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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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$49.99
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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$39.99
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Renews at $19.99p/m
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MATLAB for Machine Learning

Exploring MATLAB for Machine Learning

Machine learning (ML) is a branch of artificial intelligence that is based on the development of algorithms and mathematical models capable of learning from data and autonomously adapting to improve their performance according to a set of objectives. Thanks to this learning ability, ML is used in a wide range of applications, such as data analysis, computer vision, language modeling, speech recognition, medical diagnosis, and financial risk prediction. ML is an ever-evolving area of research and is revolutionizing many fields of science and industry. The aim of this chapter is to provide you with an introduction, background information, and a basic knowledge of ML, as well as an understanding of how to apply these concepts using MATLAB tools.

In this chapter, we’re going to cover the following main topics:

  • Introducing ML
  • Discovering the different types of learning processes
  • Using ML techniques
  • Exploring MATLAB toolboxes...

Technical requirements

In this chapter, we will introduce basic concepts relating to ML. To understand these topics, a basic knowledge of algebra and mathematical modeling is needed. A working knowledge of the MATLAB environment is also required.

Introducing ML

ML is based on the idea of providing computers with a large amount of input data, together with the corresponding correct answers or labels, and allowing them to learn from this data, identifying patterns, relationships, and regularities within them. Unlike traditional programming approaches, in which computers follow precise instructions to perform specific tasks, ML allows machines to independently learn from data and make decisions based on statistical models and predictions.

One of the key concepts of ML is the ability to generalize. This means that a model trained on information in the training dataset should be able to make accurate predictions about new data that it has never seen before. This allows ML to be applied across a wide range of domains.

How to define ML

To better understand the basic concepts of ML, we can start from the definitions formulated by the pioneers in this field. According to Arthur L. Samuel (1959) – “ML is a field...

Discovering the different types of learning processes

Learning is based on the idea that perceptions should not only guide actions but also enhance the agent’s ability to automatically learn from interactions with the world and the decision-making processes themselves. A system is considered capable of learning when it has an executive component for making decisions and a learning component for modifying the executive component to improve decisions. Learning is influenced by the components learned from the system, by the feedback received after the actions are performed, and by the type of representation used.

ML offers several ways of allowing algorithms to learn from data, which are classified into categories based on the type of feedback on which the learning system is based. Choosing which learning category to use for a specific problem must be done in advance to find the best solution. It is useful to evaluate the robustness of the algorithm, such as its ability to make...

Using ML techniques

In the previous section, we explored the various types of ML paradigms in detail. So, we have understood the basic principles that underlie the different approaches. At this point, it is necessary to understand what the elements that allow us to discriminate between the different approaches are; in other words, in this section, we will understand how to adequately choose the learning approach necessary to obtain our results.

Selecting the ML paradigm

Selecting the appropriate ML algorithm can feel overwhelming given the numerous options available, including both supervised and unsupervised approaches, each employing different learning strategies.

There is no universally superior method, nor one that fits all situations. In large part, the search for the right algorithm involves trial and error; even seasoned data scientists cannot determine whether an algorithm will work without testing it. Nonetheless, the algorithm choice is also influenced by factors...

Exploring MATLAB toolboxes for ML

Up until now, we have acquired knowledge about the functions and capabilities of ML algorithms. We have also gained an understanding of how to identify various types of algorithms, select the appropriate solution for our requirements, and establish an effective workflow. Now, it is time to delve into the process of executing these tasks within the MATLAB environment.

With MATLAB, the process of solving ML problems becomes remarkably straightforward. The comprehensive set of tools and functionalities provided by MATLAB empowers users to leverage various algorithms and techniques effortlessly. Whether you are a beginner or an experienced practitioner, MATLAB equips you with the necessary resources to dive into the world of ML with confidence.

MATLAB is a software platform specifically designed to address scientific problems and facilitate design processes. It offers an integrated environment where calculations, visualizations, and programming seamlessly...

ML applications in real life

ML, as a modern innovation, has revolutionized numerous industrial and professional processes, enhancing various aspects of our daily lives. Intelligent systems powered by ML algorithms possess the capability to learn from historical data or past experiences. By leveraging this knowledge, ML applications can generate outcomes and insights.

The fields of study in which ML is used cover many types of problems. The main ones are as follows:

  • The representation of knowledge and reasoning that aims to reproduce the way of reasoning of the human brain through the definition of symbolism and languages to create machines capable of performing automatic reasoning
  • Planning and coordination dealing with the development of systems that, given an application domain, have the objective of predicting future results and making decisions to achieve these objectives and maximize their benefits
  • Robotics, for studies related to the movement of mechanical...

Summary

In this chapter, we embarked on an exciting journey into the world of ML, exploring a range of popular algorithms to find the best fit for our specific needs. We learned the importance of conducting a preliminary analysis to determine the most suitable algorithm and gained insights into the step-by-step process of building ML models.

Furthermore, we delved into the powerful capabilities of MATLAB for ML, including its support for classification, regression, clustering, and deep learning tasks. We discovered the convenience of using MATLAB apps for automated model training and code generation, streamlining our workflow.

We also introduced the Statistics and Machine Learning Toolbox and the Deep Learning Toolbox, which provided us with additional tools and functionalities to solve our specific problems. We recognized the significance of statistics and algebra in the field of ML and understood how MATLAB could assist us in leveraging these concepts effectively.

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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
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Publication date : Jan 30, 2024
Length: 374 pages
Edition : 2nd
Language : English
ISBN-13 : 9781835087695
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Estimated delivery fee Deliver to South Korea

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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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