Resources
Topics
AB Testing
AdaBoost
- Lecture 16: Boosting
- Lecture 16: Boosting [Notebook]
- S-Section 9
- S-Section 9 [Notebook]
- S-Section 8
- S-Section 8 [Notebook]
AIC
- A-Section 2: AIC
- AIC [Notebook]
Bagging
- Lecture 15: Regression Trees and Random Forests
- Lecture 15: Regression Trees and Random Forests [Notebook]
BeautifulSoup
- Lecture 2: Pandas and Scraping
- Lecture 2: Pandas and Scraping [Notebook]
- S-Section 2
- S-Section 2 [Notebook]
- S-Section 1
- S-Section 1 [Notebook]
Bias/Variance Tradeoff
Boosting
- Lecture 16: Boosting
- Lecture 16: Boosting [Notebook]
- Lab 9: Random Forests and Boosting [Notebook]
Bootstrapping
Classification
- Lecture 11: Logistic Regression II
- Lecture 10: Logistic Regression I
- Lecture 10: Logistic Regression I [Notebook]
- A-Section 6
- S-Section 7
- S-Section 7 [Notebook]
Cross Validation
- Lecture 6: Cross-Validation and Model Selection
- Lecture 6: Cross-Validation and Model Selection [Notebook]
- S-Section 3
- S-Section 3 [Notebook]
Data Science
- Lecture 0: Introduction
- Lecture 0: Introduction [Notebook]
Data Scraping
Decision Trees
- Lecture 16: Boosting
- Lecture 16: Boosting [Notebook]
- Lecture 15: Regression Trees and Random Forests
- Lecture 15: Regression Trees and Random Forests [Notebook]
- Lecture 14: Classification Trees
- Lecture 14: Classification Trees [Notebook]
- Lab 8: Discriminant Analysis & Classification Trees [Notebook]
Diagnostic Testing
Dimensionality Reduction
- Lecture 8: PCA and High Dimensionality, Dealing with Big Data
- Lecture 8: PCA and High Dimensionality, Dealing with Big Data [Notebook]
- A-Section 4
Discriminant Analysis
EDA
- Lecture 3: Numpy, Scraping, Proper Visualization, EDA
- Lecture 3: Numpy, Scraping, Proper Visualization, EDA [Notebook]
- Lecture 2: Pandas and Scraping [Notebook]
- Lecture 1: Data, Stats, and Visualization
- Lecture 0: Introduction [Notebook]
- Lecture 1: Data, Stats, and Visualization [Notebook]
- Lab 2: EDA [Notebook]
Experiment Design
Hubway
- Lecture 0: Introduction [Notebook]
- Lecture 1: Data, Stats, and Visualization [Notebook]
Hypothesis Testing
Imputation
Information Criteria
Introduction
- Lecture 0: Introduction
- Lecture 0: Introduction [Notebook]
kNN
- Lecture 12: kNN classification and dealing with missing data
- Lecture 4: Intro to Linear Regression and kNN
- Lecture 4: Intro to Linear Regression and kNN [Notebook]
- Lab 7: Logistic Regression, kNN Classification [Notebook]
Lasso Regression
- Lecture 8: PCA and High Dimensionality, Dealing with Big Data
- Lecture 8: PCA and High Dimensionality, Dealing with Big Data [Notebook]
- Lecture 7: Linear Model Regularization: Ridge & Lasso
- Lecture 7: Linear Model Regularization: Ridge & Lasso [Notebook]
Linear Discriminant Analysis
- Lecture 13: LDA and QDA
- Lecture 13: LDA and QDA [Notebook]
Linear Regression
- S-Section 6
- Lab 3 [Notebook]
- A-Section 1: Linear Regression
Logistic Regression
- Lecture 11: Logistic Regression II
- Lecture 10: Logistic Regression I
- Lecture 10: Logistic Regression I [Notebook]
- Lab 7: Logistic Regression, kNN Classification [Notebook]
- A-Section 5
- S-Section 6
- S-Section 6 [Notebook]
- Lab 6: Logistic Regression, PCA [Notebook]
Matplotlib
Model Selection
- Lecture 6: Cross-Validation and Model Selection
- Lecture 6: Cross-Validation and Model Selection [Notebook]
- Lecture 5: Multiple Regression and Bootstrap
- Lecture 4: Intro to Linear Regression and kNN
- S-Section 5
- S-Section 5 [Notebook]
- S-Section 4
- S-Section 4 [Notebook]
- Lab 4: Model Selection [Notebook]
- S-Section 3
- S-Section 3 [Notebook]
Multiple Linear Regression
- Lecture 5: Multiple Regression and Bootstrap
- Lecture 5: Multiple Regression and Bootstrap [Notebook]
Numpy
Pandas
- Lecture 2: Pandas and Scraping
- Lecture 2: Pandas and Scraping [Notebook]
- Lab 1: Python: Numpy, functions, Pandas, Matplotlib [Notebook]
PCA
- Lecture 8: PCA and High Dimensionality, Dealing with Big Data
- Lecture 8: PCA and High Dimensionality, Dealing with Big Data [Notebook]
- Lab 6: Logistic Regression, PCA [Notebook]
- S-Section 5
- S-Section 5 [Notebook]
Polynomial Regression
- Lecture 5: Multiple Regression and Bootstrap
- Lecture 5: Multiple Regression and Bootstrap [Notebook]
Pruning
Quadratic Discriminant Analysis
- Lecture 13: LDA and QDA
- Lecture 13: LDA and QDA [Notebook]
Random Forest
- Lecture 16: Boosting
- Lecture 16: Boosting [Notebook]
- Lecture 15: Regression Trees and Random Forests
- Lecture 15: Regression Trees and Random Forests [Notebook]
Random Forests
- S-Section 9
- S-Section 9 [Notebook]
- Lab 9: Random Forests and Boosting [Notebook]
- S-Section 8
- S-Section 8 [Notebook]
Regression
- Lecture 4: Intro to Linear Regression and kNN
- Lecture 4: Intro to Linear Regression and kNN [Notebook]
Regularization
- Lecture 7: Linear Model Regularization: Ridge & Lasso
- Lecture 7: Linear Model Regularization: Ridge & Lasso [Notebook]
- Lab 5: Regularization [Notebook]
- A-Section 3
Requests
Residual Analysis
- Lecture 5: Multiple Regression and Bootstrap
- Lecture 5: Multiple Regression and Bootstrap [Notebook]
Ridge Regression
- Lecture 7: Linear Model Regularization: Ridge & Lasso
- Lecture 7: Linear Model Regularization: Ridge & Lasso [Notebook]
ROC Curve
Scikit-Learn
SQL
Stacking
Stepwise Selection
- Lecture 6: Cross-Validation and Model Selection
- Lecture 6: Cross-Validation and Model Selection [Notebook]
Support Vector Machines
- Lecture 19: SVM II
- Lecture 19: SVM II [Notebook]
- Lecture 19: SVM II [Notebook]
- Lecture 18: SVM I
- Lecture 18: SVM I [Notebook]
- A-Section 7