Genetic Algorithms and Machine Learning for Programmers: Create AI Models and Evolve Solutions

Machine Learning enables self-driving cars, natural language recognition, and online recommendation engines. This book teaches beginner-to-intermediate programmers how to test their ML code and dive into advanced topics. It covers heuristics, design fitness functions, genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, minima and maxima, selection methods, and clusters.

\n Format: Paperback / softback
\n Length: 236 pages
\n Publication date: 08 February 2019
\n Publisher: The Pragmatic Programmers
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Machine Learning (ML) has revolutionized the field of technology, enabling the development of self-driving cars, natural language recognition, and online recommendation engines. With ML, programmers can now create their own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. This comprehensive book is designed for beginners-to-intermediate programmers who are eager to understand machine learning.

In this book, readers will learn how to use a handful of self-contained recipes to build a repertoire of algorithms. They will discover terms and approaches that apply generally, and bake intelligence into their algorithms to guide them to discover good solutions to problems. The book will cover topics such as:

Using heuristics and design fitness functions.

Building genetic algorithms.

Making nature-inspired swarms with ants, bees, and particles.

Creating Monte Carlo simulations.

Investigating cellular automata.

Finding minima and maxima, using hill climbing and simulated annealing.

Trying selection methods, including tournament and roulette wheels.

Learning about heuristics, fitness functions, metrics, and clusters.

Testing code and getting inspired to try new problems.

Working through scenarios to code their way out of a paper bag, an important skill for any competent programmer.

See how the algorithms explore and learn by creating visualizations of each problem.

Get inspired to design your own machine learning projects and become familiar with the jargon.

To get the most out of this book, readers will need to have a basic understanding of programming in C++ (>= C++11), Python (2.x or 3.x), and JavaScript. They will also need access to a computer with a programming environment installed.

Whether you are a student, professional, or hobbyist, this book will provide you with the knowledge and skills you need to start building your own machine learning algorithms. So, why wait? Start your journey into the world of machine learning today!

\n Weight: 456g\n
Dimension: 192 x 235 x 14 (mm)\n
ISBN-13: 9781680506204\n \n