MATLAB for Machine Learning : Unlock the Power of Deep Learning for Swift and Enhanced Results / Giuseppe Ciaburro.

Format:

Book

Author/Creator:

Ciaburro, Giuseppe, author.

Language:

English

Subjects (All):

Machine learning.

Computer programming.

MATLAB.

Physical Description:

1 online resource (374 pages)

Edition:

Second edition.

Place of Publication:

Birmingham, England : Packt Publishing, [2024]

Summary:

Master MATLAB tools for creating machine learning applications through effective code writing, guided by practical examples showcasing the versatility of machine learning in real-world applications Key Features Work with the MATLAB Machine Learning Toolbox to implement a variety of machine learning algorithms Evaluate, deploy, and operationalize your custom models, incorporating bias detection and pipeline monitoring Uncover effective approaches to deep learning for computer vision, time series analysis, and forecasting Purchase of the print or Kindle book includes a free PDF eBook Book Description 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.

Contents:

Cover

Title Page

Copyright and Credits

Contributors

Table of Contents

Preface

Part 1: Getting Started with Matlab

Chapter 1: Exploring MATLAB for Machine Learning

Technical requirements

Introducing ML

How to define ML

Analysis of logical reasoning

Learning strategy typologies

Discovering the different types of learning processes

Supervised learning

Unsupervised learning

Reinforcement learning

Semi-supervised learning

Transfer learning

Using ML techniques

Selecting the ML paradigm

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

Exploring MATLAB toolboxes for ML

Statistics and Machine Learning Toolbox

Deep Learning Toolbox

Reinforcement Learning Toolbox

Computer Vision Toolbox

Text Analytics Toolbox

ML applications in real life

Summary

Chapter 2: Working with Data in MATLAB

Importing data into MATLAB

Exploring the Import Tool

Using the load() function to import files

Reading ASCII-delimited files

Exporting data from MATLAB

Working with different types of data

Working with images

Audio data handling

Exploring data wrangling

Introducing data cleaning

Discovering exploratory statistics

EDA

EDA in practice

Introducing exploratory visualization

Understanding advanced data preprocessing techniques in MATLAB

Data normalization for feature scaling

Introducing correlation analysis in MATLAB

Part 2: Understanding Machine Learning Algorithms in MATLAB

Chapter 3: Prediction Using Classification and Regression

Introducing classification methods using MATLAB

Decision trees for decision-making

Exploring decision trees in MATLAB

Building an effective and accurate classifier

SVMs explained

Supervised classification using SVM.

Exploring different types of regression

Introducing linear regression

Linear regression model in MATLAB

Making predictions with regression analysis in MATLAB

Multiple linear regression with categorical predictor

Evaluating model performance

Reducing outlier effects

Using advanced techniques for model evaluation and selection in MATLAB

Understanding k-fold cross-validation

Exploring leave-one-out cross-validation

Introducing the bootstrap method

Chapter 4: Clustering Analysis and Dimensionality Reduction

Understanding clustering - basic concepts and methods

How to measure similarity

How to find centroids and centers

How to define a grouping

Understanding hierarchical clustering

Partitioning-based clustering algorithms with MATLAB

Introducing the k-means algorithm

Using k-means in MATLAB

Grouping data using the similarity measures

Applying k-medoids in MATLAB

Discovering dimensionality reduction techniques

Introducing feature selection methods

Exploring feature extraction algorithms

Feature selection and feature extraction using MATLAB

Stepwise regression for feature selection

Carrying out PCA

Chapter 5: Introducing Artificial Neural Network Modeling

Getting started with ANNs

Basic concepts relating to ANNs

Understanding how perceptrons work

Activation function to introduce non-linearity

ANN's architecture explained

Training and testing an ANN model in MATLAB

How to train an ANN

Introducing the MATLAB Neural Network Toolbox

Understanding data fitting with ANNs

Discovering pattern recognition using ANNs

Building a clustering application with an ANN

Exploring advanced optimization techniques

Understanding SGD

Exploring Adam optimization.

Introducing second-order methods

Chapter 6: Deep Learning and Convolutional Neural Networks

Understanding DL basic concepts

Automated feature extraction

Training a DNN

Exploring DL models

Approaching CNNs

Convolutional layer

Pooling layer

ReLUs

FC layer

Building a CNN in MATLAB

Exploring the model's results

Discovering DL architectures

Understanding RNNs

Analyzing LSTM networks

Introducing transformer models

Part 3: Machine Learning in Practice

Chapter 7: Natural Language Processing Using MATLAB

Explaining NLP

NLA

NLG

Analyzing NLP tasks

Introducing automatic processing

Exploring corpora and word and sentence tokenizers

Corpora

Words

Sentence tokenize

Implementing a MATLAB model to label sentences

Introducing sentiment analysis

Movie review sentiment analysis

Using an LSTM model for label sentences

Understanding gradient boosting techniques

Approaching ensemble learning

Bagging definition and meaning

Discovering random forest

Boosting algorithms explained

Chapter 8: MATLAB for Image Processing and Computer Vision

Introducing image processing and computer vision

Understanding image processing

Explaining computer vision

Exploring MATLAB tools for computer vision

Building a MATLAB model for object recognition

Introducing handwriting recognition (HWR)

Training and fine-tuning pretrained deep learning models in MATLAB

Introducing the ResNet pretrained network

The MATLAB Deep Network Designer app

Interpreting and explaining machine learning models

Understanding saliency maps

Understanding feature importance scores

Discovering gradient-based attribution methods

Summary.

Chapter 9: Time Series Analysis and Forecasting with MATLAB

Exploring the basic concepts of time series data

Understanding predictive forecasting

Introducing forecasting methodologies

Time series analysis

Extracting statistics from sequential data

Converting a dataset into a time series format in MATLAB

Understanding time series slicing

Resampling time series data in MATLAB

Moving average

Exponential smoothing

Implementing a model to predict the stock market

Dealing with imbalanced datasets in MATLAB

Understanding oversampling

Exploring undersampling

Chapter 10: MATLAB Tools for Recommender Systems

Introducing the basic concepts of recommender systems

Understanding CF

Content-based filtering explained

Hybrid recommender systems

Finding similar users in data

Creating recommender systems for network intrusion detection using MATLAB

Recommender system for NIDS

NIDS using a recommender system in MATLAB

Deploying machine learning models

Understanding model compression

Discovering model pruning techniques

Introducing quantization for efficient inference on edge devices

Getting started with knowledge distillation

Learning low-rank approximation

Chapter 11: Anomaly Detection in MATLAB

Introducing anomaly detection and fault diagnosis systems

Anomaly detection overview

Fault diagnosis systems explained

Approaching fault diagnosis using ML

Using ML to identify anomalous functioning

Anomaly detection using logistic regression

Improving accuracy using the Random Forest algorithm

Building a fault diagnosis system using MATLAB

Understanding advanced regularization techniques

Understanding dropout

Exploring L1 and L2 regularization.

Introducing early stopping

Index

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Notes:

Includes index.

Description based on print version record.

ISBN:

9781835089538

1835089534

OCLC:

1420913563