Some Introductory Machine Learning Books

Many Machine Learning books I encountered are too heavily math-wise (for a programmer). But I noted several introductory books,

•  Machine Learning, Tom M. Mitchell, McGraw Hill. 
•  Introduction to Machine Learning 2nd edition, Ethem Alpaydin, MIT Press. (without example code)
•  Bayesian Reasoning and Machine Learning, David Barber (this has free online draft version, last draft is dated Dec 13, 2014) (ex. code in Matlab with BRMLToolbox).
•  Machine Learning, A Probabilistic Perspective, Kevin P Murphy, MIT Press. (ex. code in Matlab with PMTK package.)
•  Machine Learning, An Algorithmic Perspective, Stephen Marsland, CRC Press. (ex. code in Python)
•  Machine Learning, Hands-On for Developers and Technical Professionals, Jason Bell, Wiley. (ex. code in Java with Weka toolkit.)
•  Machine Learning In Action, Peter Harrington, Manning. (ex. code in Python.)
•  Thoughtful Machine Learning, a Test Driven Approach, Matthew Kirk, O'Reilly. (ex. code in Ruby.)

More programming-wise books,
Python:

•  Mastering Machine Learning with scikit-learn, Gavin Hackeling, Packt.
•  Learning scikit-learn: Machine Learning in Python, Raúl Garreta et.al., Packt. 
•  scikit-learn Cookbook, Trent Hauck, Packt.
•  Building Machine Learning Systems with Python, Willi Richert et.al, Packt.

R:

•  An Introduction to Statistical Learning with Applications in R, Gareth James et.al, Springer.
•  Machine Learning with R, Brett Lantz, Packt.

Scala:

•  Scala for Machine Learning, Patrick R Nicolas, Packt.

Best ML course, with easy understandable video lectures, very well-structured:
Stanford's Prof. Andrew Ng  https://www.coursera.org/course/ml (old regular format with SoA, already closed since 2015).
New format of the course is on-demand (self-paced),  currently without SoA, https://www.coursera.org/learn/machine-learning .

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