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Phase 02: ML Fundamentals

AI From Scratch/Lesson 14/~75 minutes

Naive Bayes

The "naive" assumption is wrong, and it works anyway. That's the beauty of it.

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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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Resources

scikit-learn Naive Bayes docsReading McCallum and Nigam, A Comparison of Event Models for Naive Bayes Text Classification (1998)Reading Rennie et al., Tackling the Poor Assumptions of Naive Bayes Text Classifiers (2003)Reading Ng and Jordan, On Discriminative vs. Generative Classifiers (2001)Reading Naive Bayes ChooserSkill naive_bayes.pyCode