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)** A point estimate is commonly used to make decisions about ________.

(Fill in the blank) A point estimate is commonly used to make decisions about ________.

**(Fill in the blank)** The joint probability table for the $12$ variables would have ________ entries.

(Fill in the blank) The joint probability table for the <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>12</mn></mrow><annotation encoding="application/x-tex">12</annotation></semantics></math>12 variables would have ________ entries.

Which of the following libraries have direct support for Bayesian models?

Which of the following libraries have direct support for Bayesian models?

**(Fill in the blank)** The joint density functions of multiple variables describe the co-occurrence of ________ values of the random variables. 

(Fill in the blank) The joint density functions of multiple variables describe the co-occurrence of ________ values of the random variables.

Drawing balls from a bucket without replacement

Drawing cards from a deck without replacement

**(Select all that apply)** Which of the following are examples of the Bernoulli process? 

(Select all that apply) Which of the following are examples of the Bernoulli process?

**(True or False)** Minimizing the cross-entropy is equivalent to minimizing the $\text{KL}$ divergence.

(True or False) Minimizing the cross-entropy is equivalent to minimizing the <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>KL</mtext></mrow><annotation encoding="application/x-tex">\text{KL}</annotation></semantics></math>KL divergence.

**(True or False)** Using trained neural network models is an important application of causal models.

(True or False) Using trained neural network models is an important application of causal models.

**(True or False)** The logistic function describes the probability that a data point belongs to one class versus another.

(True or False) The logistic function describes the probability that a data point belongs to one class versus another.

Test yourself on Probabilistic Regression and Bayes' Nets.

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