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

Time Series Analysis and Forecasting with MATLAB

Time series data constitutes a sequence of measurements gathered over a certain period. These measurements, which are tied to a specific variable, occur at regular intervals. An essential characteristic of time series data lies in the significance of its order; the arrangement of observations on a timeline conveys meaningful patterns. Altering this order can completely reshape the data’s meaning. Sequential data is a broader concept that encompasses any data presented in a sequential manner, which includes time series data. In this chapter, we will delve into the fundamental concepts surrounding sequential data, elucidating how to construct models that capture patterns within time series or any sequential data.

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

  • Exploring the basic concepts of time series data
  • Extracting statistics from sequential data
  • Implementing a model to predict stock...