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MATLAB for Machine Learning
MATLAB for Machine Learning

MATLAB for Machine Learning: Unlock the power of deep learning for swift and enhanced results , Second Edition

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Profile Icon Giuseppe Ciaburro
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (4 Ratings)
Paperback Jan 2024 374 pages 2nd Edition
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Arrow left icon
Profile Icon Giuseppe Ciaburro
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (4 Ratings)
Paperback Jan 2024 374 pages 2nd Edition
eBook
Mex$573.99 Mex$820.99
Paperback
Mex$1025.99
Subscription
Free Trial
eBook
Mex$573.99 Mex$820.99
Paperback
Mex$1025.99
Subscription
Free Trial

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MATLAB for Machine Learning

Part 1: Getting Started with Matlab

This part provides background information and essential knowledge about MATLAB tools, along with an introduction to basic machine learning concepts. We also will focus on importing and organizing data in MATLAB, emphasizing familiarity with the MATLAB workspace for simplicity in operations. The discussion covers analyzing various data formats, moving data in and out of MATLAB, exploring datatypes for grouping variables and categorical data, exporting data in different formats such as cell arrays, structure arrays, and tabular data, and saving it in MATLAB-supported file formats. The ultimate goal is to prepare data in the right format for the subsequent phase of data analysis.

This part has the following chapters:

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Key benefits

  • 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

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.

Who is this book 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.

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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jan 30, 2024
Length: 374 pages
Edition : 2nd
Language : English
ISBN-13 : 9781835087695
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Product Details

Publication date : Jan 30, 2024
Length: 374 pages
Edition : 2nd
Language : English
ISBN-13 : 9781835087695
Category :
Languages :
Concepts :
Tools :

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Table of Contents

16 Chapters
Part 1: Getting Started with Matlab Chevron down icon Chevron up icon
Chapter 1: Exploring MATLAB for Machine Learning Chevron down icon Chevron up icon
Chapter 2: Working with Data in MATLAB Chevron down icon Chevron up icon
Part 2: Understanding Machine Learning Algorithms in MATLAB Chevron down icon Chevron up icon
Chapter 3: Prediction Using Classification and Regression Chevron down icon Chevron up icon
Chapter 4: Clustering Analysis and Dimensionality Reduction Chevron down icon Chevron up icon
Chapter 5: Introducing Artificial Neural Network Modeling Chevron down icon Chevron up icon
Chapter 6: Deep Learning and Convolutional Neural Networks Chevron down icon Chevron up icon
Part 3: Machine Learning in Practice Chevron down icon Chevron up icon
Chapter 7: Natural Language Processing Using MATLAB Chevron down icon Chevron up icon
Chapter 8: MATLAB for Image Processing and Computer Vision Chevron down icon Chevron up icon
Chapter 9: Time Series Analysis and Forecasting with MATLAB Chevron down icon Chevron up icon
Chapter 10: MATLAB Tools for Recommender Systems Chevron down icon Chevron up icon
Chapter 11: Anomaly Detection in MATLAB Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
(4 Ratings)
5 star 75%
4 star 25%
3 star 0%
2 star 0%
1 star 0%
H2N Mar 11, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Just a few books using Matlab now on the market. The book is recommended for ML engineers and data scientists who wants to use MATLAB for machine learning and deep learning. The book starts with an introduction to ML in MATLAB, followed by chapters on data management, classification and regression, clustering analysis, dimensionality reduction, deep learning, natural language processing, image processing, time series analysis, recommender systems and anomaly detection. Each chapter provides practical examples in ML and DL.
Amazon Verified review Amazon
Alex Jun 24, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book provides an easy to follow path in the complicated world of ML. It provides the background, balanced between code examples, the fundamentals and math, filling potential gaps for the readers, before going deeper into a very diverse set of capabilities. The book discusses computer vision, language model, regression and other application, touching on the theoretical sides of supervised learning and unsupervised learning, and provides basic coding examples, that combine into a very comprehensive list of solutions to real-world problems.The book is recommended to previous Matlab users that seek an entry point in the ML world, or new users where the book will provide a great starting point to their Matlab experience.
Amazon Verified review Amazon
Om S Mar 12, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
MATLAB for Machine Learning is a easy to follow book for anyone keen on learning about machine learning with MATLAB. It's packed with practical examples and clear explanations, making complex concepts easy to grasp. With step-by-step guidance, it's suitable for beginners and experienced learners alike. It's a valuable resource for those looking to apply machine learning techniques in real-world scenarios.Ideal for ML engineers, data scientists, DL engineers, and CV/NLP engineers interested in using MATLAB for machine learning and deep learning. Basic programming knowledge is necessary to get started. Happy MATLAB.
Amazon Verified review Amazon
ivan Feb 25, 2024
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
The book "MATLAB for Machine Learning" by Dr. Guiseppe Ciaburro is written lucidly and easy to follow. If you have a Matlab background and are interested in developing programming skills around machine learning using Matlab, this book is an excellent guide. It begins with a nice description of machine learning and the different learning processes necessary to build machine learning models (i.e., clustering, dimensionality reduction, classification, regression). It also provides examples of how to work with data in different formats and do exploratory data analysis. A nice touch is the chapters that introduce neural networks and deep learning. From there, the book takes you through practical case scenarios to dive deep into how you can use machine learning to solve these challenges. Since this book offers many exciting ideas and examples of leveraging Matlab for machine learning, I recommend it to those interested in honing their programmatic skills in Matlab.
Amazon Verified review Amazon
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