This is the code repository for Getting Started with MATLAB Machine Learning [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
MATLAB is the language of choice for many researchers and mathematics experts when it comes to machine learning. This video will help beginners build a foundation in machine learning using MATLAB. You'll start by getting your system ready with the MATLAB environment for machine learning and you'll see how to easily interact with the MATLAB workspace. You'll then move on to data cleansing, mining, and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll learn about the different types of regression technique and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction to improve performance. By the end of the video, you'll have learned to put it all together via real-world use cases covering the major machine learning algorithms and will be comfortable in performing machine learning with MATLAB.
- Discover different ways to transform data using SAS XPORT, import, and export tools.
- Discover the basics of classification methods and how to implement the Naive Bayes algorithm and Decision Trees in the MATLAB environment.
- Use clustering methods such as hierarchical clustering to group data using similarity measures.
- Perform data fitting, pattern recognition, and clustering analysis with the help of the MATLAB Neural Network Toolbox.
- Use feature selection and extraction for dimensionality reduction, leading to improved performance.
To fully benefit from the coverage included in this course, you will need:
This video is for data analysts, data scientists, students, or anyone keen to get started with machine learning and build efficient data processing and predictive applications. A mathematical and statistical background will really make this video easier to follow.
This course has the following software requirements:
- 4GB Ram
- 500 GB Harddisk Storage
- 2 GB Graphic Memory