- Organizing and preprocessing data
- Clustering data
- Creating classification and regression models
- Interpreting and evaluating models
- Simplifying data sets
- Using ensembles to improve model performance
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.
- Data types
- Data preparation
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.
- Unsupervised learning
- Clustering methods
- Cluster evaluation and interpretation
Building Classification Models
Objective: Use supervised learning techniques to perform predictive modeling for classification problems. Evaluate the accuracy of a predictive model.
- Supervised learning
- Training and validation
- Classification methods
Day 2 of 2
Improving Predictive Models
Objective: Reduce the dimensionality of a data set. Improve and simplify machine learning models.
- Cross validation
- Hyperparameter optimization
- Feature transformation
- Feature selection
- Ensemble learning
Building Regression Models
Objective: Use supervised learning techniques to perform predictive modeling for continuous response variables.
- Parametric regression methods
- Nonparametric regression methods
- Evaluation of regression models
Creating Neural Networks
Objective: Create and train neural networks for clustering and predictive modeling. Adjust network architecture to improve performance.
- Clustering with Self-Organizing Maps
- Classification with feed-forward networks
- Regression with feed-forward networks