book

MATLAB for Machine Learning - Second Edition

Name: MATLAB for Machine Learning - Second Edition
Author: Giuseppe Ciaburro
ISBN: 9781835087695

by Giuseppe Ciaburro

January 2024

Intermediate to advanced

374 pages

11h 5m

English

Packt Publishing

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MATLAB for Machine Learning
ContributorsAbout the authorAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesConventions usedGet in touchShare Your ThoughtsDownload a free PDF copy of this book
Part 1: Getting Started with Matlab
Chapter 1: Exploring MATLAB for Machine Learning
Technical requirementsIntroducing MLHow to define MLAnalysis of logical reasoningLearning strategy typologiesDiscovering the different types of learning processesSupervised learningUnsupervised learningReinforcement learningSemi-supervised learningTransfer learningUsing ML techniquesSelecting the ML paradigmStep-by-step guide on how to build ML modelsExploring MATLAB toolboxes for MLStatistics and Machine Learning ToolboxDeep Learning ToolboxReinforcement Learning ToolboxComputer Vision ToolboxText Analytics ToolboxML applications in real lifeSummary
Chapter 2: Working with Data in MATLAB
Technical requirementsImporting data into MATLABExploring the Import ToolUsing the load() function to import filesReading ASCII-delimited filesExporting data from MATLABWorking with different types of dataWorking with imagesAudio data handlingExploring data wranglingIntroducing data cleaningDiscovering exploratory statisticsEDAEDA in practiceIntroducing exploratory visualizationUnderstanding advanced data preprocessing techniques in MATLABData normalization for feature scalingIntroducing correlation analysis in MATLABSummary
Part 2: Understanding Machine Learning Algorithms in MATLAB
Chapter 3: Prediction Using Classification and Regression
Technical requirementsIntroducing classification methods using MATLABDecision trees for decision-makingExploring decision trees in MATLABBuilding an effective and accurate classifierSVMs explainedSupervised classification using SVMExploring different types of regressionIntroducing linear regressionLinear regression model in MATLABMaking predictions with regression analysis in MATLABMultiple linear regression with categorical predictorEvaluating model performanceReducing outlier effectsUsing advanced techniques for model evaluation and selection in MATLABUnderstanding k-fold cross-validationExploring leave-one-out cross-validationIntroducing the bootstrap methodSummary
Chapter 4: Clustering Analysis and Dimensionality Reduction
Technical requirementsUnderstanding clustering – basic concepts and methodsHow to measure similarityHow to find centroids and centersHow to define a groupingUnderstanding hierarchical clusteringPartitioning-based clustering algorithms with MATLABIntroducing the k-means algorithmUsing k-means in MATLABGrouping data using the similarity measuresApplying k-medoids in MATLABDiscovering dimensionality reduction techniquesIntroducing feature selection methodsExploring feature extraction algorithmsFeature selection and feature extraction using MATLABStepwise regression for feature selectionCarrying out PCASummary
Chapter 5: Introducing Artificial Neural Network Modeling
Technical requirementsGetting started with ANNsBasic concepts relating to ANNsUnderstanding how perceptrons workActivation function to introduce non-linearityANN’s architecture explainedTraining and testing an ANN model in MATLABHow to train an ANNIntroducing the MATLAB Neural Network ToolboxUnderstanding data fitting with ANNsDiscovering pattern recognition using ANNsBuilding a clustering application with an ANNExploring advanced optimization techniquesUnderstanding SGDExploring Adam optimizationIntroducing second-order methodsSummary
Chapter 6: Deep Learning and Convolutional Neural Networks
Technical requirementsUnderstanding DL basic conceptsAutomated feature extractionTraining a DNNExploring DL modelsApproaching CNNsConvolutional layerPooling layerReLUsFC layerBuilding a CNN in MATLABExploring the model’s resultsDiscovering DL architecturesUnderstanding RNNsAnalyzing LSTM networksIntroducing transformer modelsSummary

Part 3: Machine Learning in Practice
Chapter 7: Natural Language Processing Using MATLAB
Technical requirementsExplaining NLPNLANLGAnalyzing NLP tasksIntroducing automatic processingExploring corpora and word and sentence tokenizersCorporaWordsSentence tokenizeImplementing a MATLAB model to label sentencesIntroducing sentiment analysisMovie review sentiment analysisUsing an LSTM model for label sentencesUnderstanding gradient boosting techniquesApproaching ensemble learningBagging definition and meaningDiscovering random forestBoosting algorithms explainedSummary
Chapter 8: MATLAB for Image Processing and Computer Vision
Technical requirementsIntroducing image processing and computer visionUnderstanding image processingExplaining computer visionExploring MATLAB tools for computer visionBuilding a MATLAB model for object recognitionIntroducing handwriting recognition (HWR)Training and fine-tuning pretrained deep learning models in MATLABIntroducing the ResNet pretrained networkThe MATLAB Deep Network Designer appInterpreting and explaining machine learning modelsUnderstanding saliency mapsUnderstanding feature importance scoresDiscovering gradient-based attribution methodsSummary
Chapter 9: Time Series Analysis and Forecasting with MATLAB
Technical requirementsExploring the basic concepts of time series dataUnderstanding predictive forecastingIntroducing forecasting methodologiesTime series analysisExtracting statistics from sequential dataConverting a dataset into a time series format in MATLABUnderstanding time series slicingResampling time series data in MATLABMoving averageExponential smoothingImplementing a model to predict the stock marketDealing with imbalanced datasets in MATLABUnderstanding oversamplingExploring undersamplingSummary
Chapter 10: MATLAB Tools for Recommender Systems
Technical requirementsIntroducing the basic concepts of recommender systemsUnderstanding CFContent-based filtering explainedHybrid recommender systemsFinding similar users in dataCreating recommender systems for network intrusion detection using MATLABRecommender system for NIDSNIDS using a recommender system in MATLABDeploying machine learning modelsUnderstanding model compressionDiscovering model pruning techniquesIntroducing quantization for efficient inference on edge devicesGetting started with knowledge distillationLearning low-rank approximationSummary
Chapter 11: Anomaly Detection in MATLAB
Technical requirementsIntroducing anomaly detection and fault diagnosis systemsAnomaly detection overviewFault diagnosis systems explainedApproaching fault diagnosis using MLUsing ML to identify anomalous functioningAnomaly detection using logistic regressionImproving accuracy using the Random Forest algorithmBuilding a fault diagnosis system using MATLABUnderstanding advanced regularization techniquesUnderstanding dropoutExploring L1 and L2 regularizationIntroducing early stoppingSummary
Index
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Overview

"MATLAB for Machine Learning" is your comprehensive guide to leveraging MATLAB's powerful tools and toolbox for machine learning and deep learning tasks. Through this book, you will explore practical applications and processes that streamline the development of machine learning models while tackling real-world problems effectively.

What this Book will help me do

Gain proficiency in utilizing MATLAB's Machine Learning Toolbox for developing machine learning algorithms.
Learn how to handle data preprocessing, from data cleansing to visualization, within MATLAB.
Explore and implement foundational to advanced machine learning techniques, such as classification and regression models.
Comprehend and apply the principles of neural networks for pattern recognition and cluster analysis.
Dive into advanced concepts of deep learning, including convolutional networks, natural language processing, and time series analysis, using MATLAB's inbuilt functionality.

Author(s)

Giuseppe Ciaburro is an expert in the field of machine learning and MATLAB programming. With a robust academic background in data science and years of experience in applying these principles across domains, Giuseppe provides a clear and approachable pathway for learners in his writing.

Who is it for?

This book is ideal for machine learning professionals, data scientists, and engineers specializing in fields such as deep learning, computer vision, and natural language processing. It is suitable for those with a fundamental understanding of programming concepts who seek to apply MATLAB in solving complex learning problems. A prior familiarity with MATLAB basics will be advantageous.

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