Book Image

MATLAB for Machine Learning - Second Edition

By : Giuseppe Ciaburro
Book Image

MATLAB for Machine Learning - Second Edition

By: Giuseppe Ciaburro

Overview of this book

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.
Table of Contents (17 chapters)
Free Chapter
1
Part 1: Getting Started with Matlab
4
Part 2: Understanding Machine Learning Algorithms in MATLAB
9
Part 3: Machine Learning in Practice

Introducing exploratory visualization

Exploratory visualization is a crucial step in the data analysis process, allowing us to gain insights and understand underlying patterns, relationships, and trends within our data. It involves creating visual representations of the data to explore its various attributes and uncover potential patterns or anomalies. The primary goal of exploratory visualization is to visually inspect the data, identify any interesting features, and generate hypotheses for further investigation. By leveraging the power of visual perception, we can better understand complex datasets and make informed decisions. MATLAB provides a variety of functions and tools for exploratory data visualization. Here are some commonly used functions for exploratory visualization:

  • plot(): This function is used to create line plots, scatter plots, or any custom plot by specifying x and y coordinates.
  • histogram(): This function creates histograms to visualize the distribution...