Genetic Algorithms and Machine Learning for Programmers (Pragmatic Bookshelf)

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Frances Buontempo @fbuontempo

edited by Tammy Coron @Paradox927

Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Discover machine learning algorithms using a handful of self-contained recipes. Create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, and cellular automata. Find minima and maxima, using hill climbing and simulated annealing. Try selection methods, including tournament and roulette wheels. Learn about heuristics, fitness functions, metrics, and clusters.

Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems. Test your code and get inspired to try new problems. Work through scenarios to gain hands-on experience. See how the algorithms explore and learn by creating visualizations of each problem. Get inspired to design your own machine learning projects.

Get started with machine learning, and discover new solutions to classic problems.

“A unique take on the subject and should very much appeal to programmers looking to get started with various machine learning techniques.”

–Christopher L. Simons, Senior Lecturer, University of the West of England, Bristol, UK

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