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Phase 02: ML Fundamentals

AI From Scratch/Lesson 08/~90 minutes

Feature Engineering & Selection

A good feature is worth a thousand data points.

BuildPythonNo prerequisites

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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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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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Feature Engineering and Selection (Max Kuhn & Kjell Johnson)Reading scikit-learn Preprocessing GuideReading Target Encoding Done Right (Micci-Barreca, 2001)Reading Feature EngineerPrompt features.pyCode Source lessonSource