Chapter 1, Getting Started with MATLAB Machine Learning, introduces the basic concepts of machine learning, and then we take a tour of the different types of algorithms. In addition, some introduction, background information, and basic knowledge of the MATLAB environment will be covered. Finally, we explore the essential tools that MATLAB provides for understanding the amazing world of machine learning.
Chapter 2, Importing and Organizing Data in MATLAB, teaches us how to import and organize our data in MATLAB. Then we analyze the different formats available for the data collected and see how to move data in and out of MATLAB. Finally, we learn how to organize the data in the correct format for the next phase of data analysis.
Chapter 3, From Data to Knowledge Discovery, is where we begin to analyze data to extract useful information. We start from an analysis of the basic types of variable and the degree of cleaning the data. We analyze the techniques available for the preparation of the most suitable data for analysis and modeling. Then we go to data visualization, which plays a key role in understanding the data.
Chapter 4, Finding Relationships between Variables - Regression Techniques, shows how to perform accurate regression analysis in the MATLAB environment. We explore the amazing MATLAB interface for regression analysis, including fitting, prediction, and plotting.
Chapter 5, Pattern Recognition through Classification Algorithms, covers classification and much more. You’ll learn how to classify an object using nearest neighbors. You'll understand how to use the principles of probability for classification. We'll also cover classification techniques using decision trees and rules.
Chapter 6, Identifying Groups of Data Using Clustering Methods, shows you how to divide the data into clusters, or groupings of similar items. You'll learn how to find groups of data with k-means and k-medoids. We'll also cover grouping techniques using hierarchical clustering.
Chapter 7, Simulation of Human Thinking - Artificial Neural Networks, teaches you how to use a neural network to fit data, classify patterns, and do clustering. You’ll learn preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance.
Chapter 8, Improves the Performance of the Machine Learning Model - Dimensionality Reduction, shows you how to select a feature that best represents the set of data. You will learn feature extraction techniques for dimensionality reduction when the transformation of variables is possible.
Chapter 9, Machine Learning in Practice, starts with a real-world fitting problem. Then you’ll learn how to use a neural network to classify patterns. Finally, we perform clustering analysis. In this way, we’ll analyze supervised and unsupervised learning algorithms.