Courses to practice crucial Machine Learning skills.
These days, courses are no longer a sequence of videos. They are usually accompanied by projects and a
learning community, keeping you accountable and on the path.
Our experts recommend these courses, from free
selections to paid programs.
Deep Learning Specialization
In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. 38% of students started a new career after completing this specialization.
MIT Open: Linear Algebra
Math is the foundation of Machine Learning and much needed if you need to work on the inner logic of its systems. Senior engineers are encouraged to propose and submit their own papers – and getting your LinAlg back in order is a must for that.
Lex Fridman's MIT Deep Learning
Lex Fridman is the instructor of an immensely popular and fundamental Deep Learning course at MIT. Together with the other MIT AI courses, this can help polish your skills and get the foundations right.
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Best books to further your Machine Learning understanding.
A well-written and thorough book can be an amazing path to build deeper understand and also act as a
handbook as you discover the internet's vast resources.
These are our and our experts top picks to get
started building career-relevant skills.
At least today, code is our door to building algorithms and complex Machine Learning systems. If you want to invest in becoming a more proficient Machine Learning professional faster, investing in code skills is the way to do so.
Reinforcement Learning: An Introduction
Richard Sutton's book on Machine Learning is universally regarded as one of the most fundamental and important pieces on the matter. Reinforcement Learning is quickly becoming a major part of AI innovation, and a good read for any engineer and scientists to go through.
Generative Deep Learning
It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples o
Federated Learning (Synthesis Lectures)
How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security.
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The Machine Learning must-reads you shouldn't miss.
Key articles and posts of industry experts can help you get a better picture of what you are getting
In our opinion, these are some must-reads you really shouldn't miss.
Karpathy on "Software 2.0"
Andrej Karpathy is the Director of AI at Tesla. Before that, though, he authored this blog post in 2017 talking about Deep Learning as "Software 2.0" of some sort. A must-read if you ever want to have another way of thinking about ML.
Simple Reinforcement Learning with Tensorflow
This 8-part series by Arthur Juliani (Deep RL researcher at Unity) is an amazing entry point to the new and mysterious advancements of Reinforcement Learning, perfectly suited for folks coming from other topics in Machine Learning.
Building Safe A.I. (Trask)
Andrew Trask is a specialist in Federated Learning and Safe AI. In this blogpost, he writes about training a neural network that is fully encrypted during training (trained on unencrypted data).
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