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

Exploring data wrangling

Data wrangling, also known as data munging or data preprocessing, refers to the process of cleaning, transforming, and preparing raw data for analysis. It involves several tasks, such as handling missing or inconsistent data, removing duplicates, reshaping data formats, and merging multiple datasets. Common techniques used in data wrangling include the following:

  • Data cleaning: Identifying and handling missing values, outliers, and errors in the dataset. This may involve imputing missing values, removing outliers, or correcting errors.
  • Data transformation: Modifying the structure or format of the data to make it compatible with the desired analysis or modeling techniques. This can include tasks such as changing variable types, scaling numerical values, or encoding categorical variables.
  • Data integration: Combining multiple datasets or data sources into a single unified dataset. This may involve joining datasets based on common variables or merging...