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

Discovering exploratory statistics

Exploratory statistics refers to the initial phase of data analysis where various statistical techniques are employed to understand the main characteristics of a dataset. There are many techniques available, but the most used one is the following.

EDA

EDA is an approach to analyzing data that focuses on understanding the main characteristics, patterns, and relationships within a dataset. It involves using statistical techniques and visualizations to summarize and explore the data to gain insights and formulate hypotheses. Here are some key steps and techniques involved in EDA:

  • Data summary: Start by examining the basic summary statistics of the dataset, such as the mean, median, standard deviation, minimum, maximum, and so on. This gives an initial understanding of the central tendency, spread, and distribution of the data.
  • Data distribution: Examine the distribution of individual variables to identify any skewness or non-normality...