#### Overview of this book

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.
Title Page
Credits
Foreword
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Customer Feedback
Preface
Free Chapter
Getting Started with MATLAB Machine Learning
Importing and Organizing Data in MATLAB
From Data to Knowledge Discovery
Finding Relationships between Variables - Regression Techniques
Pattern Recognition through Classification Algorithms
Identifying Groups of Data Using Clustering Methods
Simulation of Human Thinking - Artificial Neural Networks
Improving the Performance of the Machine Learning Model - Dimensionality Reduction
Machine Learning in Practice

## Classification Learner app

In the preceding paragraphs, we have faced several classification problems using some of the algorithms available in the MATLAB environment. We did it in programming mode, deliberately in order to understand in detail the different procedures. Now that we have fully understood these concepts, we can relax and explore the tools MATLAB offers us to perform interactive classification with the use of the Classification Learner app.

Using this app, we can classify our data using various algorithms and compare the results in the same environment. Select features, specify validation schemes, train models, and assess results becomes extremely simple and automatic with this app. The classification models available are: decision trees, discriminant analysis, Support Vector Machines (SVM), logistic regression, nearest neighbors, and ensemble classification.

The Classification Learner app performs supervised machine learning, starting from a known set of input data and known...