Extract patterns and knowledge from your data in easy way using MATLAB
About This Book
- Get your first steps into machine learning with the help of this easy-to-follow guide
- Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB
- Understand how your data works and identify hidden layers in the data with the power of machine learning.
Who This Book Is For
This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well.
What You Will Learn
- Learn the introductory concepts of machine learning.
- Discover different ways to transform data using SAS XPORT, import and export tools,
- Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data.
- Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment.
- Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures.
- Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox.
- Learn feature selection and extraction for dimensionality reduction leading to improved performance.
MATLAB is the language of choice
Table of Contents
Chapter 1. Getting Started With Matlab Machine Learning
Chapter 2. Importing And Organizing Data In Matlab
Chapter 3. From Data To Knowledge Discovery
Chapter 4. Finding Relationships Between Variables - Regression Techniques
Chapter 5. Pattern Recognition Through Classification Algorithms
Chapter 6. Identifying Groups Of Data Using Clustering Methods
Chapter 7. Simulation Of Human Thinking - Artificial Neural Networks
Chapter 8. Improving The Performance Of The Machine Learning Model - Dimensionality Reduction
Chapter 9. Machine Learning In Practice