MATLAB for Machine Learning is published by Packt Publishing in September 2017. This book has 382 pages in English, ISBN-13 978-1788398435.
Who This Book Is For
This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well.
What You Will Learn
- Learn the introductory concepts of machine learning.
- Discover different ways to transform data using SAS XPORT, import and export tools,
- Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data.
- Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment.
- Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures.
- Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox.
- Learn feature selection and extraction for dimensionality reduction leading to improved performance.
MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners.
You’ll start by getting your system ready with t he MATLAB environment for machine learning and you’ll see how to easily interact with the Matlab workspace. We’ll then move on to data cleansing, mining and analyzing various data types in machine learning and you’ll see how to display data values on a plot. Next, you’ll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions.
You’ll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you’ll explore feature selection and extraction techniques for dimensionality reduction for performance improvement.
At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB.