Prerequisites

This course is also offered in an online, self-paced format.

Self-paced courses provide active engagement with MATLAB through in-browser, hands-on exercises that you can complete anytime, anywhere, at your own pace.

Watch: The Advantages of Self-Paced Training (1:03)

Machine Learning with MATLAB

This two-day course focuses on data analytics and machine learning techniques in MATLAB^® using functionality within Statistics and Machine Learning Toolbox^™ and Neural Network 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. Topics include:

Organizing and preprocessing data
Clustering data
Creating classification and regression models
Interpreting and evaluating models
Simplifying data sets
Using ensembles to improve model performance

See detailed course outline

Prerequisites

MATLAB Fundamentals

This course is also offered in an online, self-paced format.

Self-paced courses provide active engagement with MATLAB through in-browser, hands-on exercises that you can complete anytime, anywhere, at your own pace.

Watch: The Advantages of Self-Paced Training (1:03)

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. Data types Tables Categorical data Data preparation
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. Unsupervised learning Clustering methods Cluster evaluation and interpretation
Building Classification Models	Objective: Use supervised learning techniques to perform predictive modeling for classification problems. Evaluate the accuracy of a predictive model. Supervised learning Training and validation Classification methods

Day 2 of 2
Improving Predictive Models	Objective: Reduce the dimensionality of a data set. Improve and simplify machine learning models. Cross validation Feature transformation Feature selection Ensemble learning
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 and 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

Guaranteed to Run

When you register for one of these courses, you can rely on the fact that it won't be canceled or rescheduled for any reason.

Course Schedule

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Dates	Location	Language	Price	Register
On Demand	Self-Paced 180 days of full access from the day of purchase	English	USD 350

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