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Machine learning courses

Machine learning is a branch of computer science with the aim of constructing algorithms that can learn from and make predictions on data. The advances in this field led to numerous technical breakthroughs like self-driving cars or speech recognition. There are plenty of machine learning courses and tutorials available on the web. The list below will help you find the best one for you.

Free Machine learning courses and tutorials

Machine Learning for Coders
Full Machine Learning course recorded at the University of San Francisco

Machine Learning Crash Course
Google's fast-paced, practical introduction to machine learning

Machine Learning Caltech
18 lectures by Professor Yaser Abu-Mostafa from Caltech

Statistical Learning
Book about essential machine learning concepts by G. James et. al.

Machine Learning Andrew Ng
Machine Learning lecture from 2008 by Andrew Ng Stanford

Understanding Machine Learning
Comprehensive book focusing on theoretical aspects and practical algorithms

Machine Learning for Dummies
Short, free ebook on the basics of Machine Learning

Data Science: Machine Learning
Learn the basics of machine learning, the science behind the most popular and successful data science techniques, to build a movie recommendation system.

Intro to Machine Learning
Intro to Machine Learning[www.udacity.com]
This class will teach you the end-to-end process of investigating data through a machine learning lens, and you'll apply what you've learned to a real-world data set.

Machine Learning: Unsupervised Learning
Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? The answer can be found in Unsupervised Learning!

Machine Learning National Taiwan University
Machine Learning Course by Wei-Lun Chao from National Taiwan University

Principles of Machine Learning: Python Edition
Get hands-on experience building and deriving insights from machine learning models using Python and Azure Notebooks.

Machine Learning
Machine Learning[www.udacity.com]
In this course, you'll learn how to apply Supervised, Unsupervised and Reinforcement Learning techniques for solving a range of data science problems.

Machine Learning Hal Daumé III
Free ebook that covers the major aspects of modern machine learning

Reinforcement Learning
Reinforcement Learning[www.udacity.com]
Study machine learning at a deeper level and become a participant in the reinforcement learning research community.

Essential Math for Machine Learning: Python Edition
Learn the essential mathematical foundations for machine learning and artificial intelligence.

Essential Math for Machine Learning: R Edition
Learn the essential mathematical foundations for machine learning and artificial intelligence.

Machine Learning for Data Science and Analytics
Learn the principles of machine learning and the importance of algorithms.

Machine Learning for Trading
Machine Learning for Trading[www.udacity.com]
Implement machine learning based strategies to make trading decisions using real-world data.

Principles of Machine Learning: R Edition
Get hands-on experience building and deriving insights from machine learning models using R and Azure Notebooks.

Dynamic Programming: Applications In Machine Learning and Genomics
Learn how dynamic programming and Hidden Markov Models can be used to compare genetic strings and uncover evolution.

Advanced Machine Learning
Advanced Machine Learning[www.futurelearn.com]
Improve your understanding of machine learning. Explore advanced techniques and how to use them in your data science projects.

Additional useful resources

The Machine Learning Cheatsheet
The goal of this project was to develop a simple, concise, potentially exhaustive document about the most common machine learning algorithms.

Mathematics for Machine Learning
Free book that covers some of the mathematical concepts that are used in Machine Learning.

Machine learning Tips & Tricks from our community

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Yuwu · 257 weeks ago
If you are interested to really advance in Machine Learning, you should have a sound understanding of math. What you will certainly need is: Linear Algebra, Probability Theory, Calculus, Graph Theory and Optimization methods. You should also be able to program in Python, Matlab or C++ since these are often used in Machine Learning.
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