Python is a very popular language for machine learning. The machine learning libraries and frameworks in Python (especially around the SciPy stack) are maturing quickly. They may not be as feature rich as R, but they are robust enough for small to medium scale production implementation.
If you are a Python programmer looking to get into machine learning or you are generally interested to get into machine learning via Python, then I want to use this post to point out some key books you might find useful on your journey.
This is by no means a complete list of books, but I think they are the pick of the books you should look at if you are interested in machine learning in Python
Machine Learning in Python
Building Machine Learning Systems with Python (2013): Master the art of machine learning with Python and build effective machine learning systems with this intensive hands-on guide.
Learning scikit-learn: Machine Learning in Python (2013): Experience the benefits of machine learning techniques by applying them to real-world problems using Python and the open source scikit-learn library.
Machine Learning in Action (2012): Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You’ll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.
Programming Collective Intelligence: Building Smart Web 2.0 Applications (2007): This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet.
Machine Learning: An Algorithmic Perspective (2011): The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.
Specialty Machine Learning in Python
Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More (2013): You’ll learn how to acquire, analyze, and summarize data from all corners of the social web, including Facebook, Twitter, LinkedIn, Google+, GitHub, email, websites, and blogs.
Natural Language Processing with Python (2009): This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation.
Programming Computer Vision with Python: Tools and algorithms for analyzing images (2012): If you want a basic understanding of computer vision’s underlying theory and algorithms, this hands-on introduction is the ideal place to start. You’ll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, and other computer vision applications as you follow clear examples written in Python.
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (2012): It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you’ll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.