Lecture 19 (Logistic Regression) - Data 100 Su19

Lecture 28 (Final Review 2) - Data 100 Su19Подробнее

Lecture 27 (Final Review 1) - Data 100 Su19Подробнее

Lecture 26 (Ethics & Conclusion) - Data 100 Su19Подробнее

Lecture 25 (Random Forests, Runtime Analysis, Modeling Overview) - Data 100 Su19Подробнее

Lecture 24 (Spark, Decision Trees) - Data 100 Su19Подробнее

Lecture 22 (Inference for Modeling) - Data 100 Su19Подробнее

Lecture 21 (Decision Boundaries, Modeling Considerations) - Data 100 Su19Подробнее

Lecture 20 (Classifier Evaluation and Fitting) - Data 100 Su19Подробнее

Lecture 17 (Feature Engineering, Bias-Variance Tradeoff) - Data 100 Su19Подробнее

Lecture 16 (Gradient Descent) - Data 100 Su19Подробнее

Lecture 15 (Linear Regression) - Data 100 Su19Подробнее

Lecture 14 (Risk and Loss Functions) - Data 100 Su19Подробнее