A Course in Machine Learning

Here's a good book on convolutional neural networks: http://www.deeplearningbook.org/ It's suppose to be like the bible on deep learning at the moment but like most cutting edge stuff -- you're going to have to put a lot of effort into thinking for yourself and reading a lot of papers to make any new progress.

A lot of easy to digest content in this! Always great to see quality free material to help more people pick up machine learning and get involved solving problems using data science. One thing I didn't see covered in depth here was feature engineering, which is the process of preparing your raw data for the machine learning algorithms. They cover it briefly in the chapter on "Practical Considerations", but anyone looking to apply ML in the real world should look…

For those who might not know, Hal Daumé III is a highly respected researcher in machine learning, spending a lot of time working in Natural Language Processing (NLP). He also contributes his knowledge to many interesting open source projects. After creating an algorithm called SEARN for solving hard structured prediction problems, Daumé et al. went and created a practical and open implementation of the algorithm in Vowpal Wabbit, an online machine learning library built for speed and used at Yahoo…

Direct link to the PDF: http://ciml.info/dl/v0_9/ciml-v0_9-all.pdf