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