MATLAB for Machine Learning
Packt Publishing Limited (Verlag)
978-1-83508-769-5 (ISBN)
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 DescriptionDiscover 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 forThis 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.
Giuseppe Ciaburro holds a PhD and two master's degrees. He works at the Built Environment Control Laboratory - Università degli Studi della Campania "Luigi Vanvitelli". He has over 25 years of work experience in programming, first in the field of combustion and then in acoustics and noise control. His core programming knowledge is in MATLAB, Python and R. As an expert in AI applications to acoustics and noise control problems, Giuseppe has wide experience in researching and teaching. He has several publications to his credit: monographs, scientific journals, and thematic conferences. He was recently included in the world's top 2% scientists list by Stanford University (2022).
Table of Contents
Exploring MATLAB for Machine Learning
Working with Data in MATLAB
Prediction Using Classification and Regression
Clustering Analysis and Dimensionality Reduction
Introducing Artificial Neural Networks Modeling
Deep Learning and Convolutional Neural Networks
Natural Language Processing Using MATLAB
MATLAB for Image Processing and Computer Vision
Time Series Analysis and Forecasting with MATLAB
MATLAB Tools for Recommender Systems
Anomaly Detection in MATLAB
| Erscheinungsdatum | 28.12.2023 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Themenwelt | Informatik ► Software Entwicklung ► User Interfaces (HCI) |
| Mathematik / Informatik ► Informatik ► Theorie / Studium | |
| Technik ► Elektrotechnik / Energietechnik | |
| ISBN-10 | 1-83508-769-8 / 1835087698 |
| ISBN-13 | 978-1-83508-769-5 / 9781835087695 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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