Book Image

MATLAB for Machine Learning - Second Edition

By : Giuseppe Ciaburro
Book Image

MATLAB for Machine Learning - Second Edition

By: Giuseppe Ciaburro

Overview of this book

Discover why the MATLAB programming environment is highly favored by researchers and math experts for machine learning with this guide which is designed to enhance your proficiency in both machine learning and deep learning using MATLAB, paving the way for advanced applications. By navigating the versatile machine learning tools in the MATLAB environment, you’ll learn how to seamlessly interact with the workspace. You’ll then move on to data cleansing, data mining, and analyzing various types of data in machine learning, and visualize data values on a graph. As you progress, you’ll explore various classification and regression techniques, skillfully applying them with MATLAB functions. This book teaches you the essentials of neural networks, guiding you through data fitting, pattern recognition, and cluster analysis. You’ll also explore feature selection and extraction techniques for performance improvement through dimensionality reduction. Finally, you’ll leverage MATLAB tools for deep learning and managing convolutional neural networks. By the end of the book, you’ll be able to put it all together by applying major machine learning algorithms in real-world scenarios.

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Table of Contents (17 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
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Free Chapter
1
Part 1: Getting Started with Matlab
Part 1: Getting Started with Matlab
2
Chapter 1: Exploring MATLAB for Machine Learning
Chapter 1: Exploring MATLAB for Machine Learning
Technical requirements
Introducing ML
Discovering the different types of learning processes
Using ML techniques
Exploring MATLAB toolboxes for ML
ML applications in real life
Summary
3
Chapter 2: Working with Data in MATLAB
Chapter 2: Working with Data in MATLAB
Technical requirements
Importing data into MATLAB
Reading ASCII-delimited files
Exporting data from MATLAB
Working with different types of data
Exploring data wrangling
Discovering exploratory statistics
Introducing exploratory visualization
Understanding advanced data preprocessing techniques in MATLAB
Summary
4
Part 2: Understanding Machine Learning Algorithms in MATLAB
Part 2: Understanding Machine Learning Algorithms in MATLAB
5
Chapter 3: Prediction Using Classification and Regression
Chapter 3: Prediction Using Classification and Regression
Technical requirements
Introducing classification methods using MATLAB
Building an effective and accurate classifier
Exploring different types of regression
Making predictions with regression analysis in MATLAB
Evaluating model performance
Using advanced techniques for model evaluation and selection in MATLAB
Summary
6
Chapter 4: Clustering Analysis and Dimensionality Reduction
Chapter 4: Clustering Analysis and Dimensionality Reduction
Technical requirements
Understanding clustering – basic concepts and methods
Understanding hierarchical clustering
Partitioning-based clustering algorithms with MATLAB
Grouping data using the similarity measures
Discovering dimensionality reduction techniques
Feature selection and feature extraction using MATLAB
Summary
7
Chapter 5: Introducing Artificial Neural Network Modeling
Chapter 5: Introducing Artificial Neural Network Modeling
Technical requirements
Getting started with ANNs
Training and testing an ANN model in MATLAB
Understanding data fitting with ANNs
Discovering pattern recognition using ANNs
Building a clustering application with an ANN
Exploring advanced optimization techniques
Summary
8
Chapter 6: Deep Learning and Convolutional Neural Networks
Chapter 6: Deep Learning and Convolutional Neural Networks
Technical requirements
Understanding DL basic concepts
Exploring DL models
Approaching CNNs
Building a CNN in MATLAB
Exploring the model’s results
Discovering DL architectures
Summary
9
Part 3: Machine Learning in Practice
Part 3: Machine Learning in Practice
10
Chapter 7: Natural Language Processing Using MATLAB
Chapter 7: Natural Language Processing Using MATLAB
Technical requirements
Explaining NLP
Exploring corpora and word and sentence tokenizers
Implementing a MATLAB model to label sentences
Understanding gradient boosting techniques
Summary
11
Chapter 8: MATLAB for Image Processing and Computer Vision
Chapter 8: MATLAB for Image Processing and Computer Vision
Technical requirements
Introducing image processing and computer vision
Exploring MATLAB tools for computer vision
Building a MATLAB model for object recognition
Training and fine-tuning pretrained deep learning models in MATLAB
Interpreting and explaining machine learning models
Summary
12
Chapter 9: Time Series Analysis and Forecasting with MATLAB
Chapter 9: Time Series Analysis and Forecasting with MATLAB
Technical requirements
Exploring the basic concepts of time series data
Extracting statistics from sequential data
Implementing a model to predict the stock market
Dealing with imbalanced datasets in MATLAB
Summary
13
Chapter 10: MATLAB Tools for Recommender Systems
Chapter 10: MATLAB Tools for Recommender Systems
Technical requirements
Introducing the basic concepts of recommender systems
Finding similar users in data
Creating recommender systems for network intrusion detection using MATLAB
Deploying machine learning models
Summary
14
Chapter 11: Anomaly Detection in MATLAB
Chapter 11: Anomaly Detection in MATLAB
Technical requirements
Introducing anomaly detection and fault diagnosis systems
Using ML to identify anomalous functioning
Building a fault diagnosis system using MATLAB
Understanding advanced regularization techniques
Summary
15
Index
Index
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16
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Preface

MATLAB is a comprehensive programming environment used by many researchers and math experts for machine learning. This book will help you learn the basic concepts in machine learning and deep learning using MATLAB, and then refine your basic skills with advanced applications.

You’ll start by exploring the tools that the MATLAB environment offers for machine learning and see how to easily interact with the MATLAB workspace. We’ll then move on to data cleansing, mining, and analyzing various types of data in machine learning, and you’ll see how to visualize data values on a graph. Then, you’ll learn about the different types of classification and regression techniques and how to apply them to your data, using MATLAB functions. Further, you will understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. You will also explore feature selection and extraction techniques for dimensionality reduction for performance improvement. Finally, you’ll learn how to leverage MATLAB tools for deep learning and managing convolutional neural networks.

By the end of the book, you’ll learn how to put it all together in real-world cases, covering major machine learning algorithms, and you’ll feel confident as you delve into machine learning with MATLAB.

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