The ML algorithms you'll actually use on the job. Understand them intuitively, implement them from scratch, then use Scikit-learn efficiently.
Linear & logistic regression
Decision trees & random forests
Gradient boosting (XGBoost)
K-means & clustering
Model evaluation & metrics
Cross-validation & pipelines
Resources
📘Hands-On ML — Aurélien GéronBook
💻Scikit-learn Official DocsDocs
🏆Kaggle Titanic CompetitionProject
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Project: End-to-end ML Pipeline
Build a complete classification pipeline: data ingestion → feature engineering → model training → evaluation. Use Scikit-learn Pipelines and log metrics.