Book for reading before Elements of Statistical Learning? Based on this post, I want to digest Elements of Statistical Learning. Fortunately it is available for free and I started reading it.
I don't have enough knowledge to understand it. Can you recommend a book that is a better introduction to the topics in the book? Hopefully something that will give me the knowledge needed to understand it?
Related:
Is a strong background in maths a total requisite for ML?
 A: The Elements Of Statistical Learning might be a tough read, especially for a self-learner. While searching for some explanations on the second chapter I have stumbled on the following resource: https://waxworksmath.com/Authors/G_M/Hastie/WriteUp/Weatherwax_Epstein_Hastie_Solution_Manual.pdf. It contains 100+ pages of annotations and explanations that clarify some complicated moments of the book. A great resource for everyone reading this book. This complementary text includes solutions for exercises.
A: I'd strongly recommend A First Course in Machine Learning by Rogers and Girolami.  It covers the key ideas in a very logical order, with good examples and with the minimum level of maths to have a proper grounding in the fundamentals.  It doesn't have the breadth of coverage of some books, but that is exactly why it is so good as an introductory text.
A: The authors of Elements of Statistical Learning have come out with a new book (Aug 2013) aimed at users without heavy math backgrounds. An Introduction to Statistical Learning: with Applications in R
The free PDF version of this book can currently be found here.
A: Another book that is very interesting is Bayesian Reasoning and Machine Learning by David Barber. The book is available as a free download from the author's website: 
http://www.cs.ucl.ac.uk/staff/d.barber/brml/
A: I bought, but have not yet read, 

S. Marsland, Machine Learning: An Algorithmic Perspective, Chapman & Hall, 2009. 

However, the reviews are favorable and state that it is more suitable for beginners than other ML books that have more depth. Flipping through the pages, it looks to me to be good for me because I have little math background.
A: I found Programming Collective Intelligence the easiest book for beginners, since the author Toby Segaran is is focused on allowing the median software developer to get his/her hands dirty with data hacking as fast as possible. 
Typical chapter: The data problem is clearly described, followed by a rough explanation how the algorithm works and finally shows how to create some insights with just a few lines of code. 
The usage of python allows one to understand everything rather fast (you do not need to know python, seriously, I did not know it before, too). DONT think that this book is only focused on creating recommender system. It also deals with text mining / spam filtering / optimization / clustering / validation etc. and hence gives you a neat overview over the basic tools of every data miner. 
Chapter 10 even deals with stock market data, but the focus is not on time series data mining. Maybe the only drawback (for you) of this excellent book.
A: Introduction to Machine Learning, by E. Alpaydin (MIT Press, 2010, 2nd ed.), covers a lot of topics with nice illustrations (much like Bishop's Pattern Recognition and Machine Learning). 
In addition, Andrew W. Moore has some nice tutorials on Statistical Data Mining.
A: Mayhaps Wasserman's All of Statistics would be of interest. You can sample the book from the link given - and just the first few paragraphs of the preface make a hard sale to your market - and you can likely download the book free through Springer if you are associated with a university.
EDIT: Oops, didn't notice how ancient this thread was.
