MATLAB for Machine Learning - Second Edition
Data Science
Book Details
Book Title
MATLAB for Machine Learning - Second Edition
Author
Giuseppe Ciaburro
Publisher
Packt Publishing
Publication Date
2024
ISBN
9781835087695
Number of Pages
374
Language
English
Format
File Size
3.24MB
Subject
Machine Learning
Table of 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
- Discovering the different types of learning processes
- Using ML techniques
- Exploring MATLAB toolboxes for ML
- ML applications in real life
- Summary
- 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
- Part 2: Understanding Machine Learning Algorithms in MATLAB
- 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
- 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
- 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
- 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
- Part 3: Machine Learning in Practice
- 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
- 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
- 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
- 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
- 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
- Index
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