- Organizing and preprocessing data
- Clustering data
- Creating classification and regression models
- Interpreting and evaluating models
- Simplifying data sets
- Using ensembles to improve model performance
- Bring data into MATLAB and organize it for analysis, including normalizing data and removing observations with missing values. Data types, Table, Categorical data, and Data preparation.
- 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 and Cluster evaluation and interpretation.
- Use supervised learning techniques to perform predictive modeling for classification problems. Evaluate the accuracy of a predictive model. Supervised learning, Training and validation and Classification methods
- Reduce the dimensionality of a data set. Improve and simplify machine learning models. Cross validation, Feature transformation, Feature selection and Ensemble learning.
- Use supervised learning techniques to perform predictive modeling for continuous response variables. Parametric regression methods, Nonparametric regression methods and Evaluation of regression models
- 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 and Regression with feed-forward networks
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