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:
|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.
|Building Classification Models||
Objective: Use supervised learning techniques to perform predictive modeling for classification problems. Evaluate the accuracy of a predictive model.
|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.
|Creating Neural Networks||
Objective: Create and train neural networks for clustering and predictive modeling. Adjust network architecture to improve performance.
See if you are eligible for discounted pricing for academic users.
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.
180 days of full access from the day of purchase
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You are eligible for discounted academic pricing when you use MATLAB and Simulink for teaching, academic research, or for meeting course requirements at a degree granting institution.
You are not eligible for academic pricing when you use MATLAB and Simulink at a commercial or government lab, or for other commercial or industrial purposes.
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