MATLAB & Simulink
DATA ANALYSIS AND MACHINE LEARNING
Machine Learning with MATLAB
Hands-on Course with Practical Exercises
This two-day course focuses on data analytics and machine learning techniques in MATLAB® using functionality within Statistics and Machine Learning Toolbox™ and Deep Learning Toolbox™. The course 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.
Organizing and preprocessing data
Creating classification and regression models
Interpreting and evaluating models
Simplifying data sets
Using ensembles to improve model performance
Who Should Attend
This course is intended for engineer, researchers, data scientists, and managers, who are involved in the design of intelligent systems that can automatically produce models which can analyse bigger, more complex data and deliver faster and more accurate results.
Upon the completion of the course, the participants will be able to
- use unsupervised learning techniques to discover natural pattern
- use supervised learning techniques for regression and classification
- reduce feature dimension - improve the regression and classification model
Day 1 of 2
Importing and Organizing Data
Objective: Bring data into MATLAB and organize it for analysis, including normalizing data and removing observations with missing values.
Finding Natural Patterns in Data
Objective: Use unsupervised learning techniques to group observations based on a set of explanatory variables and discover natural patterns in a data set.
Cluster evaluation and interpretation
Day 2 of 2
Improving Predictive Models
Objective: Reduce the dimensionality of a data set. Improve and simplify machine learning models.
Building Regression Models
Objective: Use supervised learning techniques to perform predictive modeling for continuous response variables.
Parametric regression methods
Nonparametric regression methods
Evaluation of regression models
Creating Neural Networks
Objective: Create ad train neural networks for clustering and predictive modeling. Adjust network architecture to improve performance.
Clustering with Self-Organizing Maps
Classification with feed-forward networks
Regression with feed-forward networks