Machine Learning Fundamentals

1

ML Concepts & Paradigms

Supervised, unsupervised, reinforcement learning

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2

Supervised Learning - Regression

Continuous outputs, linear regression, evaluation

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3

Supervised Learning - Classification

Discrete outputs, decision trees, k-NN, accuracy/precision/recall

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4

Unsupervised Learning

Clustering, k-means, hierarchical, dimensionality reduction

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5

Model Evaluation & Validation

Train/test split, cross-validation, overfitting

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6

ML Workflow & Deployment

End-to-end pipeline from problem to production

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7

Capstone Project

Build complete ML classifier (depression prediction)

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