- Knowledge of basic mathematical concepts such as linear algebra, probability theory and statistics
- No previous experience with MATLAB is needed
In the first part of this training, we cover the fundamentals of MATLAB and its function as both a language and a platform. Included in this discussion is an introduction to MATLAB syntax, arrays and matrices, data visualization, script development, and object-oriented principles.
In the second part, we demonstrate how to use MATLAB for data mining, with a focus on data modeling and machine learning.
Throughout the course, participants will put into practice the ideas learned through hands-on exercises in a lab environment. By the end of the training, participants will have a thorough grasp of MATLAB's capabilities and will be able to employ it for solving real-world data science problems.
Assessments will be conducted throughout the course to gauge progress.
Format of the course
- Course includes theoretical and practical exercises, including case discussions, sample code inspection, and hands-on implementation.
Introduction
MATLAB for data mining
Part 01: MATLAB fundamentals
Overview
MATLAB for data analysis, visualization, modeling, and programming.
Working with the MATLAB user interface
Overview of MATLAB syntax
Entering commands
Using the command line interface
Creating variables
Numeric vs character data
Analyzing vectors and matrices
Creating and manipulating
Performing calculations
Visualizing vector and matrix data
Working with data files
Importing data from Excel spreadsheets
Working with data types
Working with table data
Automating commands with scripts
Creating and running scripts
Organizing and publishing your scripts
Writing programs with branching and loops
User interaction and flow control
Writing functions
Creating and calling functions
Debugging with MATLAB Editor
Applying object-oriented programming principles to your programs
Part 02: MATLAB for Data Mining
Overview
MATLAB for data mining, machine learning and predictive analytics
Using the Statistics and Machine Learning Toolbox
Accessing data
Obtaining data from files, spreadsheets, and databases
Obtaining data from test equipment and hardware
Obtaining data from software and the Web
Preprocessing the data (reduction, transformation, feature extraction)
Principal Component Analysis (PCA) and Partial Least Squares (PLS)
Exploring the data
Identifying trends, testing hypotheses, and estimating uncertainty
Segmenting the data
Clustering with K-means
Regression Analysis
Machine learning
Supervised and unsupervised machine learning algorithms
Creating customized algorithms
Modeling the data
Generating .mat files
Creating visualizations
Publishing reports
Sharing analysis tools
As MATLAB code
As standalone desktop or Web applications
Summary and closing remarks