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

Format:Self-paced

Language:English

- Hands-on exercises with automated feedback
- Access to MATLAB through your web browser
- Shareable progress report and course certificate

Learn the theory and practice of building deep neural networks with real-life image and sequence data.

Learn the basics of practical machine learning methods for classification problems.