lectures
- Lecture 0: Introduction (Aug. 30, 2018)
- Lecture 1: Data, Stats, and Visualization (Sep. 06, 2018)
- Lecture 2: Pandas and Scraping (Sep. 11, 2018)
- Lecture 3: Numpy, Scraping, Proper Visualization, EDA (Sep. 13, 2018)
- Lecture 4: Intro to Linear Regression and kNN (Sep. 18, 2018)
- Lecture 5: Multiple Regression and Bootstrap (Sep. 20, 2018)
- Lecture 6: Cross-Validation and Model Selection (Sep. 25, 2018)
- Lecture 7: Linear Model Regularization: Ridge & Lasso (Sep. 27, 2018)
- Lecture 8: PCA and High Dimensionality, Dealing with Big Data (Oct. 02, 2018)
- Lecture 9: Visualization for Communication (Oct. 04, 2018)
- Lecture 10: Logistic Regression I (Oct. 11, 2018)
- Lecture 11: Logistic Regression II (Oct. 16, 2018)
- Lecture 12: kNN classification and dealing with missing data (Oct. 18, 2018)
- Lecture 13: LDA and QDA (Oct. 23, 2018)
- Lecture 14: Classification Trees (Oct. 25, 2018)
- Lecture 15: Regression Trees and Random Forests (Nov. 06, 2018)
- Lecture 16: Boosting (Nov. 08, 2018)
- Lecture 17: Stacking (Nov. 13, 2018)
- Lecture 18: SVM I (Nov. 15, 2018)
- Lecture 19: SVM II (Nov. 20, 2018)
- Lecture 20: AB Testing (Nov. 27, 2018)