Machine Learning Overview

This section delves into algorithms and techniques that enable systems to learn from data, covering foundational concepts, model types, evaluation, and deployment considerations.

Key Topics Covered:

• The Basics: Core concepts like Model Selection, Evaluation (Over/Underfitting, Metrics), Regularization, Parameter Tuning, and Resampling.
  • Ensemble Methods: Bagging, Boosting, Stacking.
  • Performance Metrics: Accuracy, Precision, Recall, F1, etc.
  • Regularization: Lasso and Ridge regression.
• Supervised Learning: Learning from labeled data.
  • Regression: Linear, Polynomial regression.
  • Classification: Logistic Regression, Decision Trees.
• Unsupervised Learning: Finding patterns in unlabeled data.
  • Clustering: Grouping similar data points.
  • Dimensionality Reduction: Reducing the number of features.
  • Association Rules: Discovering relationships between items.
• AB Testing: Methods for comparing different versions.
• Causal Inference: Techniques for understanding cause-and-effect relationships.
• ML Deployment: Link to ML Deployment index or key file once available.

---

Browse the sidebar under the Machine Learning heading for a full list of notes within this topic.