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.

Lessons:

- Course Overview

- Review - Machine Learning Onramp

Use unsupervised learning techniques to group observations based on a set of explanatory variables and discover natural patterns in a data set.

Lessons:

- Course Example - Grouping Basketball Players

- Low Dimensional Visualization

- k-Means Clustering

- Gaussian Mixture Models

- Interpreting the Clusters

- Hierarchical Clustering

- Project - Clustering

Use available classification methods to train data classification models. Make predictions and evaluate the accuracy of a predictive model.

Lessons:

- Course Example - Classifying Fault Types

- Nearest Neighbor Classification

- Classification Trees

- Naive Bayes Classification

- Discriminant Analysis

- Support Vector Machines

- Classification with Neural Networks

- Project - Classification Methods

Validate model performance. Optimize model properties. Reduce the dimensionality of a data set and simplify machine learning models.

Lessons:

- Methods for Improving Predictive Models

- Cross Validation

- Reducing Predictors - Feature Transformation

- Reducing Predictors - Feature Selection

- Hyperparameter Optimization

- Ensemble Learning

- Project - Improving Predictive Models

Use supervised learning techniques to perform predictive modeling for continuous response variables.

Lessons:

- Course Example - Fuel Economy

- Linear Models

- Stepwise Fitting

- Regularized Linear Models

- SVMs, Trees and Neural Networks

- Gaussian Process Regression

- Project - Regression

Learn next steps and give feedback on the course.

Lessons:

- Additional Resources

- Survey

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.