HN Academy

I liked the EdX Statistical Learning course by Trevor Hastie and Robert Tibshirani, it's a great introduction to statistical modeling and data science (assuming you already have a solid math and statistics background): https://www.edx.org/course/statistical-learning

It is not too math heavy, and the focus is on basic, interpretable approaches and concepts like:

- linear and polynomial regression

- logistic regression and linear discriminant analysis

- cross-validation and the bootstrap

- model selection and regularization methods (ridge and lasso)

- tree-based methods, random forests and boosting

- support-vector machines

- neural networks and deep learning

- survival models; multiple testing

- some unsupervised learning methods like principal components and clustering (k-means and hierarchical).

The instructors are really articulate and passionate about teaching well. As a bonus, there are guest speakers about every second week including Jerome Friedman and Geoff Hinton.

I suggest first understanding machine learning in general before jumping into deep learning. The book ISLR2 is very accessible and starts with linear regression and works through many other methods including neural networks. There is a Edx course based on the book.

https://www.statlearning.com/

https://www.edx.org/course/statistical-learning

It looks pretty good. The introduction, ANN and Bayesian chapters look just as relevant today as they would have been when this book was published, 25 years ago. I like that hypotheses testing is covered.

Chapter 9, Genetic Algorithms and Programming, was hot stuff back then, and research continues today, I went to an interesting seminar a few months ago given by a genetic programming researcher, but from the perspective of solving a ML problem, less useful to the beginner. If you have a regression of classification problem, few people will be trying genetic algorithms to solve it.

Chapter 10 - 12 is rule based systems, for example Prolog, which again was thought of being the future back then, and as much as I love using Prolog, it is not part of modern ML curriculum.

Chapter 13 is short chapter on RL, which is popular today (DeepMind).

All that said, if a beginner wants to pick one book, I would suggest ISLR2, also free and has a Edx course

https://www.statlearning.com/

https://www.edx.org/course/statistical-learning

⬐ harry8

Is that the same one that was on the stanford edx clone site?

edx seems to be putting more behind a paywall and jacking up the price in a way that's pretty disappointing. That one is what $150 for the computer to tell you if the numbers you typed in some boxes is what was expected, for example. I mean, looking up the answer in the back of the book is worth that? The certificate is worth absolutely nothing to anyone save for how it makes them feel.

⬐ 082349872349872

RL ca. 1961: https://en.wikipedia.org/wiki/Matchbox_Educable_Noughts_and_...

(which I learned about from a Fred Saberhagen short)