Core ML: Algorithms from Scratch
Build every classic ML algorithm by hand — then verify against scikit-learn
4 phases. 18 lessons. 18 labs. 1 capstone. The classical ML algorithms every data scientist uses — linear regression, logistic regression, SVM, decision trees, random forests, KNN, K-Means, DBSCAN, Naive Bayes, boosting, and ensemble methods — every one implemented from scratch in pure Python, then benchmarked against scikit-learn to prove they work. You build a complete ML library and graduate understanding what scikit-learn does under the hood.
- Lessons: —
- Labs: —
- Projects: —
- Level: Beginner
Curriculum
- ML Foundations — What is ML, the learning lifecycle, and linear regression from scratch.
- Classification Algorithms — Logistic regression, decision trees, SVMs, KNN, and naive Bayes — all from scratch.
- Ensembles & Evaluation — Unsupervised learning, feature engineering, model evaluation, bias-variance, and ensemble methods.
- Production ML Practices — Hyperparameter tuning, ML pipelines, time series, anomaly detection, imbalanced data, and feature selection.
Skills You Will Learn
- Linear & Logistic Regression from Scratch
- SVM & Kernel Methods
- Decision Trees & Random Forests
- K-Means & DBSCAN Clustering
- Naive Bayes Classifiers
- Boosting & Ensemble Methods
- Model Evaluation & Cross-Validation
- Feature Engineering & Selection
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