The Missing Roadmap to Self-Study Machine Learning

Last Updated on June 7, 2016

In this post I lay out a concrete self-study roadmap for applied machine learning that you can use to orient yourself and figure out your next step.

I think a lot about frameworks and systematic approaches (as evidenced on my blog). I would consider this post a vast expansion of my previous thoughts on a self-study program in the post “Self-Study Guide to Machine Learning” that really hit a chord in the community.

Let’s jump in…

Machine Learning Roadmap

Machine learning is a huge field of study. There are so many algorithms, theories, techniques and classes of problems to learn about that it does feel overwhelming.

Machine learning is also deeply interdisciplinary. You can jump from material pitched to programmers, to material pitched to statisticians and it does feel frustrating when so much prior knowledge is assumed.

What is needed is a structured approach that provides a roadmap for studying the topics and levels of detail in machine learning that also integrates popular resources like books
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