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Phase 02: ML Fundamentals

AI From Scratch/Lesson 09/~90 minutes

Model Evaluation

A model is only as good as the way you measure it.

BuildPythonNo prerequisites

Back to phaseML Fundamentals Next lessonBias-Variance Tradeoff

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

scikit-learn Model Selection GuideReading Beyond Accuracy: Precision and Recall (Google ML Crash Course)Reading A Survey of Cross-Validation Procedures (Arlot & Celisse, 2010)Reading EvaluationSkill evaluation.pyCode main.jlCode