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.
Course Schedule
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.
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
Machine Learning with MATLAB
Prerequisites
| 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.
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| 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.
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| Building Classification Models | Objective: Use supervised learning techniques to perform predictive modeling for classification problems. Evaluate the accuracy of a predictive model.
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| Day 2 of 2 | |
|---|---|
| Improving Predictive Models | Objective: Reduce the dimensionality of a data set. Improve and simplify machine learning models.
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| Building Regression Models | Objective: Use supervised learning techniques to perform predictive modeling for continuous response variables.
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| Creating Neural Networks | Objective: Create and train neural networks for clustering and predictive modeling. Adjust network architecture to improve performance.
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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 |
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