Apply different types of machine learning models for clustering, classification, and regression in MATLABĀ®. Explore how different techniques can optimize your model performance.
Get an overview of the course. Import and process data, explore data features, and train and evaluate a classification model.
Use unsupervised learning techniques to group observations based on a set of explanatory variables and discover natural patterns in a data set.
Use available classification methods to train data classification models. Make predictions and evaluate the accuracy of a predictive model.
Validate model performance. Optimize model properties. Reduce the dimensionality of a data set and simplify machine learning models.
Use supervised learning techniques to perform predictive modeling for continuous response variables.
Learn next steps and give feedback on the course.
Format:Self-paced
Language:English
Learn core MATLAB functionality for data analysis, modeling, and programming.
Learn the basics of practical machine learning methods for classification problems.
Learn the theory and practice of building deep neural networks with real-life image and sequence data.
Get started quickly with the basics of MATLAB.
Learn core MATLAB functionality for data analysis, modeling, and programming.
Learn the basics of practical machine learning methods for classification problems.