summarizing-Part 1</p></td></tr><tr><td style='padding: 15px;'><p>4. Introduction to dplyr for Data Summarizing-Part 2</p><p style='margin: 0'>Data Pre-processing and Visualization: Introduction to dplyr for Data Summarizing-Part 2</p></td></tr><tr><td style='padding: 15px;'><p>5. Exploratory Data Analysis (EDA): Basic Visualizations with R</p><p style='margin: 0'>Data Pre-processing and Visualization: Exploratory Data Analysis (EDA): Basic Visualizations with R</p></td></tr><tr><td style='padding: 15px;'><p>6. More Exploratory Data Analysis with xda</p><p style='margin: 0'>Data Pre-processing and Visualization: More Exploratory Data Analysis with xda</p></td></tr><tr><td style='padding: 15px;'><p>7. Data Exploration & Visualization With dplyr & ggplot2</p><p style='margin: 0'>Data Pre-processing and Visualization: Data Exploration & Visualization With dplyr & ggplot2</p></td></tr><tr><td style='padding: 15px;'><p>8. Associations Between Quantitative Variables- Theory</p><p style='margin: 0'>Data Pre-processing and Visualization: Associations Between Quantitative Variables- Theory</p></td></tr><tr><td style='padding: 15px;'><p>9. Testing for Correlation</p><p style='margin: 0'>Data Pre-processing and Visualization: Testing for Correlation</p></td></tr><tr><td style='padding: 15px;'><p>10. Evaluate the Relation Between Nominal Variables</p><p style='margin: 0'>Data Pre-processing and Visualization: Evaluate the Relation Between Nominal Variables</p></td></tr><tr><td style='padding: 15px;'><p>11. Cramer's V for Examining the Strength of Association Between Nominal Variable</p><p style='margin: 0'>Data Pre-processing and Visualization: Cramer's V for Examining the Strength of Association Between Nominal Variable</p></td></tr></tbody></table><br><h4>4. Machine Learning for Data Science</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. How is Machine Learning Different from Statistical Data Analysis?</p><p style='margin: 0'>Machine Learning for Data Science: How is Machine Learning Different from Statistical Data Analysis?</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Machine Learning (ML) About? Some Theoretical Pointers</p><p style='margin: 0'>Machine Learning for Data Science: What is Machine Learning (ML) About? Some Theoretical Pointers</p></td></tr></tbody></table><br><h4>5. Unsupervised Learning in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. K-Means Clustering</p><p style='margin: 0'>Unsupervised Learning in R: K-Means Clustering</p></td></tr><tr><td style='padding: 15px;'><p>2. Other Ways of Selecting Cluster Numbers</p><p style='margin: 0'>Unsupervised Learning in R: Other Ways of Selecting Cluster Numbers</p></td></tr><tr><td style='padding: 15px;'><p>3. Fuzzy K-Means Clustering</p><p style='margin: 0'>Unsupervised Learning in R: Fuzzy K-Means Clustering</p></td></tr><tr><td style='padding: 15px;'><p>4. Weighted k-means</p><p style='margin: 0'>Unsupervised Learning in R: Weighted k-means</p></td></tr><tr><td style='padding: 15px;'><p>5. Partitioning Around Meloids (PAM)</p><p style='margin: 0'>Unsupervised Learning in R: Partitioning Around Meloids (PAM)</p></td></tr><tr><td style='padding: 15px;'><p>6. Hierarchical Clustering in R</p><p style='margin: 0'>Unsupervised Learning in R: Hierarchical Clustering in R</p></td></tr><tr><td style='padding: 15px;'><p>7. Expectation-Maximization (EM) in R</p><p style='margin: 0'>Unsupervised Learning in R: Expectation-Maximization (EM) in R</p></td></tr><tr><td style='padding: 15px;'><p>8. DBSCAN Clustering in R</p><p style='margin: 0'>Unsupervised Learning in R: DBSCAN Clustering in R</p></td></tr><tr><td style='padding: 15px;'><p>9. Cluster a Mixed Dataset</p><p style='margin: 0'>Unsupervised Learning in R: Cluster a Mixed Dataset</p></td></tr><tr><td style='padding: 15px;'><p>10. Should We Even Do Clustering?</p><p style='margin: 0'>Unsupervised Learning in R: Should We Even Do Clustering?</p></td></tr><tr><td style='padding: 15px;'><p>11. Assess Clustering Performance</p><p style='margin: 0'>Unsupervised Learning in R: Assess Clustering Performance</p></td></tr><tr><td style='padding: 15px;'><p>12. Which Clustering Algorithm to Choose?</p><p style='margin: 0'>Unsupervised Learning in R: Which Clustering Algorithm to Choose?</p></td></tr></tbody></table><br><h4>6. Feature/Dimension Reduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Dimension Reduction-theory</p><p style='margin: 0'>Feature/Dimension Reduction: Dimension Reduction-theory</p></td></tr><tr><td style='padding: 15px;'><p>2. Principal Component Analysis (PCA)</p><p style='margin: 0'>Feature/Dimension Reduction: Principal Component Analysis (PCA)</p></td></tr><tr><td style='padding: 15px;'><p>3. More on PCA</p><p style='margin: 0'>Feature/Dimension Reduction: More on PCA</p></td></tr><tr><td style='padding: 15px;'><p>4. Multidimensional Scaling</p><p style='margin: 0'>Feature/Dimension Reduction: Multidimensional Scaling</p></td></tr><tr><td style='padding: 15px;'><p>5. Singular Value Decomposition (SVD)</p><p style='margin: 0'>Feature/Dimension Reduction: Singular Value Decomposition (SVD)</p></td></tr></tbody></table><br><h4>7. Feature Selection to Select the Most Relevant Predictors</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Removing Highly Correlated Predictor Variables</p><p style='margin: 0'>Feature Selection to Select the Most Relevant Predictors: Removing Highly Correlated Predictor Variables</p></td></tr><tr><td style='padding: 15px;'><p>2. Variable Selection Using LASSO Regression</p><p style='margin: 0'>Feature Selection to Select the Most Relevant Predictors: Variable Selection Using LASSO Regression</p></td></tr><tr><td style='padding: 15px;'><p>3. Variable Selection with FSelector</p><p style='margin: 0'>Feature Selection to Select the Most Relevant Predictors: Variable Selection with FSelector</p></td></tr><tr><td style='padding: 15px;'><p>4. Boruta Analysis for Feature Selection</p><p style='margin: 0'>Feature Selection to Select the Most Relevant Predictors: Boruta Analysis for Feature Selection</p></td></tr></tbody></table><br><h4>8. Supervised Learning Theory</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Some Basic Supervised Learning Concepts</p><p style='margin: 0'>Supervised Learning Theory: Some Basic Supervised Learning Concepts</p></td></tr><tr><td style='padding: 15px;'><p>2. Pre-processing for Supervised Learning</p><p style='margin: 0'>Supervised Learning Theory: Pre-processing for Supervised Learning</p></td></tr></tbody></table><br><h4>9. Supervised Learning: Classification</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What are GLMs?</p><p style='margin: 0'>Supervised Learning: Classification: What are GLMs?</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic Regression Models as Binary Classifiers</p><p style='margin: 0'>Supervised Learning: Classification: Logistic Regression Models as Binary Classifiers</p></td></tr><tr><td style='padding: 15px;'><p>3. Binary Classifier with PCA</p><p style='margin: 0'>Supervised Learning: Classification: Binary Classifier with PCA</p></td></tr><tr><td style='padding: 15px;'><p>4. Some Pointers on Evaluating Accuracy</p><p style='margin: 0'>Supervised Learning: Classification: Some Pointers on Evaluating Accuracy</p></td></tr><tr><td style='padding: 15px;'><p>5. Obtain Binary Classification Accuracy Metrics</p><p style='margin: 0'>Supervised Learning: Classification: Obtain Binary Classification Accuracy Metrics</p></td></tr><tr><td style='padding: 15px;'><p>6. More on Binary Accuracy Measures</p><p style='margin: 0'>Supervised Learning: Classification: More on Binary Accuracy Measures</p></td></tr><tr><td style='padding: 15px;'><p>7. Linear Discriminant Analysis</p><p style='margin: 0'>Supervised Learning: Classification: Linear Discriminant Analysis</p></td></tr><tr><td style='padding: 15px;'><p>8. Our Multi-class Classification Problem</p><p style='margin: 0'>Supervised Learning: Classification: Our Multi-class Classification Problem</p></td></tr><tr><td style='padding: 15px;'><p>9. Classification Trees</p><p style='margin: 0'>Supervised Learning: Classification: Classification Trees</p></td></tr><tr><td style='padding: 15px;'><p>10. More on Classification Tree Visualization</p><p style='margin: 0'>Supervised Learning: Classification: More on Classification Tree Visualization</p></td></tr><tr><td style='padding: 15px;'><p>11. Classification with Party Package</p><p style='margin: 0'>Supervised Learning: Classification: Classification with Party Package</p></td></tr><tr><td style='padding: 15px;'><p>12. Decision Trees</p><p style='margin: 0'>Supervised Learning: Classification: Decision Trees</p></td></tr><tr><td style='padding: 15px;'><p>13. Random Forest (RF) Classification</p><p style='margin: 0'>Supervised Learning: Classification: Random Forest (RF) Classification</p></td></tr><tr><td style='padding: 15px;'><p>14. Examine Individual Variable Importance for Random Forests</p><p style='margin: 0'>Supervised Learning: Classification: Examine Individual Variable Importance for Random Forests</p></td></tr><tr><td style='padding: 15px;'><p>15. GBM Classification</p><p style='margin: 0'>Supervised Learning: Classification: GBM Classification</p></td></tr><tr><td style='padding: 15px;'><p>16. Support Vector Machines (SVM) for Classification</p><p style='margin: 0'>Supervised Learning: Classification: Support Vector Machines (SVM) for Classification</p></td></tr><tr><td style='padding: 15px;'><p>17. More SVM for Classification</p><p style='margin: 0'>Supervised Learning: Classification: More SVM for Classification</p></td></tr><tr><td style='padding: 15px;'><p>18. Variable Importance in SVM Modelling with rminer</p><p style='margin: 0'>Supervised Learning: Classification: Variable Importance in SVM Modelling with rminer</p></td></tr></tbody></table><br><h4>10. Additional Lectures</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Fuzzy C-Means Clustering</p><p style='margin: 0'>Additional Lectures: Fuzzy C-Means Clustering</p></td></tr></tbody></table>

The underlying patterns in your data hold vital insights; unearth them with cutting-edge clustering and classification techniques in R
This course is your complete guide to both supervised and unsupervised learning using R. This course covers all the main aspects of practical data science; if you take this course, there is no need to take other courses or buy books on R-based data science. In this age of big data, companies across the Globe use R to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised and supervised learning in R, you can give your company a competitive edge and take your career to the next level. Over the course of research, the author realized that almost all the R data science courses and books out there do take account of the multidimensional nature of the topic. This course will give you a robust grounding in the main aspects of machine learning: clustering and classification. Unlike other R instructors, the author digs deep into R's machine learning features and give you a one-of-a-kind grounding in data science! You will go all the way from carrying out data reading & cleaning to machine learning, to finally implementing powerful machine learning algorithms and evaluating their performance via R.
The following topics will be covered: - • A full introduction to the R Framework for data science • Data structures and reading in R, including CSV, Excel, and HTML data • How to pre-process and clean data by removing NAs/No data, visualization • Machine learning, supervised learning, and unsupervised learning in R • Model building and selection and much more! The course will help you implement methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R-based data science in real life. After taking this course, you'll easily use data science packages such as Caret to work with real data in R. You'll even understand concepts such as unsupervised learning, dimension reduction, and supervised learning. All the code and supporting files for this course are available at - https://github.com/PacktPublishing/Clustering-and-Classification-with-Machine-Learning-in-R
What You Will Learn
Read-in data into the R environment from different sources
Carry out basic data pre-processing and wrangling in R Studio
Implement unsupervised/clustering techniques such as K-means clustering
Implement dimensional reduction techniques (PCA) and feature selection
Implement supervised learning techniques/classification such as Random Forests
Evaluate model performance and learn the best practices for evaluating machine learning model accuracy
Audience
This course is for students interested in getting started with data science applications in the R Studio environment. Students wishing to learn how to implement unsupervised learning on real data. Anyone with prior exposure to R who wants to get started with practical data science.
Approach
Every video is packed with hands-on instructions and clear explanations. Real data has been used to demonstrate how to implement these techniques in real life, on your data.
Key Features
Provides in-depth training in everything you need to know to get started with practical R data science * Jargon-free and suitable for people who have a non-mathematical background * In-depth coverage of the latest unsupervised and supervised techniques 
Github Repo
https://github.com/packtpublishing/clustering-and-classification-with-machine-learning-in-r
About the Author
Minerva Singh
Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a part-time Data Scientist. As part of her research, she must carry out extensive data analysis, including spatial data analysis. For this purpose, she prefers to use a combination of freeware tools: R, QGIS, and Python. She does most of her spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing, and analysis. She also holds an MPhil degree in Geography and Environment from Oxford University. She has honed her statistical and data analysis skills through several MOOCs, including The Analytics Edge and Statistical. In addition to spatial data analysis, she is also proficient in statistical analysis, machine learning, and data mining.
Course Outline
1. Introduction to the Course

1. Welcome to Clustering & Classification with Machine Learning in R
Introduction to the Course: Welcome to Clustering & Classification with Machine Learning in R
2. Installing R and R Studio
Introduction to the Course: Installing R and R Studio
2. Read in Data from Different Sources in R

1. Read in CSV & Excel Data
Read in Data from Different Sources in R: Read in CSV & Excel Data
2. Read in Unzipped Folder
Read in Data from Different Sources in R: Read in Unzipped Folder
3. Read in Online CSV
Read in Data from Different Sources in R: Read in Online CSV
4. Read in Googlesheets
Read in Data from Different Sources in R: Read in Googlesheets
5. Read in Data from Online HTML Tables-Part 1
Read in Data from Different Sources in R: Read in Data from Online HTML Tables-Part 1
6. Read in Data from Online HTML Tables-Part 2
Read in Data from Different Sources in R: Read in Data from Online HTML Tables-Part 2
7. Read Data from a Database
Read in Data from Different Sources in R: Read Data from a Database
3. Data Pre-processing and Visualization

1. Remove Missing Values
Data Pre-processing and Visualization: Remove Missing Values
2. More Data Cleaning
Data Pre-processing and Visualization: More Data Cleaning
3. Introduction to dplyr for Data Summarizing-Part 1
Data Pre-processing and Visualization: Introduction to dplyr for Data Summarizing-Part 1
4. Introduction to dplyr for Data Summarizing-Part 2
Data Pre-processing and Visualization: Introduction to dplyr for Data Summarizing-Part 2
5. Exploratory Data Analysis (EDA): Basic Visualizations with R
Data Pre-processing and Visualization: Exploratory Data Analysis (EDA): Basic Visualizations with R
6. More Exploratory Data Analysis with xda
Data Pre-processing and Visualization: More Exploratory Data Analysis with xda
7. Data Exploration & Visualization With dplyr & ggplot2
Data Pre-processing and Visualization: Data Exploration & Visualization With dplyr & ggplot2
8. Associations Between Quantitative Variables- Theory
Data Pre-processing and Visualization: Associations Between Quantitative Variables- Theory
9. Testing for Correlation
Data Pre-processing and Visualization: Testing for Correlation
10. Evaluate the Relation Between Nominal Variables
Data Pre-processing and Visualization: Evaluate the Relation Between Nominal Variables
11. Cramer's V for Examining the Strength of Association Between Nominal Variable
Data Pre-processing and Visualization: Cramer's V for Examining the Strength of Association Between Nominal Variable
4. Machine Learning for Data Science

1. How is Machine Learning Different from Statistical Data Analysis?
Machine Learning for Data Science: How is Machine Learning Different from Statistical Data Analysis?
2. What is Machine Learning (ML) About? Some Theoretical Pointers
Machine Learning for Data Science: What is Machine Learning (ML) About? Some Theoretical Pointers
5. Unsupervised Learning in R

1. K-Means Clustering
Unsupervised Learning in R: K-Means Clustering
2. Other Ways of Selecting Cluster Numbers
Unsupervised Learning in R: Other Ways of Selecting Cluster Numbers
3. Fuzzy K-Means Clustering
Unsupervised Learning in R: Fuzzy K-Means Clustering
4. Weighted k-means
Unsupervised Learning in R: Weighted k-means
5. Partitioning Around Meloids (PAM)
Unsupervised Learning in R: Partitioning Around Meloids (PAM)
6. Hierarchical Clustering in R
Unsupervised Learning in R: Hierarchical Clustering in R
7. Expectation-Maximization (EM) in R
Unsupervised Learning in R: Expectation-Maximization (EM) in R
8. DBSCAN Clustering in R
Unsupervised Learning in R: DBSCAN Clustering in R
9. Cluster a Mixed Dataset
Unsupervised Learning in R: Cluster a Mixed Dataset
10. Should We Even Do Clustering?
Unsupervised Learning in R: Should We Even Do Clustering?
11. Assess Clustering Performance
Unsupervised Learning in R: Assess Clustering Performance
12. Which Clustering Algorithm to Choose?
Unsupervised Learning in R: Which Clustering Algorithm to Choose?
6. Feature/Dimension Reduction

1. Dimension Reduction-theory
Feature/Dimension Reduction: Dimension Reduction-theory
2. Principal Component Analysis (PCA)
Feature/Dimension Reduction: Principal Component Analysis (PCA)
3. More on PCA
Feature/Dimension Reduction: More on PCA
4. Multidimensional Scaling
Feature/Dimension Reduction: Multidimensional Scaling
5. Singular Value Decomposition (SVD)
Feature/Dimension Reduction: Singular Value Decomposition (SVD)
7. Feature Selection to Select the Most Relevant Predictors

1. Removing Highly Correlated Predictor Variables
Feature Selection to Select the Most Relevant Predictors: Removing Highly Correlated Predictor Variables
2. Variable Selection Using LASSO Regression
Feature Selection to Select the Most Relevant Predictors: Variable Selection Using LASSO Regression
3. Variable Selection with FSelector
Feature Selection to Select the Most Relevant Predictors: Variable Selection with FSelector
4. Boruta Analysis for Feature Selection
Feature Selection to Select the Most Relevant Predictors: Boruta Analysis for Feature Selection
8. Supervised Learning Theory

1. Some Basic Supervised Learning Concepts
Supervised Learning Theory: Some Basic Supervised Learning Concepts
2. Pre-processing for Supervised Learning
Supervised Learning Theory: Pre-processing for Supervised Learning
9. Supervised Learning: Classification

1. What are GLMs?
Supervised Learning: Classification: What are GLMs?
2. Logistic Regression Models as Binary Classifiers
Supervised Learning: Classification: Logistic Regression Models as Binary Classifiers
3. Binary Classifier with PCA
Supervised Learning: Classification: Binary Classifier with PCA
4. Some Pointers on Evaluating Accuracy
Supervised Learning: Classification: Some Pointers on Evaluating Accuracy
5. Obtain Binary Classification Accuracy Metrics
Supervised Learning: Classification: Obtain Binary Classification Accuracy Metrics
6. More on Binary Accuracy Measures
Supervised Learning: Classification: More on Binary Accuracy Measures
7. Linear Discriminant Analysis
Supervised Learning: Classification: Linear Discriminant Analysis
8. Our Multi-class Classification Problem
Supervised Learning: Classification: Our Multi-class Classification Problem
9. Classification Trees
Supervised Learning: Classification: Classification Trees
10. More on Classification Tree Visualization
Supervised Learning: Classification: More on Classification Tree Visualization
11. Classification with Party Package
Supervised Learning: Classification: Classification with Party Package
12. Decision Trees
Supervised Learning: Classification: Decision Trees
13. Random Forest (RF) Classification
Supervised Learning: Classification: Random Forest (RF) Classification
14. Examine Individual Variable Importance for Random Forests
Supervised Learning: Classification: Examine Individual Variable Importance for Random Forests
15. GBM Classification
Supervised Learning: Classification: GBM Classification
16. Support Vector Machines (SVM) for Classification
Supervised Learning: Classification: Support Vector Machines (SVM) for Classification
17. More SVM for Classification
Supervised Learning: Classification: More SVM for Classification
18. Variable Importance in SVM Modelling with rminer
Supervised Learning: Classification: Variable Importance in SVM Modelling with rminer
10. Additional Lectures

1. Fuzzy C-Means Clustering
Additional Lectures: Fuzzy C-Means Clustering

HTML

The underlying patterns in your data hold vital insights; unearth them with cutting-edge clustering and classification techniques in R

Clustering and Classification with Machine Learning in R

<strong>The course is crafted to reflect the most in-demand workplace skills. It will help you understand all the essential concepts and methodologies with regards to PySpark. This course provides a detailed compilation of all the basics, which will motivate you to make quick progress and experience much more than what you have learned.</strong>
        <p>The hottest buzzwords in the Big Data analytics industry are Python and Apache Spark. PySpark supports the collaboration of Python and Apache Spark. In this course, you'll start right from the basics and proceed to the advanced levels of data analysis. From cleaning data to building features and implementing machine learning (ML) models, you'll learn how to execute end-to-end workflows using PySpark.

Right through the course, you'll be using PySpark to perform data analysis. You'll explore Spark RDDs, Dataframes, and a bit of Spark SQL queries. Also, you'll explore the transformations and actions that can be performed on the data using Spark RDDs and Dataframes. You'll also explore the ecosystem of Spark and Hadoop and their underlying architecture. You'll use the Databricks environment to run the Spark scripts and explore it as well.

Finally, you'll have a taste of Spark with AWS cloud. You'll see how we can leverage AWS storages, databases, computations, and how Spark can communicate with different AWS services and get its required data.

By the end of this course, you'll be able to understand and implement the concepts of PySpark and AWS to solve real-world problems.

The code bundles are available here: https://github.com/PacktPublishing/PySpark-and-AWS-Master-Big-Data-with-PySpark-and-AWS</p>
        <h2>What You Will Learn</h2><p>Learn the importance of Big Data<br>Explore the Spark and Hadoop architecture and ecosystem<br>Learn about PySpark Dataframes and PySpark DataFrames actions<br>Use PySpark DataFrames transformations<br>Apply collaborative filtering to develop a recommendation system using ALS models</p>
        <h2>Audience</h2><p>This course requires python programming experience as a prerequisite.</p>
        <h2>Approach</h2><p>In this learning-by-doing course, every theoretical explanation is followed by practical implementation. At the end of each concept, homework/tasks/activities/quizzes along with solutions are assigned. This is to evaluate and promote your learning based on the previous concepts and methods you have learned. Most of these activities will be coding-based, as the aim is to get you up and running with implementations.</p>
        <h2>Key Features</h2><p>Relate the concepts and practical aspects of Spark and AWS with real-world problems * Implement any project that requires PySpark knowledge from scratch * Know the theory and practical aspects of PySpark and AWS</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/PySpark-and-AWS-Master-Big-Data-with-PySpark-and-AWS</p>
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are a group of 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</h4><p></p><br><h4>2. Introduction to Hadoop, Spark EcoSystems and Architectures</h4><p></p><br><h4>3. Spark RDDs</h4><p></p><br><h4>4. Spark DFs</h4><p></p><br><h4>5. Collaborative filtering</h4><p></p><br><h4>6. Spark Streaming</h4><p></p><br><h4>7. ETL Pipeline</h4><p></p><br><h4>8. Project - Change Data Capture / Replication On Going</h4><p></p>

The course is crafted to reflect the most in-demand workplace skills. It will help you understand all the essential concepts and methodologies with regards to PySpark. This course provides a detailed compilation of all the basics, which will motivate you to make quick progress and experience much more than what you have learned.
The hottest buzzwords in the Big Data analytics industry are Python and Apache Spark. PySpark supports the collaboration of Python and Apache Spark. In this course, you'll start right from the basics and proceed to the advanced levels of data analysis. From cleaning data to building features and implementing machine learning (ML) models, you'll learn how to execute end-to-end workflows using PySpark. Right through the course, you'll be using PySpark to perform data analysis. You'll explore Spark RDDs, Dataframes, and a bit of Spark SQL queries. Also, you'll explore the transformations and actions that can be performed on the data using Spark RDDs and Dataframes. You'll also explore the ecosystem of Spark and Hadoop and their underlying architecture. You'll use the Databricks environment to run the Spark scripts and explore it as well. Finally, you'll have a taste of Spark with AWS cloud. You'll see how we can leverage AWS storages, databases, computations, and how Spark can communicate with different AWS services and get its required data. By the end of this course, you'll be able to understand and implement the concepts of PySpark and AWS to solve real-world problems. The code bundles are available here: https://github.com/PacktPublishing/PySpark-and-AWS-Master-Big-Data-with-PySpark-and-AWS
What You Will Learn
Learn the importance of Big Data
Explore the Spark and Hadoop architecture and ecosystem
Learn about PySpark Dataframes and PySpark DataFrames actions
Use PySpark DataFrames transformations
Apply collaborative filtering to develop a recommendation system using ALS models
Audience
This course requires python programming experience as a prerequisite.
Approach
In this learning-by-doing course, every theoretical explanation is followed by practical implementation. At the end of each concept, homework/tasks/activities/quizzes along with solutions are assigned. This is to evaluate and promote your learning based on the previous concepts and methods you have learned. Most of these activities will be coding-based, as the aim is to get you up and running with implementations.
Key Features
Relate the concepts and practical aspects of Spark and AWS with real-world problems * Implement any project that requires PySpark knowledge from scratch * Know the theory and practical aspects of PySpark and AWS
Github Repo
https://github.com/PacktPublishing/PySpark-and-AWS-Master-Big-Data-with-PySpark-and-AWS
About the Author
AI Sciences
AI Sciences are a group of 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

2. Introduction to Hadoop, Spark EcoSystems and Architectures

3. Spark RDDs

4. Spark DFs

5. Collaborative filtering

6. Spark Streaming

7. ETL Pipeline

8. Project - Change Data Capture / Replication On Going


Apache Spark

Big Data

The course is crafted to reflect the most in-demand workplace skills. It will help you understand all the essential concepts and methodologies with regards to PySpark. This course provides a detailed compilation of all the basics, which will motivate you to make quick progress and experience much more than what you have learned.

PySpark and AWS: Master Big Data with PySpark and AWS

<p><strong>&gt;&gt; 12-Hour Knowledge Knockdown! Prices Reduced Like Never Before &lt;&lt;</strong></p><p>In the era of big data, the demand for skilled data science professionals has skyrocketed in the UK. According to a recent report, the data science job market in the UK is expected to grow by over <strong>25% by 2026</strong>. Aside from that, Candidates with data science skills have a <strong>96% </strong>employment rate and can earn on average <strong>£40,000 per year.</strong> Our Complete Data Science bundle is about to take you on a tour starting from the beginning.</p><p><strong>This CCTV Operator Training Bundle Contains 4</strong><strong> of Our Premium Courses for One Discounted Price:</strong></p><ul><li><strong> Course 01: </strong>Complete Data Science</li><li><strong> Course 02:</strong> Data Science with Python</li><li><strong> Course 03: </strong>Information Management</li><li><strong> Course 04:</strong> GDPR Data Protection</li></ul><p>Take our Complete Data Science Bundle to learn how to maximise your potential and climb your chosen professional ladder. By participating in these popular courses, you can learn the fundamentals of Python. Discover Python data types. Loops, list comprehension, functions, lambda expressions, maps, and filters should all be taught. Learn about the numpy. Indexing, slicing, broadcasting, and boolean masking are all covered in our Complete Data Science course. Recognise arithmetic and universal functions. Discover everything there is to know about pandas. Learn how to use Python to become an expert in data analysis and visualisation.</p><p><strong>Learning Outcomes of Data Science</strong></p><ul><li>Develop a comprehensive understanding of the data science lifecycle.</li><li>Master data analysis techniques and Python programming for data manipulation.</li><li>Gain proficiency in information management and data organization strategies.</li><li>Understand data protection regulations, including GDPR, and their implications.</li><li>Learn to build robust data-driven applications and predictive models.</li><li>Enhance data visualization skills for effective communication of insights.</li></ul><p>Invest in your future by enrolling today and gain a competitive edge in the rapidly evolving field of data science.</p><p><strong>Why Choose Our Data Science </strong>b<strong>und;e?</strong></p><ul><li>Get a <strong>Free CPD Accredited Certificate</strong> upon completion of Data Science</li><li>Get a <strong>free student ID card</strong> with Data Science Training</li><li>The Data Science is affordable and simple to understand</li><li><strong>Lifetime access</strong> to the Data Science course materials</li><li>The Data Science comes with 24/7 tutor support</li></ul> <p>Start your learning journey straightaway!</p><p><strong>*** Course Curriculum ***</strong></p><p><strong> Course 01: Complete Data Science</strong></p><ul><li>Welcome, Course Introduction &amp; overview, and Environment set-up</li><li>Python Essentials</li><li>Python for Data Analysis using NumPy</li><li>Python for Data Analysis using Pandas</li><li>Python for Data Visualization using matplotlib</li><li>Python for Data Visualization using Seaborn</li><li>Python for Data Visualization using pandas</li><li>Python for interactive &amp; geographical plotting using Plotly and Cufflinks</li><li>Capstone Project - Python for Data Analysis &amp; Visualization</li><li>Python for Machine Learning (ML) - scikit-learn - Linear Regression Model</li><li>Python for Machine Learning - scikit-learn - Logistic Regression Model</li><li>Python for Machine Learning - scikit-learn - K Nearest Neighbors</li><li>Python for Machine Learning - scikit-learn - Decision Tree and Random Forests</li><li>Python for Machine Learning - scikit-learn - Support Vector Machines (SVMs)</li><li>Python for Machine Learning - scikit-learn - K Means Clustering</li><li>Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA)</li><li>Recommender Systems - (Additional Topic)</li><li>Natural Language Processing (NLP) - NLTK - (Additional Topic)</li></ul><p><strong> Course 02: Data Science with Python</strong></p><ul><li>Unit 01: Introduction To Python Data Science</li><li>Unit 02: Data Cleaning Packages</li><li>Unit 03: Data Visualization Packages</li></ul><p><strong> Course 03: Information Management</strong></p><ul><li>Module 01: Introduction To Information Management</li><li>Module 02: Information Management Strategy</li><li>Module 03: Databases And Information Management</li><li>Module 04: Management Information Systems (MIS)</li><li>Module 05: Auditing Information Systems</li><li>Module 06: Ethical And Social Issues And Data Protection</li></ul><p><strong> Course 04: GDPR Data Protection</strong></p><ul><li>Module 01: Basics Of GDPR</li><li>Module 02: Principles Of GDPR</li><li>Module 03: Legal Foundation For Processing</li><li>Module 04: Rights Of Individuals</li><li>Module 05: Accountability And Governance</li><li>Module 06: Data Protection Officer</li><li>Module 07: Security Of Data</li><li>Module 08: Personal Data Breaches</li><li>Module 09: International Data Transfers After The Brexit</li><li>Module 10: Exemptions - Part One</li></ul><p>and much more...</p><p><strong>How will I get my Certificate?</strong></p><p>After successfully completing the course, you will be able to order your Certificates as proof of your achievement.</p><ul><li><strong>PDF Certificate: Free</strong> (Previously it was £12.99*4 = £51)</li><li><strong>CPD Hard Copy Certificate: </strong><strong>£29.99 </strong>(Each)</li></ul> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        40 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div> <h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>This Data Science bundle is suitable for everyone. This bundle is ideal for:</p><ul><li>Data scientist</li><li>Data analyst-statistician</li><li>CSE Students</li><li>Interns</li><li>App Developer</li><li>Coders'</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>You will not need any prior background or expertise to enrol in this Data Science bundle.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This Data Science Training bundle will allow you to kickstart or take your career in the related sector to the next stage.</p><ul><li>Data Analyst</li><li>Data Scientist</li><li>Business Analyst</li><li>Marketing Analyst</li><li>Data Engineer</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited Digital Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
            <div><p>Upon passing the Course, you need to order a <strong>Digital Certificate</strong> for each of the courses inside this bundle as proof of your new skills that are accredited by <strong>CPD QS </strong>for Free.</p></div>
        <h4>CPD Accredited Hard Copy Certificate</h4>
        <p>
            Hard copy certificate -
            £29
        </p>
            <div><p><em>Please note that International students have to pay an additional £10 as a shipment fee.</em><em><br /></em></p></div>

                        </div>

>> 12-Hour Knowledge Knockdown! Prices Reduced Like Never Before <<
In the era of big data, the demand for skilled data science professionals has skyrocketed in the UK. According to a recent report, the data science job market in the UK is expected to grow by over 25% by 2026. Aside from that, Candidates with data science skills have a 96% employment rate and can earn on average £40,000 per year. Our Complete Data Science bundle is about to take you on a tour starting from the beginning.
This CCTV Operator Training Bundle Contains 4 of Our Premium Courses for One Discounted Price:
Course 01: Complete Data Science
Course 02: Data Science with Python
Course 03: Information Management
Course 04: GDPR Data Protection
Take our Complete Data Science Bundle to learn how to maximise your potential and climb your chosen professional ladder. By participating in these popular courses, you can learn the fundamentals of Python. Discover Python data types. Loops, list comprehension, functions, lambda expressions, maps, and filters should all be taught. Learn about the numpy. Indexing, slicing, broadcasting, and boolean masking are all covered in our Complete Data Science course. Recognise arithmetic and universal functions. Discover everything there is to know about pandas. Learn how to use Python to become an expert in data analysis and visualisation.
Learning Outcomes of Data Science
Develop a comprehensive understanding of the data science lifecycle.
Master data analysis techniques and Python programming for data manipulation.
Gain proficiency in information management and data organization strategies.
Understand data protection regulations, including GDPR, and their implications.
Learn to build robust data-driven applications and predictive models.
Enhance data visualization skills for effective communication of insights.
Invest in your future by enrolling today and gain a competitive edge in the rapidly evolving field of data science.
Why Choose Our Data Science bund;e?
Get a Free CPD Accredited Certificate upon completion of Data Science
Get a free student ID card with Data Science Training
The Data Science is affordable and simple to understand
Lifetime access to the Data Science course materials
The Data Science comes with 24/7 tutor support
Start your learning journey straightaway!
*** Course Curriculum ***
 Course 01: Complete Data Science
Welcome, Course Introduction & overview, and Environment set-up
Python Essentials
Python for Data Analysis using NumPy
Python for Data Analysis using Pandas
Python for Data Visualization using matplotlib
Python for Data Visualization using Seaborn
Python for Data Visualization using pandas
Python for interactive & geographical plotting using Plotly and Cufflinks
Capstone Project - Python for Data Analysis & Visualization
Python for Machine Learning (ML) - scikit-learn - Linear Regression Model
Python for Machine Learning - scikit-learn - Logistic Regression Model
Python for Machine Learning - scikit-learn - K Nearest Neighbors
Python for Machine Learning - scikit-learn - Decision Tree and Random Forests
Python for Machine Learning - scikit-learn - Support Vector Machines (SVMs)
Python for Machine Learning - scikit-learn - K Means Clustering
Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA)
Recommender Systems - (Additional Topic)
Natural Language Processing (NLP) - NLTK - (Additional Topic)
 Course 02: Data Science with Python
Unit 01: Introduction To Python Data Science
Unit 02: Data Cleaning Packages
Unit 03: Data Visualization Packages
 Course 03: Information Management
Module 01: Introduction To Information Management
Module 02: Information Management Strategy
Module 03: Databases And Information Management
Module 04: Management Information Systems (MIS)
Module 05: Auditing Information Systems
Module 06: Ethical And Social Issues And Data Protection
 Course 04: GDPR Data Protection
Module 01: Basics Of GDPR
Module 02: Principles Of GDPR
Module 03: Legal Foundation For Processing
Module 04: Rights Of Individuals
Module 05: Accountability And Governance
Module 06: Data Protection Officer
Module 07: Security Of Data
Module 08: Personal Data Breaches
Module 09: International Data Transfers After The Brexit
Module 10: Exemptions - Part One
and much more...
How will I get my Certificate?
After successfully completing the course, you will be able to order your Certificates as proof of your achievement.
PDF Certificate: Free (Previously it was £12.99*4 = £51)
CPD Hard Copy Certificate: £29.99 (Each)
 CPD 
40 CPD hours / points  Accredited by CPD Quality Standards
 Who is this course for? 
This Data Science bundle is suitable for everyone. This bundle is ideal for:
Data scientist
Data analyst-statistician
CSE Students
Interns
App Developer
Coders'
 Requirements 
You will not need any prior background or expertise to enrol in this Data Science bundle.
 Career path 
This Data Science Training bundle will allow you to kickstart or take your career in the related sector to the next stage.
Data Analyst
Data Scientist
Business Analyst
Marketing Analyst
Data Engineer
 Certificates 
CPD Accredited Digital Certificate
 Digital certificate - Included 
Upon passing the Course, you need to order a Digital Certificate for each of the courses inside this bundle as proof of your new skills that are accredited by CPD QS for Free.
CPD Accredited Hard Copy Certificate
 Hard copy certificate - £29 
Please note that International students have to pay an additional £10 as a shipment fee.


Academy for Health and Fitness

Big Data Analytics

Data Science

Professional Skills Course (PSC)

Data Skills

Trainers Course

Certified in Data Protection (CDP)

Complete Data Science - CPD Certified

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			<h2><b>Course Overview</b></h2>		</div>
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					<div><p><span>Machine learning as a programming technique has shaped the future of technology. In this course, you will learn how to build intelligent handwriting recognition apps from scratch using Python and Core ML.</span></p><p><span>The </span><b>Machine Learning for Apps Level 4 </b><span>course will teach you how to take advantage of machine learning to code like a pro and build incredible apps that can make predictions. Designed by industry experts, it covers best practices for managing projects, core concepts for creating your own ML model, building a convolutional neural network, and much more.</span></p><p><span>On successful completion, you will be able to build an amazing handwriting recognition app and convolutional neural network from scratch, and have an in-depth understanding of the core ML basics. This course is ideal for those with a basic understanding of iOS development.</span></p></div>
				</div>
				</div>
				<div>
				<div>
					<div><p>This best selling <strong>Machine Learning for Apps Level 4 </strong>has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth <strong>Machine Learning for Apps Level 4 </strong>is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The <strong>Machine Learning for Apps Level 4 </strong>is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The <strong>Machine Learning for Apps Level 4 </strong>is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the<strong> Machine Learning for Apps Level 4</strong>, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the <strong>Machine Learning for Apps Level 4 </strong>will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the <strong>Machine Learning for Apps Level 4 </strong>to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Course Overview
Machine learning as a programming technique has shaped the future of technology. In this course, you will learn how to build intelligent handwriting recognition apps from scratch using Python and Core ML.
The Machine Learning for Apps Level 4 course will teach you how to take advantage of machine learning to code like a pro and build incredible apps that can make predictions. Designed by industry experts, it covers best practices for managing projects, core concepts for creating your own ML model, building a convolutional neural network, and much more.
On successful completion, you will be able to build an amazing handwriting recognition app and convolutional neural network from scratch, and have an in-depth understanding of the core ML basics. This course is ideal for those with a basic understanding of iOS development.
This best selling Machine Learning for Apps Level 4 has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Machine Learning for Apps Level 4 is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.
The Machine Learning for Apps Level 4 is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The Machine Learning for Apps Level 4 is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.
After successfully completing the Machine Learning for Apps Level 4, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Machine Learning for Apps Level 4 will help you stand out from the crowd. You can also validate your certification on our website.
We know that you are busy and that time is precious, so we have designed the Machine Learning for Apps Level 4 to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. 
Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.

Educators providing Machine Learning (ML) courses in Cardiff delivered On Demand