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Phase 02: ML Fundamentals

AI From Scratch/Lesson 07/~90 minutes

Unsupervised Learning

No labels, no teacher. The algorithm finds structure on its own.

BuildPythonNo prerequisites

Back to phaseML Fundamentals Next lessonFeature Engineering & Selection

Phase 02

ML Fundamentals

01What Is Machine Learning 02Linear Regression 03Logistic Regression 04Decision Trees and Random Forests 05Support Vector Machines 06K-Nearest Neighbors and Distances 07Unsupervised Learning 08Feature Engineering & Selection 09Model Evaluation 10Bias-Variance Tradeoff 11Ensemble Methods 12Hyperparameter Tuning 13ML Pipelines 14Naive Bayes 15Time Series Fundamentals 16Anomaly Detection 17Handling Imbalanced Data 18Feature Selection

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Phase 02

ML Fundamentals

01What Is Machine Learning 02Linear Regression 03Logistic Regression 04Decision Trees and Random Forests 05Support Vector Machines 06K-Nearest Neighbors and Distances 07Unsupervised Learning 08Feature Engineering & Selection 09Model Evaluation 10Bias-Variance Tradeoff 11Ensemble Methods 12Hyperparameter Tuning 13ML Pipelines 14Naive Bayes 15Time Series Fundamentals 16Anomaly Detection 17Handling Imbalanced Data 18Feature Selection

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Resources

Stanford CS229 - Unsupervised LearningReading scikit-learn Clustering GuideReading DBSCAN original paper (Ester et al., 1996)Reading Clustering GuideSkill clustering.pyCode Source lessonSource