Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MATLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow the reader to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more.

What you'll learn:How to write code for machine learning, adaptive control and estimation using MATLABHow these three areas complement each otherHow these three areas are needed for robust machine learning applicationsHow to use MATLAB graphics and visualization tools for machine learningHow to code real world examples in MATLAB for major applications of machine learning in big data Who is this book for: The primary audiences are engineers, data scientists and students wanting a comprehensive and code cookbook rich in examples on machine learning using MATLAB.

Download

Mirror1

Mirror2

See also

• Software Exorcism: A Handbook for Debugging and Optimizing Legacy Code by Bill Blunden (2003)
• Hormones and the Endocrine System: Textbook of Endocrinology by Bernhard Kleine (2016)
• Genetics: A Conceptual Approach 6e by Benjamin A. Pierce (2017)
• A Functional Approach to Java by Ben Weidig (2022)
• Calculus of variations and optimal control by Amol Sasan (2005)