lectures

• Lecture 1: Introduction (Sep. 03, 2019)
• Lecture 2: Data and Data Exploration (Sep. 04, 2019)
• Lecture 3: Pandas and Web Scraping (Sep. 11, 2019)
• Lecture 4: Introduction to Regression (Sep. 16, 2019)
• Lecture 5: Linear Regression (Sep. 18, 2019)
• Lecture 6: Multiple Linear Regression, Polynomial Regression (Sep. 23, 2019)
• Lecture 7: Model Selection and Regularization (Sep. 25, 2019)
• Lecture 8: Regularization and EDA (Sep. 30, 2019)
• Lecture 9: Visualization for Communication (Oct. 02, 2019)
• Lecture 10: Logistic Regression (Oct. 07, 2019)
• Lecture 11: Logistic Regression 2 (Oct. 09, 2019)
• Lecture 12: KNN Classification & Imputation (Oct. 16, 2019)
• Lecture 14: PCA (Oct. 23, 2019)
• Lecture 15: Decision Trees (Oct. 28, 2019)
• Lecture 16: Bagging, & Random Forest (Oct. 30, 2019)
• Lecture 17: Boosting Methods (Nov. 04, 2019)
• Lecture 18: Neural Networks 1 – Perceptron and MLP (Nov. 06, 2019)
• Lecture 19: NN 2: Anatomy of NN, design choices (Nov. 11, 2019)
• Lecture 20: NN 3: Back Propagation (Nov. 13, 2019)
• Lecture 21: NN 4: Regularization methods (Nov. 18, 2019)
• Lecture 22: Visualization for Model Interpretation (Nov. 20, 2019)
• Lecture 23: Experimental Design & Testing I (Nov. 25, 2019)
• Lecture 24: Experimental Design & Testing II (Dec. 02, 2019)