YoVDO

Machine Learning with MATLAB

Offered By: MathWorks via MATLAB Academy

Tags

MATLAB Courses Data Visualization Courses Machine Learning Courses Unsupervised Learning Courses Data Processing Courses Predictive Modeling Courses Dimensionality Reduction Courses Classification Models Courses K-Means Clustering Courses

Course Description

Overview

  • Getting Started: Get an overview of the course. Import and process data, explore data features, and train and evaluate a classification model.
  • Finding Natural Patterns in Data: Use unsupervised learning techniques to group observations based on a set of explanatory variables and discover natural patterns in a data set.
  • Classification Methods: Use available classification methods to train data classification models. Make predictions and evaluate the accuracy of a predictive model.
  • Improving Predictive Models: Validate model performance. Optimize model properties. Reduce the dimensionality of a data set and simplify machine learning models.
  • Regression Methods: Use supervised learning techniques to perform predictive modeling for continuous response variables.
  • Conclusion: Learn next steps and give feedback on the course.

Syllabus

  • Course Overview
  • Review - Machine Learning Onramp
  • Course Example - Grouping Basketball Players
  • Low Dimensional Visualization
  • k-Means Clustering
  • Gaussian Mixture Models
  • Interpreting the Clusters
  • Hierarchical Clustering
  • Project - Clustering
  • 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
  • Methods for Improving Predictive Models
  • Cross Validation
  • Reducing Predictors - Feature Transformation
  • Reducing Predictors - Feature Selection
  • Hyperparameter Optimization
  • Ensemble Learning
  • Project - Improving Predictive Models
  • Course Example - Fuel Economy
  • Linear Models
  • Stepwise Fitting
  • Regularized Linear Models
  • SVMs, Trees and Neural Networks
  • Gaussian Process Regression
  • Project - Regression
  • Additional Resources
  • Survey

Taught by

Andrea Bayas

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