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 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. 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.

Details and launch

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 Hyperparameter optimization 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

Results 1 - 12 of 12

Dates	Location	Language	Price
On Demand	Self-Paced 180 days of full access from the day of purchase	English	USD 350
22 Feb 2022- 23 Feb 2022	Virtual 9:00 am - 5:00 pm China Standard Time	Chinese	USD 1,500
01 Mar 2022- 02 Mar 2022	Virtual 9:00 am - 5:00 pm Central European Time	English	USD 1,500
10 Mar 2022- 11 Mar 2022	Virtual 9:00 am - 5:00 pm Japan Standard Time	Japanese	USD 1,500
22 Mar 2022- 23 Mar 2022	Virtual 9:00 am - 5:00 pm Australian Eastern Daylight Time	English	USD 1,500
19 Apr 2022- 20 Apr 2022	Virtual 9:00 am - 5:00 pm US Pacific Daylight Time	English	USD 1,500
26 Apr 2022- 27 Apr 2022	Virtual 9:00 am - 5:00 pm Korea Standard Time	Korean	USD 1,500
05 May 2022- 06 May 2022	Virtual 9:00 am - 5:00 pm Australian Eastern Standard Time	English	USD 1,500
10 May 2022- 11 May 2022	Virtual 9:00 am - 5:00 pm Central European Summer Time	German	USD 1,500
16 Jun 2022- 17 Jun 2022	Virtual 9:00 am - 5:00 pm Japan Standard Time	Japanese	USD 1,500
21 Jun 2022- 22 Jun 2022	Virtual 9:00 am - 5:00 pm Central European Summer Time	French	USD 1,500
23 Jun 2022- 24 Jun 2022	Virtual 9:00 am - 5:00 pm Korea Standard Time	Korean	USD 1,500

Results 1 - 12 of 12

The pricing applies for purchase and use in United States, For pricing in other regions Contact Sales. The product price does not include sales, use, excise, value-added, or other taxes. Any applicable taxes, duties, levies, assessments and governmental charges payable in connection with this purchase will be assessed on the order. Refer to Training Policies for more information