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MATLAB for Machine Learning

MATLAB for Machine Learning

By : Giuseppe Ciaburro
4.8 (4)
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MATLAB for Machine Learning

MATLAB for Machine Learning

4.8 (4)
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)
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1
Part 1: Getting Started with Matlab
4
Part 2: Understanding Machine Learning Algorithms in MATLAB
9
Part 3: Machine Learning in Practice

Summary

In this chapter, we explored several key concepts in the field of data analysis and predictive modeling. We started by discussing the basics of time series data, which refers to data that is collected over a certain period and contains a sequential order. The extraction of statistics from such sequential data is then highlighted as an important step in analyzing and understanding patterns within the data.

This chapter also emphasized the implementation of a model to predict stock market data. This involves using various techniques and algorithms to analyze historical stock market data, identify patterns and trends, and make predictions about future stock prices.

Lastly, this chapter addressed the challenge of dealing with imbalanced datasets in MATLAB. An imbalanced dataset refers to a situation where the distribution of classes within the dataset is significantly skewed, making it difficult to train a model accurately. We discussed methods and strategies to handle imbalanced...

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