AI News, Level-Up Your Machine Learning

Level-Up Your Machine Learning

Since launching Metacademy, I've had a number of people ask , What should I do if I want to get 'better' at machine learning, but I don't know what I want to learn?

then watch as they grimace in the same way an out-of-shape person grimaces when a healthy friend responds with, 'Oh, I watch what I eat and consistently exercise.'

you'll scribble in the margins and dog-ear commonly referenced areas and look for applications of the topics you learn -- the textbook itself becomes a part of your knowledge (the above image shows my nearest textbook).

Key Chapters: It's a short read, and every chapter is fairly illuminating -- though, you can skip the worksheet examples, and chapters 8 and 10 if you're interested in a basic overview.

This will test your ability to manipulate data for a desired machine learning task, and also your ability to apply the correct machine learning technique to a somewhat vague problem.

Expectations: You'll be able to recognize when fundamental machine learning algorithms apply to certain problems and implement functioning machine learning code in R Necessary Background: No real prerequisites, though the following will help (these can be learned/reviewed as you go): Key Chapters: It's a short book, and I recommend all of the chapters -- be sure to actually think through the examples (and type them into R).

This will test your data munging capabilities, your strategy for analyzing a larger dataset, your knowledge of machine learning techniques, and your ability to write analysis code in R.

This will test your ability to understand and interpret cutting-edge machine learning algorithms, approximate and online inference techniques, as well as your implementation chops, your data munging abilities, and your ability to define an interesting application from a vaguely defined problem.

If you're unfamiliar with Bayesian statistics, I recommend studying the first 5 chapters of Doing Bayesian Data Analysis There's a number of subjects you may want to study in depth at the master level: convex optimization, [measure-theoretic] probability theory, discrete optimization, linear algebra, differential geometry, or maybe computational neurology.

PGMs pervade machine learning, and with a strong understanding of this content, you'll be able to dive into most machine learning specialties without too much pain.

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