Lesson 3: Clustering, Neural Networks, and Feature Engineering
Lesson Overview
Develop practical capability in clustering, neural networks, and feature engineering as part of the MATLAB for Data Analytics and Machine Learning learning path.
Topics Covered
K-means and hierarchical clustering
Anomaly detection
Feature selection and dimensionality reduction
Neural-network and deep-learning overview
Handling imbalance and scaling
Comparing analytical approaches
Practical Activity
Build a guided MATLAB exercise covering k-means and hierarchical clustering.
Apply anomaly detection to a practical engineering or data scenario.
Validate results and troubleshoot an issue involving comparing analytical approaches.
Key Takeaways
Explain and apply k-means and hierarchical clustering.
Use feature selection and dimensionality reduction in a structured workflow.
Validate outputs with plots, metrics, tests, or simulation evidence.
