Core Machine Learning Topics

Fundamental Concepts

• Introduction to Machine Learning
• Linear Regression
• Logistic Regression
• k-Nearest Neighbors (KNN)
• Naive Bayes & Probabilistic Models

Tree-Based & Ensemble Methods

• Ensemble Methods: Bagging & Random Forests
• Boosting: AdaBoost, Gradient Boosted Trees, XGBoost

Margin-Based Models

• Support Vector Machines (SVMs)

Unsupervised Learning

• Clustering: k-Means & Hierarchical
• Gaussian Mixture Models & EM Algorithm

Dimensionality Reduction & Visualization

• Principal Component Analysis (PCA)
• t-SNE & UMAP

Model Diagnostics

• Bias–Variance Tradeoff
• Model Evaluation & Metrics