Machine Learning Fundamentals
Machine learning is a core branch of artificial intelligence that studies how computers can automatically learn patterns from data. This section covers the fundamentals and classical algorithms of machine learning.
Contents:
- Introduction to Machine Learning — Learning paradigms, bias-variance tradeoff, model selection
- Interview Notes — Summary of core concepts and common questions
- Supervised Learning — Linear models, decision trees, SVM, ensemble methods
- Unsupervised Learning — Clustering, dimensionality reduction, anomaly detection
- Ensemble Learning — Random Forest, XGBoost, LightGBM, Stacking
- Kernel Methods — SVM in depth, kernel trick, Gaussian Processes
- Probabilistic Graphical Models — EM algorithm, Naive Bayes, variational inference
- Time Series Analysis — ARIMA, Prophet, ML time series methods
- Feature Engineering — Feature selection, construction, encoding
- Model Evaluation & Selection — Cross-validation, metrics, hyperparameter search