The bad part about eBooks is that they always pile up. They are probably the most non-read books in existence. Or why should I bother reading 16 eBooks on the same topic, when reading a single good one would be the sane solution (the one I'd choose for paper books)?
I certainly agree with you. Often, it's just better to buy a paper book than try to read a little bit here and there. After all, paper books are not that expensive. My personal problem is than I often buy books that I never end up reading, or that I read after years (five, six, or even more). I'm pretty sure I'm not the only one that suffers from this, though.
Actually there are several really good books in this list, also available as paper copies if you want (albeit quite expensive if you go that route. ESL for example is $70+).
But I do agree, a good textbook is well worth the investment over some free poorly written one.
I didn't knew that the Hastie/Tibshirani/Friedman was legally available as a free download. I would recommend it to anyone with a sufficient maths/stats background.
Yes, both 'The Elements of Statistical Learning' and 'An Introduction to Statistical Learning with Applications in R' are available free in pdf.
For fans of hard copy, I recently found that if your local (university?) library is a SpringerLink customer, you can purchase a print-on-demand copy of either book for $26.99, which includes shipping. Interior pages are in black and white (including the graphs), but that is a really cheap price for these two.
Andrew Ng's course notes from his physical class at Stanford (CS 229 - Machine Learning) are extensive and available as well at:
I'd suggest going back to the original youtube[1] and course materials[2]. Coursera version is nothing but a hand-wavy watered down "feel good" version of the original class. I also really like the Caltech's take "Learning from data"[3]
While not free, 'Machine Learning: A Probabilistic Perspective' (http://www.amazon.co.uk/gp/aw/d/0262018020) is the best book I have found so far. I also second the recommendations for Tibshirani's and MacKay's books; the former for mathematical foundations, the latter for the intuition.
First of all thanks for sharing. I would like to study machine learning.
I have a good code and math background.
Which of these books is the most recommended?
"The Elements of Statistical Learning" is great. It assumes an astute reader, but if you've made it through some post-graduate level work, you'd be fine.
