The guide covers a wide range of topics such as linear regression, logistic regression, decision trees, clustering, and neural networks. It also discusses the use of toolboxes for specific applications such as bioinformatics, finance, and image processing. The book is divided into several chapters, each focusing on a different aspect of statistics and machine learning. Chapter 1 provides an overview of the MATLAB environment and its capabilities for statistical analysis and machine learning. Chapter 2 covers data preprocessing techniques, including data cleaning, transformation, and feature extraction. Chapter 3 introduces linear regression and logistic regression, including model development and evaluation. Chapter 4 explores decision trees and their applications in machine learning. Chapter 5 discusses clustering methods and their uses in data segmentation and classification. Chapter 6 delves into neural networks and deep learning techniques for more advanced applications. Finally, Chapter 7 provides a comprehensive case study demonstrating the practical application of MATLAB in real-world scenarios. The book is written in a clear and concise manner, making it accessible to both beginners and experienced users of MATLAB.

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