Machine Learning Explained: Regression, Trees, Clustering and Neural Networks

How do machine learning models turn data into predictions? Erika Barker introduces common algorithms through examples from fraud detection, recommendation systems and risk assessment. This episode is a starting point for listeners who want to understand the methods behind AI.

• Linear and logistic regression
• Decision trees and random forests
• Support vector machines and kernel methods
• Clustering with k-means and DBSCAN
• Dimensionality reduction with PCA and t-SNE
• Neural networks and deep learning
• Overfitting and the bias-variance trade-off

The examples illustrate how different methods approach different problems. Real systems may combine several techniques and require validation on appropriate data. The discussion focuses on concepts rather than a coding tutorial.

Research and writing: ErikaBarker.ai.