Mastering Machine Learning Theory and Practice

INTERACTIVE COURSE

Mastering Machine Learning Theory and Practice

Beginner

96 Lessons

36h

Certificate of Completion

AI Explanations

AI Explanations

Mastering Machine Learning Theory and Practice

109 Playgrounds

10 Quizzes

307 Illustrations

Takeaway Skills

An understanding of the foundations of machine learning and scientific modeling

A working knowledge of deep learning models including MLPs, CNNs, and RNNs

Familiarity with traditional machine learning methods including regression, SVMs, and decision trees

An understanding of the proper application of these methods

Hands-on experience with implementing various machine learning models using sklearn and Keras

Course Overview

The machine learning field is rapidly advancing today due to the availability of large datasets and the ability to process big data efficiently. Moreover, several new techniques have produced groundbreaking results for standard machine learning problems. This course provides a detailed description of different machine learning algorithms and techniques, including regression, deep learning, reinforcement learning, Bayes nets, support vector machines (SVMs), and decision trees. The course also offers suffici...Show More

Course Content

1

Getting started

Introduction to the Course

2

Introduction

The Basic Idea of Machine Learning

History of Machine Learning

Mathematical Formulation of the Basic Learning Problem Nonlinear Regression in High-Dimensions Recent Advances No Free Lunch, but Worth the Bite Quiz

3

Scientific Programming with Python

Programming Environment Basic Language Elements Code Efficiency and Vectorization Data Handling Image Processing and Convolutional Filters Quiz

4

Machine Learning with Sklearn

Overview Classification Using Multiple Machine Learning Models Performance Measures and Evaluations Data Handling Dimensionality Reduction, Feature Selection, and t-SNE Decision Trees and Random Forests Linear Support Vector Machines (SVMs)Nonlinear Support Vector Machines (SVMs)Validation of Support Vector Machines (SVMs)Challenge: Which Classifier Works Well?Solution: Which Classifier Works Well?Quiz

5

Neural Networks and Keras

Neurons and the Threshold Perceptron Multilayer Perceptron (MLP) and Keras Applying MLP to the MNIST dataset Representational Learning Introduction to Convolutional Neural Networks (CNNs)Applications of Convolutional Neural Networks (CNNs)Object Detection Challenges in Machine Learning Challenge: Choose the Best Model Solution: Choose the Best Model Quiz

6

Regression and Optimization

11 Lessons

7

Basic Probability Theory

8 Lessons

8

Probabilistic Regression and Bayes Nets

7 Lessons

9

Generative Models

10 Lessons

10

Cyclic Models and Recurrent Neural Networks

9 Lessons

11

Reinforcement Learning

8 Lessons

12

Artificial intelligence, the Brain, and Our Society

4 Lessons

13

Conclusion

2 Lessons

COURSE AUTHOR

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