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 MATLAB
- How these three areas complement each other
- How these three areas are needed for robust machine learning applications
- How to use MATLAB graphics and visualization tools for machine learning
- How 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.

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