MATLAB Machine Learning Recipes: A Problem-Solution Approach
AUTHOR | Thomas, Stephanie; Paluszek, Michael |
PUBLISHER | Apress (02/01/2019) |
PRODUCT TYPE | Paperback (Paperback) |
Description
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:
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
Show More
Product Format
Product Details
ISBN-13:
9781484239155
ISBN-10:
1484239156
Binding:
Paperback or Softback (Trade Paperback (Us))
Content Language:
English
Edition Number:
0002
More Product Details
Page Count:
347
Carton Quantity:
11
Product Dimensions:
7.01 x 0.76 x 10.00 inches
Weight:
1.54 pound(s)
Feature Codes:
Illustrated
Country of Origin:
NL
Subject Information
BISAC Categories
Computers | Computer Science
Computers | Database Administration & Management
Computers | Data Science - General
Dewey Decimal:
005.7
Descriptions, Reviews, Etc.
jacket back
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. You will:
- Learn to write code for machine learning, adaptive control and estimation using MATLAB
- See how these three areas complement each other
- Understand why these three areas are needed for robust machine learning applications
- Use MATLAB graphics and visualization tools for machine learning
- Code real world examples in MATLAB for major applications of machine learning in big data
Show More
publisher marketing
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:
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
Show More
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