Get a hands-on introduction to machine learning with genetic algorithms using Python. Genetic algorithms are one of the tools you can use to apply machine learning to finding good, sometimes even optimal, solutions to problems that have billions of potential solutions. This book gives you experience making genetic algorithms work for you, using easy-to-follow example projects that you can fall back upon when learning to use other machine learning tools and techniques. The step-by-step tutorials build your skills from Hello World! to optimizing one genetic algorithm with another, and finally genetic programming; thus preparing you to apply genetic algorithms to problems in your own field of expertise.

Python is a high-level, low ceremony and powerful language whose code can be easily understood even by entry-level programmers. If you have experience with another programming language then you should have no difficulty learning Python by induction.

Souce code: https://github.com/handcraftsman/GeneticAlgorithmsWithPython

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I am a polyglot programmer with more than 15 years of professional programming experience. When learning a new programming language, I start with a familiar problem and try to learn enough of the new language to solve it. For me, an engine for solving genetic algorithms is that familiar problem. Why? For one thing, it is a project where I can explore interesting puzzles, and where even a child's game like Tic-tac-toe can be viewed on a whole new level. Also, I can select increasingly complex puzzles to drive evolution in the capabilities of the engine. This allows me to discover the expressiveness of the language, the power of its tool chain, and the size of its development community as I work through the idiosyncrasies of the language.

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