Gain insights into machine learning algorithms like regression, deep learning, and SVMs. Learn about Python, probability theory, and optimization through practical coding exercises. Delve into ML intricacies.

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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 sufficient mathematical details for a deeper understanding of how different techniques work. An overview of the Python programming language and the fundamental theoretical aspects of ML, including probability theory and optimization, is also included. The course contains several practical coding exercises as well.

By the end of the course, you will have a deep understanding of different machine-learning methods and the ability to choose the right method for different applications.

Mastering Machine Learning Theory and Practice

**(Fill in the blank)** The ability of a computer to learn to play video games is an example of _______.

(Fill in the blank) The ability of a computer to learn to play video games is an example of _______.

**(Fill in the blank)** An approximation of a system to predict its behavior is called _______.

(Fill in the blank) An approximation of a system to predict its behavior is called _______.

What does $\mathcal{L}$ represent in the equation below?
$$
w_{i} \leftarrow w_{i}-\alpha \nabla_{w} \mathcal{L}
$$


What does <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">L</mi></mrow><annotation encoding="application/x-tex">\mathcal{L}</annotation></semantics></math>L represent in the equation below?
<math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msub><mi>w</mi><mi>i</mi></msub><mo>←</mo><msub><mi>w</mi><mi>i</mi></msub><mo>−</mo><mi>α</mi><msub><mi mathvariant="normal">∇</mi><mi>w</mi></msub><mi mathvariant="script">L</mi></mrow><annotation encoding="application/x-tex">w_{i} \leftarrow w_{i}-\alpha \nabla_{w} \mathcal{L}
</annotation></semantics></math>wi​←wi​−α∇w​L

Linear regression is the simplest example of which one of the following?

Linear regression is the simplest example of which one of the following?

**(True or False)** The increase of variance in the test data is one indication of overfitting.

(True or False) The increase of variance in the test data is one indication of overfitting.

Test yourself on the basics of machine learning.

Getting started

Introduction

Scientific Programming with Python

Machine Learning with Sklearn

Neural Networks and Keras

Regression and Optimization

Basic Probability Theory

Probabilistic Regression and Bayes Nets

Generative Models

Cyclic Models and Recurrent Neural Networks

Reinforcement Learning

Artificial intelligence, the Brain, and Our Society

Conclusion

Quiz