ML Learning Resources
Comments recommend books, online courses like Andrew Ng's Coursera, and resources such as the Deep Learning Book for learning machine learning and deep learning.
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Sounds like you might like this book: https://www.deeplearningbook.org/
Possibly "Learning from Data"[1][2] would be of interest?[1]: https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/...[2]: https://amlbook.com/
Does https://mlbook.explained.ai/ help?Been on HN before, got very positive comments. From the author of ANTLR no less.
Read the "mathematics for machine learning" book.
Without doubt do the Andrew Ng course on Machine Learning.https://www.coursera.org/learn/machine-learningIt's excellent.
Sure, Goodfellow bookhttp://www.deeplearningbook.org/
Here’s the book that’s mentioned:http://www.deeplearningbook.org/Seems to have good reviews on Amazon:https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma...
I would pair [1] with Hands On Machine Learning with Scikit-Learn and Tensorflow by Aurélien Géron (I own both). It gives an excellent overview of machine learning including non-deep stuff (plus the ins and outs of scikit-learn and tensorflow).
Sure, I started off with Andrew Ng's course on coursera. Then I started with the book called Machine Learning by Tom Mitchell. I also have the PCI book to supplement Mitchell's book with code examples. I got Bishop's book too but to be honest I'm finding it a little harder to follow than the others.
I'm starting the fast.ai courses as a recent stat grad looking to expand my knowledge. Heard many good testimonials. Besides that, I think Andrew Ng's Coursera offerings - intro to ML and the newer NN specialization - are great first steps. Personally I have a hard time learning from videos, so I refer to my copy of Pattern Recognition and Machine Learning by Bishop.If you want a linear algebra text, I enjoy Strang's Introduction to Linear Algebra