Reddit reviews Machine Learning: The Art and Science of Algorithms that Make Sense of Data

Depending on what you want to learn:

PYTHON SPECIFIC

You mentioned building websites, so check out the flask mega tutorial. It might be a bit early to take on a project like this after only a month, but you've got time and learning-by-doing is good. This'll teach you to build a twitter clone using python, so you'll see databases, project structure, user logons etc. Plus he's got a book version, which contains much of the same info, but is good for when you can't be at a computer.

The python cookbook is fantastic for getting things done; gives short solutions to common problems / tasks. (How do I read lines from a csv file? How do I parse a file that's too big to fit in memory? How do I create a simple TCP server?). Solutions are concise and readable so you don't have to wade through loads of irrelevant stuff.

A little while down the road if you feel like going deep, fluent python will give you a deeper understanding of python than many people you'll encounter at Uni when you're out.

WEB DEV

If you want to go more into web dev, you'll also need to know some HTML, CSS and Javascript. Duckett's books don't go too in depth, but they're beautiful, a nice introduction, and a handy reference. Once you've got some JS, Secrets of the javascript ninja will give you a real appreciation of the deeper aspects of JS.

MACHINE LEARNING
In one of your comments you mentioned machine learning.

These aren't language specific programming books, and this isn't my specialty, but:

Fundamentals of Machine Learning for Predictive data analytics is a great introduction to the entire process, based upon CRISP-DM. Not much of a maths background required. This was the textbook used for my uni's first data analytics module. Highly recommended.

If you like you some maths, Flach will give you a stronger theoretical understanding, but personally I'd leave that until later.

Good luck and keep busy; you've got plenty to learn!

I used Machine Learning: The Art and Science of Algorithms that Make Sense of Data and Evaluating Learning Algorithms: A Classification Perspective in my grad course. They're both super to-the-point and are written in non-overcomplex language

From what I know, there's two basic ways most music recommendation services use. The one technique is to use an efficient comparison method called minhashing. But the basic idea is that you represent every song as a collection of users who like the song. The similarity between one user and another is the Jaccard similarity (the proportion of people in song A shared by song B). Minhashing is then used as more of a search algorithm for finding which sets share Jaccard similarity.

This works okay for a lot of things, but the music service Pandora actually does not use that method. They have a unique approach where someone (I think mostly grad students in musicolgy) actually sat down and listened to every song and filled out a little chart that said things like "minor key tonality" and you write in the tempo and all that. Like, just an exhaustive list. Then to find similar music they're using a distance metric of some kind, although I don't know all the details. But basically if you imagine every attribute a song can have as a dimension, a song is a point in high dimensional space and you're trying to find music that's physically closer. Pandora does also learn a little bit about what attributes are important to you, too.

In general, this sort of topic is part of a field called machine learning. I personally enjoyed this ML book which was maybe a bit heavy on math and theory and not so much on practicality, but I do think quite a few other more down-to-earth books on the subject have been published if you want to look around and find a good one. I also hear great things about the coursera class on machine learning and data science.

This book is a good introduction:

https://www.amazon.co.uk/Machine-Learning-Science-Algorithms-Sense/dp/1107422221

As a book for beginning R programmers, I would recommend The Art of R Programming: A Tour of Statistical Software Design, written by Norman Matloff. As a general machine learning book, I liked this book, written by Peter Flach.

Maybe add this book to this list of books? its modern and by the editor of the Machine Learning Journal

http://www.amazon.com/Machine-Learning-Science-Algorithms-Sense/dp/1107422221

http://www.amazon.com/Machine-Learning-Tom-M-Mitchell/dp/0070428077/ref=sr_1_3?s=books&ie=UTF8&qid=1397051304&sr=1-3&keywords=machine+learning is old but a classic.

And http://www.amazon.com/Data-Mining-Practical-Techniques-Management/dp/0123748569/ref=sr_1_1?s=books&ie=UTF8&qid=1397051336&sr=1-1&keywords=data+mining
Is a good by the authors of weka as well.


There are of course a lot of books but I think these are good ones for beginners.

For Machine Learning:

Machine Learning: The Art and Science of Algorithms that Make Sense of Data


The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Second book might be hard to digest without a statistics background.

Artificial Intelligence:

Artificial Intelligence: A Modern Approach