[psv1]

Good free resources:

- MIT: Big Picture of Calculus

- Harvard: Stats 110

- MIT: Matrix Methods in Data Analysis, Signal Processing, and Machine Learning

If any of these seem too difficult - Khan Academy Precalculus (they also have Linear Algebra and Calculus material).

This gives you a math foundation. Some books more specific to ML:

- Foundations of Data Science - Blum et al.

- Elements of Statistical Learning - Hastie et al. The simpler version of this book - Introduction to Statistical Learning - also has a free companion course on Stanford's website.

- Machine Learning: A Probabilistic Perspective - Murphy

That's a lot of material to cover. And at some point you should start experimenting and building things yourself of course. If you'are already familiar with Python, the Data Science Handbook (Jake Vanderplas) is a good guide through the ecosystem of libraries that you would commonly use.

Things I don't recommend - Fast.ai, Goodfellow's Deep Learning Book, Bishop's Pattern Recognition and ML book, Andrew Ng's ML course, Coursera, Udacity, Udemy, Kaggle.

[joshvm]

Bear in mind Elements of Statistical Learning is a grad-level text. I would never recommend that to a beginner to the field over an Introduction to Statistical Inference, by the same authors.

Geron Aurelien's Oreilly book is great - Hands-On Machine Learning with Scikit-Learn and TensorFlow. Get the second edition which covers Tensorflow 2.

[psv1]

You're right about ESL, that's why I started the list with some more fundamental material. Also, +1 for Aurelien's book, it's really good; I didn't know he had a revised edition for TensorFlow 2.

[vowelless]

Why don't you recommend fast.ai and kaggle?

[mathisonturing]

and Andrew Ng's ML course?

[fantispug]

If you like books and you want to deeply understand ML techniques I'd suggest jumping straight into "Introduction to Statistical Learning" and only learning calculus/stats/matrix methods (linear algebra) as you need them (you really don't need much from them in practice).

But it's ok to start using libraries and fitting models without understanding how they work deeply, and coming back to these books later (just make sure you come back; there's lots of useful ideas in them!) In which case I'd recommend some of the resources the parent doesn't recommend

[psv1]

> If you like books and you want to deeply understand ML techniques I'd suggest jumping straight into "Introduction to Statistical Learning" and only learning calculus/stats/matrix methods (linear algebra) as you need them (you really don't need much from them in practice).

This doesn't work. ISL is good, but it aims to be accessible by excluding most of the math. So if you go over it, you'll neither "deeply understand ML techniques", nor will you encounter enough math that you can learn along the way as you suggest.