MATLAB for Machine Learning: Unlock the power of deep learning for swift and enhanced results. 2 ed

Cover....1

Title Page....2

Copyright and Credits....2

Contributors....4

Table of Contents....8

Preface....14

Part 1: Getting Started with Matlab....22

Chapter 1: Exploring MATLAB for Machine Learning....24

Technical requirements....24

Introducing ML....25

How to define ML....25

Analysis of logical reasoning....25

Learning strategy typologies....27

Discovering the different types of learning processes....29

Supervised learning....30

Unsupervised learning....31

Reinforcement learning....32

Semi-supervised learning....33

Transfer learning....34

Using ML techniques....34

Selecting the ML paradigm....35

Step-by-step guide on how to build ML models....37

Exploring MATLAB toolboxes for ML....40

Statistics and Machine Learning Toolbox....41

Deep Learning Toolbox....42

Reinforcement Learning Toolbox....43

Computer Vision Toolbox....44

Text Analytics Toolbox....45

ML applications in real life....46

Summary....47

Chapter 2: Working with Data in MATLAB....50

Technical requirements....51

Importing data into MATLAB....51

Exploring the Import Tool....53

Using the load() function to import files....55

Reading ASCII-delimited files....57

Exporting data from MATLAB....60

Working with different types of data....63

Working with images....63

Audio data handling....64

Exploring data wrangling....65

Introducing data cleaning....66

Discovering exploratory statistics....68

EDA....69

EDA in practice....69

Introducing exploratory visualization....72

Understanding advanced data preprocessing techniques in MATLAB....73

Data normalization for feature scaling....74

Introducing correlation analysis in MATLAB....75

Summary....79

Part 2: Understanding Machine Learning Algorithms in MATLAB....82

Chapter 3: Prediction Using Classification and Regression....84

Technical requirements....84

Introducing classification methods using MATLAB....85

Decision trees for decision-making....85

Exploring decision trees in MATLAB....87

Building an effective and accurate classifier....93

SVMs explained....93

Supervised classification using SVM....96

Exploring different types of regression....99

Introducing linear regression....99

Linear regression model in MATLAB....100

Making predictions with regression analysis in MATLAB....103

Multiple linear regression with categorical predictor....103

Evaluating model performance....107

Reducing outlier effects....108

Using advanced techniques for model evaluation and selection in MATLAB....111

Understanding k-fold cross-validation....112

Exploring leave-one-out cross-validation....115

Introducing the bootstrap method....116

Summary....116

Chapter 4: Clustering Analysis and Dimensionality Reduction....118

Technical requirements....118

Understanding clustering – basic concepts and methods....119

How to measure similarity....119

How to find centroids and centers....121

How to define a grouping....122

Understanding hierarchical clustering....123

Partitioning-based clustering algorithms with MATLAB....129

Introducing the k-means algorithm....129

Using k-means in MATLAB....130

Grouping data using the similarity measures....136

Applying k-medoids in MATLAB....137

Discovering dimensionality reduction techniques....140

Introducing feature selection methods....141

Exploring feature extraction algorithms....143

Feature selection and feature extraction using MATLAB....145

Stepwise regression for feature selection....145

Carrying out PCA....150

Summary....156

Chapter 5: Introducing Artificial Neural Network Modeling....158

Technical requirements....158

Getting started with ANNs....159

Basic concepts relating to ANNs....159

Understanding how perceptrons work....161

Activation function to introduce non-linearity....162

ANN’s architecture explained....164

Training and testing an ANN model in MATLAB....165

How to train an ANN....165

Introducing the MATLAB Neural Network Toolbox....166

Understanding data fitting with ANNs....169

Discovering pattern recognition using ANNs....178

Building a clustering application with an ANN....187

Exploring advanced optimization techniques....192

Understanding SGD....193

Exploring Adam optimization....195

Introducing second-order methods....196

Summary....198

Chapter 6: Deep Learning and Convolutional Neural Networks....200

Technical requirements....201

Understanding DL basic concepts....201

Automated feature extraction....201

Training a DNN....202

Exploring DL models....203

Approaching CNNs....205

Convolutional layer....206

Pooling layer....208

ReLUs....209

FC layer....209

Building a CNN in MATLAB....210

Exploring the model’s results....218

Discovering DL architectures....222

Understanding RNNs....222

Analyzing LSTM networks....223

Introducing transformer models....225

Summary....225

Part 3: Machine Learning in Practice....228

Chapter 7: Natural Language Processing Using MATLAB....230

Technical requirements....230

Explaining NLP....231

NLA....233

NLG....234

Analyzing NLP tasks....235

Introducing automatic processing....237

Exploring corpora and word and sentence tokenizers....237

Corpora....238

Words....239

Sentence tokenize....240

Implementing a MATLAB model to label sentences....240

Introducing sentiment analysis....241

Movie review sentiment analysis....242

Using an LSTM model for label sentences....243

Understanding gradient boosting techniques....249

Approaching ensemble learning....249

Bagging definition and meaning....250

Discovering random forest....251

Boosting algorithms explained....252

Summary....254

Chapter 8: MATLAB for Image Processing and Computer Vision....256

Technical requirements....256

Introducing image processing and computer vision....257

Understanding image processing....257

Explaining computer vision....261

Exploring MATLAB tools for computer vision....262

Building a MATLAB model for object recognition....264

Introducing handwriting recognition (HWR)....265

Training and fine-tuning pretrained deep learning models in MATLAB....271

Introducing the ResNet pretrained network....272

The MATLAB Deep Network Designer app....273

Interpreting and explaining machine learning models....278

Understanding saliency maps....278

Understanding feature importance scores....279

Discovering gradient-based attribution methods....280

Summary....281

Chapter 9: Time Series Analysis and Forecasting with MATLAB....282

Technical requirements....282

Exploring the basic concepts of time series data....283

Understanding predictive forecasting....283

Introducing forecasting methodologies....284

Time series analysis....286

Extracting statistics from sequential data....290

Converting a dataset into a time series format in MATLAB....291

Understanding time series slicing....293

Resampling time series data in MATLAB....294

Moving average....295

Exponential smoothing....297

Implementing a model to predict the stock market....299

Dealing with imbalanced datasets in MATLAB....309

Understanding oversampling....310

Exploring undersampling....311

Summary....312

Chapter 10: MATLAB Tools for Recommender Systems....314

Technical requirements....314

Introducing the basic concepts of recommender systems....315

Understanding CF....315

Content-based filtering explained....316

Hybrid recommender systems....317

Finding similar users in data....318

Creating recommender systems for network intrusion detection using MATLAB....324

Recommender system for NIDS....325

NIDS using a recommender system in MATLAB....326

Deploying machine learning models....330

Understanding model compression....331

Discovering model pruning techniques....331

Introducing quantization for efficient inference on edge devices....333

Getting started with knowledge distillation....333

Learning low-rank approximation....334

Summary....335

Chapter 11: Anomaly Detection in MATLAB....336

Technical requirements....336

Introducing anomaly detection and fault diagnosis systems....337

Anomaly detection overview....337

Fault diagnosis systems explained....338

Approaching fault diagnosis using ML....340

Using ML to identify anomalous functioning....342

Anomaly detection using logistic regression....343

Improving accuracy using the Random Forest algorithm....347

Building a fault diagnosis system using MATLAB....349

Understanding advanced regularization techniques....353

Understanding dropout....353

Exploring L1 and L2 regularization....354

Introducing early stopping....355

Summary....356

Index....358

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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.

What you will learn

• Discover different ways to transform data into valuable insights
• Explore the different types of regression techniques
• Grasp the basics of classification through Naive Bayes and decision trees
• Use clustering to group data based on similarity measures
• Perform data fitting, pattern recognition, and cluster analysis
• Implement feature selection and extraction for dimensionality reduction
• Harness MATLAB tools for deep learning exploration

Who this book is for

This book is for ML engineers, data scientists, DL engineers, and CV/NLP engineers who want to use MATLAB for machine learning and deep learning. A fundamental understanding of programming concepts is necessary to get started.