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]
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.
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.
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.
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[ 2.6 ms ] story [ 55.6 ms ] threadFirst listing is from a commercial solver, rather sales oriented, though it looks like the topics may not depend on it?
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.
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:
http://cs229.stanford.edu/materials.html
https://class.coursera.org/ml-004/
[1] https://www.youtube.com/watch?v=UzxYlbK2c7E
[2] http://cs229.stanford.edu/
[3] http://work.caltech.edu/telecourse.html
A Course in Machine Learning http://www.e-booksdirectory.com/details.php?ebook=9395
A First Encounter with Machine Learning http://www.e-booksdirectory.com/details.php?ebook=8818
Bayesian Reasoning and Machine Learning http://www.e-booksdirectory.com/details.php?ebook=5283
Introduction to Machine Learning http://www.e-booksdirectory.com/details.php?ebook=4493
The Elements of Statistical Learning: Data Mining, Inference, and Prediction http://www.e-booksdirectory.com/details.php?ebook=3267
Reinforcement Learning by C. Weber, M. Elshaw, N. M. Mayer http://www.e-booksdirectory.com/details.php?ebook=3227
Machine Learning by Abdelhamid Mellouk, Abdennacer Chebira http://www.e-booksdirectory.com/details.php?ebook=2852
How Are We To Know? by Nils J. Nilsson http://www.e-booksdirectory.com/details.php?ebook=2710
Reinforcement Learning: An Introduction http://www.e-booksdirectory.com/details.php?ebook=1825
Gaussian Processes for Machine Learning http://www.e-booksdirectory.com/details.php?ebook=1774
Machine Learning, Neural and Statistical Classification http://www.e-booksdirectory.com/details.php?ebook=1118
Introduction To Machine Learning http://www.e-booksdirectory.com/details.php?ebook=1117
Inductive Logic Programming: Techniques and Applications http://www.e-booksdirectory.com/details.php?ebook=1105
Practical Artificial Intelligence Programming in Java http://www.e-booksdirectory.com/details.php?ebook=32
Information Theory, Inference, and Learning Algorithms http://www.e-booksdirectory.com/details.php?ebook=21
But I do agree, a good textbook is well worth the investment over some free poorly written one.
I found this to be quite a good introduction.