Resources

• a-section
• labs
• lectures
• sections

Topics

activation function

• Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
• Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
• Lab 11: Neural Network Basics - Introduction to tf.keras

AdaBoost

• Lecture 17: Boosting Methods
• Lecture 17: Boosting Methods [Notebook]

adaboost and xgboost

• S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost [Notebook]
• S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost

array reshape

• Lab 3: Matplotlib, Simple Linear Regression, kNN, array reshape
• Lab 03: Extended Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
• Lab 03: Prelab [Notebook]
• Lab 03: Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]

bagging

• S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost [Notebook]
• S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost
• S-Section 06: Bagging and Random Forest [Notebook]
• S-Section 07: Bagging and Random Forest
• Lab 9: Decision Trees
• Lab 9: Decision Trees [Notebook]
• Lecture 16: Bagging, & Random Forest

batching

• S-Section 09: Feed forward neural networks [Notebook]
• S-Section 09: Feed forward neural networks

Bayesian

• Advanced Section 2: Regularization
• Advanced Sections 2: [Notebook]

beautiful soup

• Lab 2: Pandas and Scraping

beautifulsoup

• S-Section 01: Introduction to Web Scraping [Notebook]
• S-Section 01: Introduction to Web Scraping [Notebook]
• S-Section 01: Introduction to Web Scraping

bias

• Lecture 8: Regularization and EDA

biases

• Lecture 8: Regularization and EDA

big data

• S-Section 06: PCA and Logistic Regression [Notebook]
• S-Section 06: PCA and Logistic Regression

boosting

• S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost [Notebook]
• S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost
• Lecture 17: Boosting Methods
• Lecture 17: Boosting Methods [Notebook]

bootstrap

• Lecture 5: Linear Regression

boundaries

• Lecture 10: Logistic Regression [Notebook]

Categorical Predictors

• Lecture 6: Multiple Linear Regression, Polynomial Regression

categorical variables

• S-Section 03: Multiple Linear and Polynomial Regression [Notebook]
• S-Section 03: Multiple Linear and Polynomial Regression

CI

• Lecture 5: Linear Regression

Classification

• Lecture 15: Decision Trees
• Lecture 15: Decision Trees [Notebook]
• S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]
• S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification
• Lecture 12: KNN Classification & Imputation
• Lecture 10: Logistic Regression [Notebook]

Collinearity

• Lecture 6: Multiple Linear Regression, Polynomial Regression

communication

• Lecture 9: Visualization for Communication

confidence intervals

• S-Section 02: kNN and Linear Regression [Notebook]
• S-Section 02: kNN and Linear Regression
• Lecture 5: Linear Regression

confusion matrix

• Lecture 11: Logistic Regression 2 [Notebook]

crawl

• Lab 2: Pandas and Scraping

cross-validation

• Lecture 11: Logistic Regression 2 [Notebook]
• S-Section 04: Regularization and Model Selection [Notebook]
• S-Section 04: Regularization and Model Selection
• Lab 4: Multiple and Polynomial Regression
• Lab 04: Multiple and Polynomial Regression [Notebook]
• Lab 04: Multiple and Polynomial Regression [Notebook]
• Lecture 7: Model Selection and Regularization

CV

• Lecture 11: Logistic Regression 2 [Notebook]

Data

• Lecture 2: Data and Data Exploration

Data Cleaning

• Lecture 3: Code Pandas + Beautiful Soup [Notebook]

Data Exploration

• Lecture 2: Data and Data Exploration

Data Science Demo

• Lecture 2: Data Science Demo (repeat from Lecture 1) [Notebook]
• Lecture 1: Data Science Demo [Notebook]

data science process

• Lecture 1: Introduction

data scraping

• S-Section 01: Introduction to Web Scraping [Notebook]
• S-Section 01: Introduction to Web Scraping [Notebook]
• S-Section 01: Introduction to Web Scraping

dataframe

• Lecture 3: Pandas and Web Scraping

decision boundaries

• S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]
• S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification

decision trees

• S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost [Notebook]
• S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost
• S-Section 06: Bagging and Random Forest [Notebook]
• S-Section 07: Bagging and Random Forest
• Lab 9: Decision Trees
• Lab 9: Decision Trees [Notebook]
• Lecture 15: Decision Trees
• Lecture 15: Decision Trees [Notebook]

Demo

• Advanced Section 2: Regularization
• Advanced Sections 2: [Notebook]

Descriptive Statistics

• Lecture 2: Data and Data Exploration

Dictionaries

• Lab 1: Python basics, YAML environments, Numpy

dimensionality reduction

• S-Section 06: PCA and Logistic Regression [Notebook]
• S-Section 06: PCA and Logistic Regression
• Lab 8: PCA
• Lab 8: PCA [Notebook]
• Advanced Sections 4: PCA
• Lecture 14: PCA
• Lecture 14: PCA [Notebook]

dropout

• Lab 12: Building and Regularizing your first Neural Network [Notebook]
• Lab 12: Building and Regularizing your first Neural Network [Notebook]

eda

• Lecture 9: Visualization for Communication
• Lecture 8: Regularization and EDA
• Lecture 3: Pandas and Web Scraping

Eigenvalues

• Advanced Sections 4: PCA

Eigenvectors

• Advanced Sections 4: PCA
• Advanced Section 1: Linear Algebra and Hypothesis Testing

Eignevalues

• Advanced Section 1: Linear Algebra and Hypothesis Testing

Elastic Net

• Advanced Section 2: Regularization
• Advanced Sections 2: [Notebook]

entropy

• Lab 9: Decision Trees
• Lab 9: Decision Trees [Notebook]
• Lecture 15: Decision Trees
• Lecture 15: Decision Trees [Notebook]

explained variance

• S-Section 06: PCA and Logistic Regression [Notebook]
• S-Section 06: PCA and Logistic Regression

exploratory data analysis

• Lecture 3: Pandas and Web Scraping

feed forward

• S-Section 09: Feed forward neural networks [Notebook]
• S-Section 09: Feed forward neural networks

feed forward neural networks

• Lab 12: Building and Regularizing your first Neural Network [Notebook]
• Lab 12: Building and Regularizing your first Neural Network [Notebook]
• Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
• Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
• Lab 11: Neural Network Basics - Introduction to tf.keras

Functions

• Lab 1: Python basics, YAML environments, Numpy

Gini Index

• Lecture 15: Decision Trees
• Lecture 15: Decision Trees [Notebook]

GLM

• Advanced Section 3: Generalized Linear Models
• Advanced Section 3: Generalized Linear Models [Notebook]

google sites

• Lab 13: Making websites! [Notebook]

Gradient Descent

• Lecture 17: Boosting Methods
• Lecture 17: Boosting Methods [Notebook]

html

• Lab 13: Making websites! [Notebook]

http

• Lab 13: Making websites! [Notebook]

Hypothesis Testing

• Lecture 6: Multiple Linear Regression, Polynomial Regression
• Advanced Section 1: Linear Algebra and Hypothesis Testing
• Lecture 5: Linear Regression

imputation

• Lecture 12: KNN Classification & Imputation

information gain

• Lab 9: Decision Trees
• Lab 9: Decision Trees [Notebook]

interaction terms

• S-Section 03: Multiple Linear and Polynomial Regression [Notebook]
• S-Section 03: Multiple Linear and Polynomial Regression
• Lecture 6: Multiple Linear Regression, Polynomial Regression

Introduction

• Lecture 1: Introduction

k-Nearest Neighbors (kNN) Regression

• Lab 3: Scikit-learn for Regression [Notebook]

keras

• Lab 12: Building and Regularizing your first Neural Network [Notebook]
• Lab 12: Building and Regularizing your first Neural Network [Notebook]
• Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
• Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
• Lab 11: Neural Network Basics - Introduction to tf.keras
• S-Section 09: Feed forward neural networks [Notebook]
• S-Section 09: Feed forward neural networks

KNN

• Lecture 12: KNN Classification & Imputation
• Lab 3: Matplotlib, Simple Linear Regression, kNN, array reshape
• Lab 03: Extended Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
• Lab 03: Prelab [Notebook]
• Lab 03: Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]

KNN-classification

• S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]
• S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification

KNN imputation classification

• Lecture 12: KNN Classification & Imputation [Notebook]

knn k-Nearest Neighbors (kNN)

• S-Section 02: kNN and Linear Regression [Notebook]
• S-Section 02: kNN and Linear Regression

kNN regression

• Lecture 4: Introduction to Regression

lasso

• S-Section 04: Regularization and Model Selection [Notebook]
• S-Section 04: Regularization and Model Selection
• Lecture 8: Regularization and EDA
• Advanced Section 2: Regularization
• Advanced Sections 2: [Notebook]

Linear Algebra

• Advanced Section 1: Linear Algebra and Hypothesis Testing

linear regression

• Lab 6: Logistic Regression
• Lab 6: Logistic Regression [Notebook]
• Lab 6: Logistic Regression [Notebook]
• Lab 4: Multiple and Polynomial Regression
• Lab 04: Multiple and Polynomial Regression [Notebook]
• Lab 04: Multiple and Polynomial Regression [Notebook]
• S-Section 02: kNN and Linear Regression [Notebook]
• S-Section 02: kNN and Linear Regression
• Lecture 5: Linear Regression
• Lab 3: Scikit-learn for Regression [Notebook]

Lists

• Lab 1: Python basics, YAML environments, Numpy

logistic regression

• S-Section 06: PCA and Logistic Regression [Notebook]
• S-Section 06: PCA and Logistic Regression
• S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]
• S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification
• Lab 6: Logistic Regression
• Lab 6: Logistic Regression [Notebook]
• Lab 6: Logistic Regression [Notebook]
• Lecture 11: Logistic Regression 2 [Notebook]
• Lecture 10: Logistic Regression [Notebook]

logistics

• Lecture 1: Introduction

matplotlib

• Lab 5: Exploratory Data Analysis, seaborn, more Plotting
• Lab 3: Matplotlib, Simple Linear Regression, kNN, array reshape
• Lab 03: Extended Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
• Lab 03: Prelab [Notebook]
• Lab 03: Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
• S-Section 01: Introduction to Web Scraping [Notebook]
• S-Section 01: Introduction to Web Scraping [Notebook]
• S-Section 01: Introduction to Web Scraping
• Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]
• Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]

metrics

• Lecture 11: Logistic Regression 2 [Notebook]

mle

• Lab 6: Logistic Regression
• Lab 6: Logistic Regression [Notebook]
• Lab 6: Logistic Regression [Notebook]

MNIST

• S-Section 06: PCA and Logistic Regression [Notebook]
• S-Section 06: PCA and Logistic Regression

model selection

• S-Section 04: Regularization and Model Selection [Notebook]
• S-Section 04: Regularization and Model Selection
• Advanced Section 2: Regularization
• Advanced Sections 2: [Notebook]
• Lecture 7: Model Selection and Regularization

multiclass

• Lecture 11: Logistic Regression 2 [Notebook]

multilayer perceptron

• Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
• Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
• Lab 11: Neural Network Basics - Introduction to tf.keras

multinomial regression

• Lab 4: Multiple and Polynomial Regression
• Lab 04: Multiple and Polynomial Regression [Notebook]
• Lab 04: Multiple and Polynomial Regression [Notebook]

multiple linear regression

• S-Section 03: Multiple Linear and Polynomial Regression [Notebook]
• S-Section 03: Multiple Linear and Polynomial Regression
• Lecture 6: Multiple Linear Regression, Polynomial Regression

Multiple Logistic Regression

• S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]
• S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification

neural networks

• S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver [Notebook]
• S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver
• Lab 12: Building and Regularizing your first Neural Network [Notebook]
• Lab 12: Building and Regularizing your first Neural Network [Notebook]
• Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
• Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
• Lab 11: Neural Network Basics - Introduction to tf.keras
• S-Section 09: Feed forward neural networks [Notebook]
• S-Section 09: Feed forward neural networks

NumPy

• Lab 1: Python basics, YAML environments, Numpy
• Lab 01: YAML Environments, Python basics, Numpy [Notebook]

OOB

• Lecture 16: Bagging, & Random Forest

out of bag error

• Lecture 16: Bagging, & Random Forest

overfitting

• S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver [Notebook]
• S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver
• Lecture 6: Multiple Linear Regression, Polynomial Regression

p-values

• Lecture 5: Linear Regression

pairplot

• S-Section 03: Multiple Linear and Polynomial Regression [Notebook]
• S-Section 03: Multiple Linear and Polynomial Regression

pandas

• Lab 5: Exploratory Data Analysis, seaborn, more Plotting
• S-Section 01: Introduction to Web Scraping [Notebook]
• S-Section 01: Introduction to Web Scraping [Notebook]
• S-Section 01: Introduction to Web Scraping
• Lab 02: More Pandas [Notebook]
• Lab 02: Scraping [Notebook]
• Lecture 3: Code Pandas + Beautiful Soup [Notebook]
• Lecture 3: Pandas and Web Scraping
• Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]
• Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]

pca

• Lab 8: PCA
• Lab 8: PCA [Notebook]
• Advanced Sections 4: PCA
• Lecture 14: PCA
• Lecture 14: PCA [Notebook]

pipeline

• S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]
• S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification

plots

• Lab 5: Exploratory Data Analysis, seaborn, more Plotting
• Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]
• Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]

polynomial regression

• S-Section 03: Multiple Linear and Polynomial Regression [Notebook]
• S-Section 03: Multiple Linear and Polynomial Regression
• Lab 4: Multiple and Polynomial Regression
• Lab 04: Multiple and Polynomial Regression [Notebook]
• Lab 04: Multiple and Polynomial Regression [Notebook]
• Lecture 6: Multiple Linear Regression, Polynomial Regression

predictors

• Lecture 4: Introduction to Regression

principal components analysis

• S-Section 06: PCA and Logistic Regression [Notebook]
• S-Section 06: PCA and Logistic Regression

principle component analysis

• Lab 8: PCA
• Lab 8: PCA [Notebook]

probabilities

• Lecture 10: Logistic Regression [Notebook]

Python

• Lab 01: YAML Environments, Python basics, Numpy [Notebook]

Qualitative Predictors

• Lecture 6: Multiple Linear Regression, Polynomial Regression

R-square

• Lecture 4: Introduction to Regression

R^2

• Lecture 4: Introduction to Regression

random forest

• S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost [Notebook]
• S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost
• S-Section 06: Bagging and Random Forest [Notebook]
• S-Section 07: Bagging and Random Forest
• Lecture 16: Bagging, & Random Forest

Regression

• Lecture 6: Multiple Linear Regression, Polynomial Regression

regression trees

• Lab 9: Decision Trees
• Lab 9: Decision Trees [Notebook]

regularization

• S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver [Notebook]
• S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver
• Lab 12: Building and Regularizing your first Neural Network [Notebook]
• Lab 12: Building and Regularizing your first Neural Network [Notebook]
• Lecture 11: Logistic Regression 2 [Notebook]
• S-Section 04: Regularization and Model Selection [Notebook]
• S-Section 04: Regularization and Model Selection
• Advanced Section 2: Regularization
• Advanced Sections 2: [Notebook]

requests

• Lab 2: Pandas and Scraping

response variable

• Lecture 4: Introduction to Regression

RF

• Lecture 16: Bagging, & Random Forest

ridge

• S-Section 04: Regularization and Model Selection [Notebook]
• S-Section 04: Regularization and Model Selection
• Advanced Section 2: Regularization
• Advanced Sections 2: [Notebook]

Ridge regression

• Lecture 8: Regularization and EDA

roc

• Lecture 11: Logistic Regression 2 [Notebook]

Scikit-learn

• Lab 3: Scikit-learn for Regression [Notebook]

scraping

• Lab 2: Pandas and Scraping
• Lecture 3: Pandas and Web Scraping

seaborn

• Lab 5: Exploratory Data Analysis, seaborn, more Plotting
• Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]
• Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]

Simple Linear Regression

• Lab 3: Matplotlib, Simple Linear Regression, kNN, array reshape
• Lab 03: Extended Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
• Lab 03: Prelab [Notebook]
• Lab 03: Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]

sklearn

• Lecture 11: Logistic Regression 2 [Notebook]
• Lecture 10: Logistic Regression [Notebook]
• S-Section 02: kNN and Linear Regression [Notebook]
• S-Section 02: kNN and Linear Regression
• Lab 3: Matplotlib, Simple Linear Regression, kNN, array reshape
• Lab 03: Extended Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
• Lab 03: Prelab [Notebook]
• Lab 03: Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]

Statistical model

• Lecture 4: Introduction to Regression

statsmodels

• S-Section 02: kNN and Linear Regression [Notebook]
• S-Section 02: kNN and Linear Regression
• Lab 3: Matplotlib, Simple Linear Regression, kNN, array reshape
• Lab 03: Extended Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
• Lab 03: Prelab [Notebook]
• Lab 03: Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]

stochastic gradient descent

• S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver [Notebook]
• S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver
• S-Section 09: Feed forward neural networks [Notebook]
• S-Section 09: Feed forward neural networks

t-test.

• Lecture 5: Linear Regression

tensorflow

• Lab 12: Building and Regularizing your first Neural Network [Notebook]
• Lab 12: Building and Regularizing your first Neural Network [Notebook]
• Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
• Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
• Lab 11: Neural Network Basics - Introduction to tf.keras
• S-Section 09: Feed forward neural networks [Notebook]
• S-Section 09: Feed forward neural networks

The Data Science Process

• Lecture 2: Data Science Demo (repeat from Lecture 1) [Notebook]
• Lecture 1: Data Science Demo [Notebook]

train-test

• Lecture 4: Introduction to Regression

training

• S-Section 09: Feed forward neural networks [Notebook]
• S-Section 09: Feed forward neural networks

training and testing data splitting

• S-Section 02: kNN and Linear Regression [Notebook]
• S-Section 02: kNN and Linear Regression

trees

• Lab 9: Decision Trees
• Lab 9: Decision Trees [Notebook]

Variable Importance

• Lecture 16: Bagging, & Random Forest

Variance vs Bias

• Lecture 15: Decision Trees
• Lecture 15: Decision Trees [Notebook]

visualization

• Lecture 9: Visualization for Communication

web pages

• Lab 13: Making websites! [Notebook]

web scraping

• Lab 2: Pandas and Scraping
• Lecture 3: Pandas and Web Scraping

website scraping

• Lab 2: Pandas and Scraping

websites

• Lab 13: Making websites! [Notebook]

weights

• Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
• Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
• Lab 11: Neural Network Basics - Introduction to tf.keras

wix

• Lab 13: Making websites! [Notebook]

www

• Lab 13: Making websites! [Notebook]

YAML

• Lab 1: Python basics, YAML environments, Numpy
• Lab 01: YAML Environments, Python basics, Numpy [Notebook]