<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

Loughborough

<div>
		

		<div>
			
			<p>This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open-source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. This course includes hands-on activities for each topic area.</p>
			<div><i></i> 5 Days 1 Exam</div>
			<div><i></i>  £595.00 incl. VAT</div>
			
			
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		<h3>Description</h3>
		<p></p><h3>Overview:</h3>
<p>Artificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many organisations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organisations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open-source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. This course includes hands-on activities for each topic area.</p>
<h3>Course Objectives:</h3>
<p>In this course, you will implement AI techniques in order to solve business problems. You will:</p>
<p>• Specify a general approach to solve a given business problem that uses applied AI and ML.<br />• Collect and refine a dataset to prepare it for training and testing. <br />• Train and tune a machine learning model. <br />• Finalize a machine learning model and present the results to the appropriate audience. <br />• Build linear regression models. <br />• Build classification models. <br />• Build clustering models. <br />• Build decision trees and random forests. <br />• Build support-vector machines (SVMs). <br />• Build artificial neural networks (ANNs). <br />• Promote data privacy and ethical practices within AI and ML projects. </p>
<p> </p>
<p></p>
	
		<h3>Modules/Lessons</h3>
		<p></p><h4><span>Lesson 1: Solving Business Problems Using AI and ML</span></h4>
<p>Topic A: Identify AI and ML Solutions for Business Problems<br />Topic B: Formulate a Machine Learning Problem<br />Topic C: Select Appropriate Tools  </p>
<h4>Lesson 2: Collecting and Refining the Dataset</h4>
<p>Topic A: Collect the Dataset<br />Topic B: Analyze the Dataset to Gain Insights<br />Topic C: Use Visualizations to Analyze Data<br />Topic D: Prepare Data</p>
<h4>Lesson 3: Setting Up and Training a Model</h4>
<p>Topic A: Set Up a Machine Learning Model<br />Topic B: Train the Model </p>
<h4>Lesson 4: Finalizing a Model </h4>
<p>Topic A: Translate Results into Business Actions<br />Topic B: Incorporate a Model into a Long-Term Business Solution </p>
<h4>Lesson 5: Building Linear Regression Models</h4>
<p>Topic A: Build a Regression Model Using Linear Algebra<br />Topic B: Build a Regularized Regression Model Using Linear Algebra<br />Topic C: Build an Iterative Linear Regression Model </p>
<h4>Lesson 6: Building Classification Models</h4>
<p>Topic A: Train Binary Classification Models<br />Topic B: Train Multi-Class Classification Models<br />Topic C: Evaluate Classification Models<br />Topic D: Tune Classification Models</p>
<h4>Lesson 7: Building Clustering Models</h4>
<p>Topic A: Build k-Means Clustering Models<br />Topic B:  Build  Hierarchical  Clustering  Mode</p>
<h4>Lesson 8: Building Advanced Models</h4>
<p>Topic A: Build  Decision Tree Models<br />Topic B: Build Random Forest Model Lesson</p>
<h4>Lesson  9: Building Supports Vector Machines</h4>
<p>Topic A: Build SVM Models for Classification<br />Topic B:  Build SVM Models for Regression</p>
<h4>Lesson 10: Building Artificial Neural Networks</h4>
<p>Topic A: Build Multilayer Perceptrons (MLP)<br />Topic B: Build Convolutional Neural Networks (CNN)</p>
<h4>Lesson 11: Promoting Data Privacy and Ethical Practices</h4>
<p>Topic A: Protect Data Privacy<br />Topic B: Promote Ethical  Practices<br />Topic C: Establish Data Privacy and Ethics Policies</p>
<p></p>
	
		<h3>Prerequisites</h3>
		<p></p><p>To ensure your success in this course, you should have at least a high-level understanding of fundamental AI concepts, including, but not limited to: machine learning, supervised learning, unsupervised learning, artificial neural networks, computer vision, and natural language processing.</p>
<p>You should also have experience working with databases and a high-level programming language such as Python, Java, or C/C++.</p>
<p></p>
	
		<h3>Target Student</h3>
		<p></p><p>The skills covered in this course converge on three areas-software development, applied math and statistics, and business analysis. Target students for this course may be strong in one or two or these of these areas and looking to round out their skills in the other areas so they can apply artificial intelligence (AI) systems, particularly machine learning models, to business problems.  So the target student may be a programmer looking to develop additional skills to apply machine learning algorithms to business problems, or a data analyst who already has strong skills in applying math and statistics to business problems, but is looking to develop technology skills related to machine learning.   A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. </p>
<p>This course is also designed to assist students in preparing for the Certified Artificial Intelligence (AI) Practitioner certification.</p>
<p></p>

This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open-source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. This course includes hands-on activities for each topic area.
5 Days 1 Exam £595.00 incl. VAT
Want to run this event in-house? Enquire about running this event in-house 
Interested in attending? Have a suggestion about running this event near you?
Register your interest now
Description

Overview:
Artificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many organisations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organisations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open-source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. This course includes hands-on activities for each topic area.
Course Objectives:
In this course, you will implement AI techniques in order to solve business problems. You will:
• Specify a general approach to solve a given business problem that uses applied AI and ML.
• Collect and refine a dataset to prepare it for training and testing. 
• Train and tune a machine learning model. 
• Finalize a machine learning model and present the results to the appropriate audience. 
• Build linear regression models. 
• Build classification models. 
• Build clustering models. 
• Build decision trees and random forests. 
• Build support-vector machines (SVMs). 
• Build artificial neural networks (ANNs). 
• Promote data privacy and ethical practices within AI and ML projects. 
 

Modules/Lessons

Lesson 1: Solving Business Problems Using AI and ML
Topic A: Identify AI and ML Solutions for Business Problems
Topic B: Formulate a Machine Learning Problem
Topic C: Select Appropriate Tools 
Lesson 2: Collecting and Refining the Dataset
Topic A: Collect the Dataset
Topic B: Analyze the Dataset to Gain Insights
Topic C: Use Visualizations to Analyze Data
Topic D: Prepare Data
Lesson 3: Setting Up and Training a Model
Topic A: Set Up a Machine Learning Model
Topic B: Train the Model 
Lesson 4: Finalizing a Model
Topic A: Translate Results into Business Actions
Topic B: Incorporate a Model into a Long-Term Business Solution 
Lesson 5: Building Linear Regression Models
Topic A: Build a Regression Model Using Linear Algebra
Topic B: Build a Regularized Regression Model Using Linear Algebra
Topic C: Build an Iterative Linear Regression Model 
Lesson 6: Building Classification Models
Topic A: Train Binary Classification Models
Topic B: Train Multi-Class Classification Models
Topic C: Evaluate Classification Models
Topic D: Tune Classification Models
Lesson 7: Building Clustering Models
Topic A: Build k-Means Clustering Models
Topic B: Build Hierarchical Clustering Mode
Lesson 8: Building Advanced Models
Topic A: Build Decision Tree Models
Topic B: Build Random Forest Model Lesson
Lesson 9: Building Supports Vector Machines
Topic A: Build SVM Models for Classification
Topic B: Build SVM Models for Regression
Lesson 10: Building Artificial Neural Networks
Topic A: Build Multilayer Perceptrons (MLP)
Topic B: Build Convolutional Neural Networks (CNN)
Lesson 11: Promoting Data Privacy and Ethical Practices
Topic A: Protect Data Privacy
Topic B: Promote Ethical Practices
Topic C: Establish Data Privacy and Ethics Policies

Prerequisites

To ensure your success in this course, you should have at least a high-level understanding of fundamental AI concepts, including, but not limited to: machine learning, supervised learning, unsupervised learning, artificial neural networks, computer vision, and natural language processing.
You should also have experience working with databases and a high-level programming language such as Python, Java, or C/C++.

Target Student

The skills covered in this course converge on three areas-software development, applied math and statistics, and business analysis. Target students for this course may be strong in one or two or these of these areas and looking to round out their skills in the other areas so they can apply artificial intelligence (AI) systems, particularly machine learning models, to business problems. So the target student may be a programmer looking to develop additional skills to apply machine learning algorithms to business problems, or a data analyst who already has strong skills in applying math and statistics to business problems, but is looking to develop technology skills related to machine learning. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. 
This course is also designed to assist students in preparing for the Certified Artificial Intelligence (AI) Practitioner certification.


Mpi Learning - Professional Learning And Development Provider

Machine Learning (ML)

NLP Practitioner

Trainers Course

Azure Machine Learning

Microsoft AI-900

AI (Artificial Intelligence)

This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open-source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. This course includes hands-on activities for each topic area.

Certified Artificial Intelligence Practitioner

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>The EXIN BCS Artificial Intelligence Foundation certification is focused on individuals with an interest in, (or need to implement) AI in an organization, especially those working in areas such as science, engineering, knowledge engineering, finance, education or IT services.</p>Overview<p>You will be able to<br />Describe how Artificial (AI) is Part of 'Universal Design', and 'The Fourth Industrial Revolution'<br />Demonstrate Understanding of the Artificial Intelligence (AI) Intelligen Agent Description<br />Explain the Benefits of Artificial Intelligence (AI)<br />Describe how we Learn from Data - Functionality, Software and Hardware<br />Demonstrate an Understanding that Artificial Intelligence (AI) (in Particular, Machine Learning (ML)) will Drive Humans and Machines to Work Together<br />Describe a ''Learning from Experience'' Agile Approach to Projects</p><p>Candidates should be able to demonstrate a knowledge and understanding in the application of ethical and sustainable Artificial Intelligence (AI):- Human-centric Ethical and Sustainable Human and Artificial Intelligence (AI)</p></div><div id="course-pills-2"><h4>Ethical and Sustainable Human and Artificial Intelligence (AI)</h4><ul><li>Recall the General Definition of Human and Artificial Intelligence (AI)</li><li>Describe what are Ethics and Trustworthy Artificial Intelligence (AI)</li><li>Describe the Three Fundamental Areas of Sustainability and the United NationÃ¯s Seventeen Sustainability Goals</li><li>Describe how Artificial Intelligence (AI) is Part of 'Universal Design', and 'The Fourth Industrial Revolution'</li><li>Understand that Machine Learning (ML) is a Significant Contribution to the Growth of Artificial Intelligence (AI)</li></ul><h4>Artificial Intelligence (AI) and Robotics</h4><ul><li>Demonstrate Understanding of the Artificial Intelligence (AI) Intelligent Agent Description</li><li>Describe what a Robot is</li><li>Describe what an intelligent Robot is</li></ul><h4>Applying the Benefits of Artificial Intelligence (AI) ? Challenges and Risks</h4><ul><li>Describe how Sustainability Relates to Human-Centric Ethical Artificial Intelligence (AI) and how our Values will Drive our use of Artificial Intelligence (AI) and will Change Humans, Society and Organizations</li><li>Explain the Benefits of Artifical Intelligence (AI)</li><li>Describe the Challenges of Artificial Intelligence (AI) Projects</li><li>Demonstrate Understanding of the Risks of Artificial Intelligence (AI) Projects</li><li>List Opportunities for Artificial Intelligence (AI)</li><li>Identify a Typical Funding Source for Artificial Intelligence (AI) Projects and Relate to the NASA Technology Readiness Levels (TRLs)</li></ul><h4>Starting Artificial Intelligence (AI): how to Build a Machine Learning (ML) Toolbox ? Theory and Practice</h4><ul><li>Describe how we Learn from Data - Functionality, Software and Hardware</li><li>Recall which Rypical, Narrow Artificial Intelligence (AI) Capability is Useful in Machine Learning (ML9 and Artificial Intelligence (AI) AgentsÃ¯Functionality</li></ul><h4>The Management, Roles and Responsibilities of Humans and Machines</h4><ul><li>Demonstrate an Understanding that Artificial Intelligence (AI) (in Particular, Machine Learning (ML)) will Drive Humans and Machines to Work Together</li><li>List Future Directions of Humans and Machines Working Together</li><li>Describe a ''Learning from Experience'' Agile Approach to Projects</li></ul></div>

Duration
3 Days
18 CPD hours
This course is intended for
The EXIN BCS Artificial Intelligence Foundation certification is focused on individuals with an interest in, (or need to implement) AI in an organization, especially those working in areas such as science, engineering, knowledge engineering, finance, education or IT services.
Overview
You will be able to
Describe how Artificial (AI) is Part of 'Universal Design', and 'The Fourth Industrial Revolution'
Demonstrate Understanding of the Artificial Intelligence (AI) Intelligen Agent Description
Explain the Benefits of Artificial Intelligence (AI)
Describe how we Learn from Data - Functionality, Software and Hardware
Demonstrate an Understanding that Artificial Intelligence (AI) (in Particular, Machine Learning (ML)) will Drive Humans and Machines to Work Together
Describe a ''Learning from Experience'' Agile Approach to Projects
Candidates should be able to demonstrate a knowledge and understanding in the application of ethical and sustainable Artificial Intelligence (AI):- Human-centric Ethical and Sustainable Human and Artificial Intelligence (AI)
Ethical and Sustainable Human and Artificial Intelligence (AI)
Recall the General Definition of Human and Artificial Intelligence (AI)
Describe what are Ethics and Trustworthy Artificial Intelligence (AI)
Describe the Three Fundamental Areas of Sustainability and the United NationÃ¯s Seventeen Sustainability Goals
Describe how Artificial Intelligence (AI) is Part of 'Universal Design', and 'The Fourth Industrial Revolution'
Understand that Machine Learning (ML) is a Significant Contribution to the Growth of Artificial Intelligence (AI)
Artificial Intelligence (AI) and Robotics
Demonstrate Understanding of the Artificial Intelligence (AI) Intelligent Agent Description
Describe what a Robot is
Describe what an intelligent Robot is
Applying the Benefits of Artificial Intelligence (AI) ? Challenges and Risks
Describe how Sustainability Relates to Human-Centric Ethical Artificial Intelligence (AI) and how our Values will Drive our use of Artificial Intelligence (AI) and will Change Humans, Society and Organizations
Explain the Benefits of Artifical Intelligence (AI)
Describe the Challenges of Artificial Intelligence (AI) Projects
Demonstrate Understanding of the Risks of Artificial Intelligence (AI) Projects
List Opportunities for Artificial Intelligence (AI)
Identify a Typical Funding Source for Artificial Intelligence (AI) Projects and Relate to the NASA Technology Readiness Levels (TRLs)
Starting Artificial Intelligence (AI): how to Build a Machine Learning (ML) Toolbox ? Theory and Practice
Describe how we Learn from Data - Functionality, Software and Hardware
Recall which Rypical, Narrow Artificial Intelligence (AI) Capability is Useful in Machine Learning (ML9 and Artificial Intelligence (AI) AgentsÃ¯Functionality
The Management, Roles and Responsibilities of Humans and Machines
Demonstrate an Understanding that Artificial Intelligence (AI) (in Particular, Machine Learning (ML)) 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

Nexus Human

ICDL (International Certification of Digital Literacy)

EXIN BCS Artificial Intelligence Foundation

<div id="course-pills-1">Duration<p>5 Days</p><p>30 CPD hours</p>This course is intended for<p>The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification</p>Overview<p>In this course, you will develop AI solutions for business problems.<br />You will:<br />Solve a given business problem using AI and ML.<br />Prepare data for use in machine learning.<br />Train, evaluate, and tune a machine learning model.<br />Build linear regression models.<br />Build forecasting models.<br />Build classification models using logistic regression and k -nearest neighbor.<br />Build clustering models.<br />Build classification and regression models using decision trees and random forests.<br />Build classification and regression models using support-vector machines (SVMs).<br />Build artificial neural networks for deep learning.<br />Put machine learning models into operation using automated processes.<br />Maintain machine learning pipelines and models while they are in production</p><p>Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions.</p></div><div id="course-pills-2"><h4>Solving Business Problems Using AI and ML</h4><ul><li>Topic A: Identify AI and ML Solutions for Business Problems</li><li>Topic B: Formulate a Machine Learning Problem</li><li>Topic C: Select Approaches to Machine Learning</li></ul><h4>Preparing Data</h4><ul><li>Topic A: Collect Data</li><li>Topic B: Transform Data</li><li>Topic C: Engineer Features</li><li>Topic D: Work with Unstructured Data</li></ul><h4>Training, Evaluating, and Tuning a Machine Learning Model</h4><ul><li>Topic A: Train a Machine Learning Model</li><li>Topic B: Evaluate and Tune a Machine Learning Model</li></ul><h4>Building Linear Regression Models</h4><ul><li>Topic A: Build Regression Models Using Linear Algebra</li><li>Topic B: Build Regularized Linear Regression Models</li><li>Topic C: Build Iterative Linear Regression Models</li></ul><h4> Building Forecasting Models</h4><ul><li>Topic A: Build Univariate Time Series Models</li><li>Topic B: Build Multivariate Time Series Models</li></ul><h4>Building Classification Models Using Logistic Regression and k-Nearest Neighbor</h4><ul><li>Topic A: Train Binary Classification Models Using Logistic Regression</li><li>Topic B: Train Binary Classification Models Using k-Nearest Neighbor</li><li>Topic C: Train Multi-Class Classification Models</li><li>Topic D: Evaluate Classification Models</li><li>Topic E: Tune Classification Models</li></ul><h4>Building Clustering Models</h4><ul><li>Topic A: Build k-Means Clustering Models</li><li>Topic B: Build Hierarchical Clustering Models</li></ul><h4>Building Decision Trees and Random Forests</h4><ul><li>Topic A: Build Decision Tree Models</li><li>Topic B: Build Random Forest Models</li></ul><h4>Building Support-Vector Machines</h4><ul><li>Topic A: Build SVM Models for Classification</li><li>Topic B: Build SVM Models for Regression</li></ul><h4>Building Artificial Neural Networks</h4><ul><li>Topic A: Build Multi-Layer Perceptrons (MLP)</li><li>Topic B: Build Convolutional Neural Networks (CNN)</li><li>Topic C: Build Recurrent Neural Networks (RNN)</li></ul><h4>Operationalizing Machine Learning Models</h4><ul><li>Topic A: Deploy Machine Learning Models</li><li>Topic B: Automate the Machine Learning Process with MLOps</li><li>Topic C: Integrate Models into Machine Learning Systems</li></ul><h4>Maintaining Machine Learning Operations</h4><ul><li>Topic A: Secure Machine Learning Pipelines</li><li>Topic B: Maintain Models in Production</li></ul></div>

Duration
5 Days
30 CPD hours
This course is intended for
The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification
Overview
In this course, you will develop AI solutions for business problems.
You will:
Solve a given business problem using AI and ML.
Prepare data for use in machine learning.
Train, evaluate, and tune a machine learning model.
Build linear regression models.
Build forecasting models.
Build classification models using logistic regression and k -nearest neighbor.
Build clustering models.
Build classification and regression models using decision trees and random forests.
Build classification and regression models using support-vector machines (SVMs).
Build artificial neural networks for deep learning.
Put machine learning models into operation using automated processes.
Maintain machine learning pipelines and models while they are in production
Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions.
Solving Business Problems Using AI and ML
Topic A: Identify AI and ML Solutions for Business Problems
Topic B: Formulate a Machine Learning Problem
Topic C: Select Approaches to Machine Learning
Preparing Data
Topic A: Collect Data
Topic B: Transform Data
Topic C: Engineer Features
Topic D: Work with Unstructured Data
Training, Evaluating, and Tuning a Machine Learning Model
Topic A: Train a Machine Learning Model
Topic B: Evaluate and Tune a Machine Learning Model
Building Linear Regression Models
Topic A: Build Regression Models Using Linear Algebra
Topic B: Build Regularized Linear Regression Models
Topic C: Build Iterative Linear Regression Models
Building Forecasting Models
Topic A: Build Univariate Time Series Models
Topic B: Build Multivariate Time Series Models
Building Classification Models Using Logistic Regression and k-Nearest Neighbor
Topic A: Train Binary Classification Models Using Logistic Regression
Topic B: Train Binary Classification Models Using k-Nearest Neighbor
Topic C: Train Multi-Class Classification Models
Topic D: Evaluate Classification Models
Topic E: Tune Classification Models
Building Clustering Models
Topic A: Build k-Means Clustering Models
Topic B: Build Hierarchical Clustering Models
Building Decision Trees and Random Forests
Topic A: Build Decision Tree Models
Topic B: Build Random Forest Models
Building Support-Vector Machines
Topic A: Build SVM Models for Classification
Topic B: Build SVM Models for Regression
Building Artificial Neural Networks
Topic A: Build Multi-Layer Perceptrons (MLP)
Topic B: Build Convolutional Neural Networks (CNN)
Topic C: Build Recurrent Neural Networks (RNN)
Operationalizing Machine Learning Models
Topic A: Deploy Machine Learning Models
Topic B: Automate the Machine Learning Process with MLOps
Topic C: Integrate Models into Machine Learning Systems
Maintaining Machine Learning Operations
Topic A: Secure Machine Learning Pipelines
Topic B: Maintain Models in Production

AWS Certified Solutions Architect

CertNexus Certified Artificial Intelligence Practitioner CAIP (AIP-210)

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.
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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.

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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)

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

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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

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p>This course is intended for:<br />Developers<br />Solutions Architects<br />Data Engineers<br />Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon<br />SageMaker</p>Overview<p>In this course, you will learn to:<br />Select and justify the appropriate ML approach for a given business problem<br />Use the ML pipeline to solve a specific business problem<br />Train, evaluate, deploy, and tune an ML model using Amazon SageMaker<br />Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS<br />Apply machine learning to a real-life business problem after the course is complete</p><p>This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.</p></div><div id="course-pills-2"><h4>Module 0: Introduction</h4><ul><li>Pre-assessment</li></ul><h4>Module 1: Introduction to Machine Learning and the ML Pipeline </h4><ul><li>Overview of machine learning, including use cases, types of machine learning, and key concepts<br />Overview of the ML pipeline<br />Introduction to course projects and approach</li></ul><h4>Module 2: Introduction to Amazon SageMaker</h4><ul><li>Introduction to Amazon SageMaker</li><li>Demo: Amazon SageMaker and Jupyter notebooks</li><li>Hands-on: Amazon SageMaker and Jupyter notebooks</li></ul><h4>Module 3: Problem Formulation</h4><ul><li>Overview of problem formulation and deciding if ML is the right solution</li><li>Converting a business problem into an ML problem</li><li>Demo: Amazon SageMaker Ground Truth</li><li>Hands-on: Amazon SageMaker Ground Truth</li><li>Practice problem formulation</li><li>Formulate problems for projects</li></ul><h4>Module 4: Preprocessing</h4><ul><li>Overview of data collection and integration, and techniques for data preprocessing and visualization<br />Practice preprocessing<br />Preprocess project data<br />Class discussion about projects</li></ul><h4>Module 5: Model Training</h4><ul><li>Choosing the right algorithm<br />Formatting and splitting your data for training<br />Loss functions and gradient descent for improving your model<br />Demo: Create a training job in Amazon SageMaker</li></ul><h4>Module 6: Model Evaluation</h4><ul><li>How to evaluate classification models</li><li>How to evaluate regression models</li><li>Practice model training and evaluation</li><li>Train and evaluate project models</li><li>Initial project presentations</li></ul><h4>Module 7: Feature Engineering and Model Tuning</h4><ul><li>Feature extraction, selection, creation, and transformation</li><li>Hyperparameter tuning</li><li>Demo: SageMaker hyperparameter optimization</li><li>Practice feature engineering and model tuning</li><li>Apply feature engineering and model tuning to projects</li><li>Final project presentations</li></ul><h4>Module 8: Deployment</h4><ul><li>How to deploy, inference, and monitor your model on Amazon SageMaker<br />Deploying ML at the edge<br />Demo: Creating an Amazon SageMaker endpoint<br />Post-assessment<br />Course wrap-up</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans The Machine Learning Pipeline on AWS training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the The Machine Learning Pipeline on AWS course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Duration
4 Days
24 CPD hours
This course is intended for
This course is intended for:
Developers
Solutions Architects
Data Engineers
Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon
SageMaker
Overview
In this course, you will learn to:
Select and justify the appropriate ML approach for a given business problem
Use the ML pipeline to solve a specific business problem
Train, evaluate, deploy, and tune an ML model using Amazon SageMaker
Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS
Apply machine learning to a real-life business problem after the course is complete
This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.
Module 0: Introduction
Pre-assessment
Module 1: Introduction to Machine Learning and the ML Pipeline
Overview of machine learning, including use cases, types of machine learning, and key concepts
Overview of the ML pipeline
Introduction to course projects and approach
Module 2: Introduction to Amazon SageMaker
Introduction to Amazon SageMaker
Demo: Amazon SageMaker and Jupyter notebooks
Hands-on: Amazon SageMaker and Jupyter notebooks
Module 3: Problem Formulation
Overview of problem formulation and deciding if ML is the right solution
Converting a business problem into an ML problem
Demo: Amazon SageMaker Ground Truth
Hands-on: Amazon SageMaker Ground Truth
Practice problem formulation
Formulate problems for projects
Module 4: Preprocessing
Overview of data collection and integration, and techniques for data preprocessing and visualization
Practice preprocessing
Preprocess project data
Class discussion about projects
Module 5: Model Training
Choosing the right algorithm
Formatting and splitting your data for training
Loss functions and gradient descent for improving your model
Demo: Create a training job in Amazon SageMaker
Module 6: Model Evaluation
How to evaluate classification models
How to evaluate regression models
Practice model training and evaluation
Train and evaluate project models
Initial project presentations
Module 7: Feature Engineering and Model Tuning
Feature extraction, selection, creation, and transformation
Hyperparameter tuning
Demo: SageMaker hyperparameter optimization
Practice feature engineering and model tuning
Apply feature engineering and model tuning to projects
Final project presentations
Module 8: Deployment
How to deploy, inference, and monitor your model on Amazon SageMaker
Deploying ML at the edge
Demo: Creating an Amazon SageMaker endpoint
Post-assessment
Course wrap-up
Additional course details:
Nexus Humans The Machine Learning Pipeline on AWS training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. 

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

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

While we feel this is the best course for the The Machine Learning Pipeline on AWS course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.

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


Educators providing Machine Learning (ML) courses in Nottingham delivered Online