Number of pages:
- Utilizing development tools
- Verifying application behavior
- Creating robust applications
- Structuring code
- Structuring data
- Managing data efficiently
- Creating a toolbox
This book provides hands-on experience using the features in the MATLAB language to write efficient, robust, and well-organized code. These concepts form the foundation for writing full applications, developing algorithms, and extending built-in MATLAB capabilities. Details of performance optimization, as well as tools for writing, debugging, and profiling code are covered.
This book focuses on data analytics and machine learning techniques in MATLAB using functionality within Statistics and Machine Learning Toolbox and Neural Network Toolbox. The book demonstrates the use of unsupervised learning to discover features in large data sets and supervised learning to build predictive models. Examples and exercises highlight techniques for visualization and evaluation of results.
This book focuses on importing and preparing data for data analytics applications. The book is intended for data analysts and data scientists who need to automate the processing, analysis, and visualization of data from multiple sources.