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AD-502 Machine Learning Notes

5 notes for Machine Learning, unit by unit.

Unit 1 · Short notes Introduction to Machine Learning Introduction to machine learning · Machine learning life cycle · Types of machine learning system (supervised, unsupervised, batch, online, instance‑based, model‑based) · Scope and limitations · Challenges of machine learning · Data visualization
Unit 2 · Short notes Clustering in Machine Learning Clustering in machine learning · Partitioning clustering · Distribution model‑based clustering · Hierarchical clustering · Fuzzy clustering · Birch algorithm
Unit 3 · Short notes Classification Algorithms Logistic regression · Decision tree classification · Neural networks · K‑Nearest Neighbors (K‑NN) · Support vector machine · Naïve Bayes (Gaussian, Multinomial, Bernoulli)
Unit 4 · Short notes Ensemble Learning and Random Forest Introduction to ensemble learning · Max voting · Averaging · Weighted average · Voting classifiers · Bagging
Unit 5 · Short notes Dimensionality Reduction Curse of dimensionality · Projection‑based dimensionality reduction · Manifold learning · Principal component analysis (PCA) – preserving variance · Principal components · Projecting to d dimensions

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