Resources

• a-sections
• lectures
• sections

Topics

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

Adam

• S-Section 10: Feed Forward Neural Networks: Regularization and Adam optimizer [Notebook]
• S-Section 10: Feed Forward Neural Networks: Regularization and Adam optimizer

Back Propagation

• Lecture 31: Neural Networks 4 -Back Propagation, SGD
• Lecture 31: Neural Networks 4 -Back Propagation, SGD [Notebook]
• Lecture 31: Neural Networks 4 -Back Propagation, SGD [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 07: Bagging and Random Forest [Notebook]
• S-Section 07: Bagging and Random Forest
• Lecture 23: Regression Trees, Bagging, and RF
• Lecture 23: Regression Trees, Bagging, and RF [Notebook]
• Lecture 23: Regression Trees, Bagging, and RF [Notebook]

batching

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

beautiful soup

• Lecture 3: Web Scraping + PANDAS
• Lecture 3: Web Scraping + PANDAS [Notebook]
• Lecture 3: Web Scraping + PANDAS [Notebook]

beautifulsoup

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

bias

• Lecture 2: Data + RegEx
• Lecture 2: Data + RegEx [Notebook]

bias/variance trade-off

• Lecture 11: Regularization
• Lecture 11: Regularization [Notebook]
• Lecture 11: Regularization [Notebook]
• Lecture 11: Regularization [Notebook]

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 26: Boosting Methods for Classification
• Lecture 26: Boosting Methods for Classification [Notebook]
• Lecture 26: Boosting Methods for Classification [Notebook]
• Lecture 25: Boosting Methods for Regression
• Lecture 25: Boosting Methods for Regression [Notebook]
• Lecture 25: Boosting Methods for Regression [Notebook]

bootstrap

• S-Section 03: Multiple Linear and Polynomial Regression [Notebook]
• S-Section 03: Multiple Linear and Polynomial Regression
• Lecture 9: Inference in Linear Regression
• Lecture 9: Inference in Linear Regression [Notebook]
• Lecture 9: Inference in Linear Regression [Notebook]
• Lecture 9: Inference in Linear Regression [Notebook]

Case Study

• Lecture 27: Case Study 2
• Lecture 27: Case Study 2 [Notebook]
• Lecture 27: Case Study 2 [Notebook]

Categorical Data

• Lecture 25: Boosting Methods for Regression
• Lecture 25: Boosting Methods for Regression [Notebook]
• Lecture 25: Boosting Methods for Regression [Notebook]

categorical variables

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

classification

• S-Section 10: Feed Forward Neural Networks: Regularization and Adam optimizer [Notebook]
• S-Section 10: Feed Forward Neural Networks: Regularization and Adam optimizer
• Lecture 26: Boosting Methods for Classification
• Lecture 26: Boosting Methods for Classification [Notebook]
• Lecture 26: Boosting Methods for Classification [Notebook]
• Lecture 22: Classification Trees
• Lecture 22: Classification Trees [Notebook]
• Lecture 18: Multiclass Logistic Regression
• Lecture 18: Multiclass Logistic Regression [Notebook]
• Lecture 18: Multiclass Logistic Regression [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 17: kNN classification and Logistic Regression II
• Lecture 17: kNN classification and Logistic Regression II [Notebook]
• Lecture 17: kNN classification and Logistic Regression II [Notebook]
• Lecture 15: kNN classification and Logistic Regression I
• Lecture 15: kNN classification and Logistic Regression I [Notebook]
• Lecture 15: kNN classification and Logistic Regression I [Notebook]

clustering

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

confidence intervals

• Lecture 10: Hypothesis Testing and Predictive CI
• Lecture 10: Hypothesis Testing and Predictive CI [Notebook]
• Lecture 10: Hypothesis Testing and Predictive CI [Notebook]
• Lecture 10: Hypothesis Testing and Predictive CI [Notebook]
• S-Section 02: kNN and Linear Regression [Notebook]
• S-Section 02: kNN and Linear Regression

cross validation

• S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]
• S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification
• S-Section 04: Regularization and Model Selection
• S-Section 04: Regularization and Model Selection [Notebook]
• Lecture 7: Model Selection
• Lecture 7: Model Selection [Notebook]
• Lecture 7: Model Selection [Notebook]
• Lecture 7: Model Selection [Notebook]
• Lecture 7: Model Selection [Notebook]

data

• Lecture 2: Data + RegEx
• Lecture 2: Data + RegEx [Notebook]

Data Augmentation

• Lecture 32: Regularization methods - Weight decay, data augmentation and dropout
• Lecture 32: Regularization methods - Weight decay, data augmentation and dropout [Notebook]
• Lecture 32: Regularization methods - Weight decay, data augmentation and dropout [Notebook]

Data Science Demo

• Lecture 1: Data Science Demo [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

datasets

• Lecture 2: Data + RegEx
• Lecture 2: Data + RegEx [Notebook]

debugging

• Lecture 13: Thinking critically about models, data, and debugging
• Lecture 13: Thinking critically about models, data, and debugging [Notebook]

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 07: Bagging and Random Forest [Notebook]
• S-Section 07: Bagging and Random Forest
• Lecture 22: Classification Trees
• Lecture 22: Classification Trees [Notebook]

design

• Lecture 14: Visualization
• Lecture 14: Visualization [Notebook]

dimensionality reduction

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

dropout

• S-Section 10: Feed Forward Neural Networks: Regularization and Adam optimizer [Notebook]
• S-Section 10: Feed Forward Neural Networks: Regularization and Adam optimizer
• Lecture 32: Regularization methods - Weight decay, data augmentation and dropout
• Lecture 32: Regularization methods - Weight decay, data augmentation and dropout [Notebook]
• Lecture 32: Regularization methods - Weight decay, data augmentation and dropout [Notebook]

eda

• Lecture 4: Advanced PANDAS
• Lecture 4: EDA + PANDAS [Notebook]
• Lecture 4: EDA + PANDAS [Notebook]
• Lecture 4: EDA + PANDAS [Notebook]
• Lecture 3: Web Scraping + PANDAS
• Lecture 3: Web Scraping + PANDAS [Notebook]
• Lecture 3: Web Scraping + PANDAS [Notebook]

explained variance

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

Feature Interpretation

• Lecture 25: Boosting Methods for Regression
• Lecture 25: Boosting Methods for Regression [Notebook]
• Lecture 25: Boosting Methods for Regression [Notebook]

feed forward

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

Generalized Linear Models

• Advanced Section 3: Generalized Linear Models

Gradient Boositng

• Lecture 25: Boosting Methods for Regression
• Lecture 25: Boosting Methods for Regression [Notebook]
• Lecture 25: Boosting Methods for Regression [Notebook]

Gradient Descent

• Lecture 31: Neural Networks 4 -Back Propagation, SGD
• Lecture 31: Neural Networks 4 -Back Propagation, SGD [Notebook]
• Lecture 31: Neural Networks 4 -Back Propagation, SGD [Notebook]
• Lecture 30: Neural Networks 3: Design Choices II & Gradient Descent
• Lecture 30: Neural Networks 3: Design Choices II & Gradient Descent [Notebook]
• Lecture 30: Neural Networks 3: Design Choices II & Gradient Descent [Notebook]

graphing

• Lecture 14: Visualization
• Lecture 14: Visualization [Notebook]

Hyperparameters

• Lecture 24: Tuning Hyperparameters
• Lecture 24: Tuning Hyperparameters [Notebook]
• Lecture 24: Tuning Hyperparameters [Notebook]
• Lecture 12: Estimation of the Regularization Coefficients using CV and comparison
• Lecture 12: Estimation of the Regularization Coefficients using CV and comparison [Notebook]
• Lecture 12: Estimation of the Regularization Coefficients using CV and comparison [Notebook]

hypothesis testing

• Lecture 10: Hypothesis Testing and Predictive CI
• Lecture 10: Hypothesis Testing and Predictive CI [Notebook]
• Lecture 10: Hypothesis Testing and Predictive CI [Notebook]
• Lecture 10: Hypothesis Testing and Predictive CI [Notebook]
• Advanced Section 1: Linear Algebra and Hypothesis Testing

Imbalanced Data

• Lecture 25: Boosting Methods for Regression
• Lecture 25: Boosting Methods for Regression [Notebook]
• Lecture 25: Boosting Methods for Regression [Notebook]

Imputation

• Lecture 19: Missing Data
• Lecture 19: Missing Data [Notebook]

inference

• Lecture 9: Inference in Linear Regression
• Lecture 9: Inference in Linear Regression [Notebook]
• Lecture 9: Inference in Linear Regression [Notebook]
• Lecture 9: Inference in Linear Regression [Notebook]

interaction terms

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

Interpretation

• Lecture 35: Interpreting Prediction Models
• Lecture 35: Interpreting Prediction Models [Notebook]

Introduction

• Lecture 1: Introduction

k-Nearest Neighbors

• Lecture 5: kNN & Linear Regression
• Lecture 5: kNN & Linear Regression [Notebook]
• Lecture 5: kNN & Linear Regression [Notebook]
• Lecture 5: kNN & Linear Regression [Notebook]

keras

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

kNN

• Lecture 17: kNN classification and Logistic Regression II
• Lecture 17: kNN classification and Logistic Regression II [Notebook]
• Lecture 17: kNN classification and Logistic Regression II [Notebook]
• Lecture 15: kNN classification and Logistic Regression I
• Lecture 15: kNN classification and Logistic Regression I [Notebook]
• Lecture 15: kNN classification and Logistic Regression I [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 k-Nearest Neighbors (kNN)

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

kNN regression

• Lecture 5: kNN & Linear Regression
• Lecture 5: kNN & Linear Regression [Notebook]
• Lecture 5: kNN & Linear Regression [Notebook]
• Lecture 5: kNN & Linear Regression [Notebook]

lasso

• S-Section 04: Regularization and Model Selection
• S-Section 04: Regularization and Model Selection [Notebook]
• Lecture 12: Estimation of the Regularization Coefficients using CV and comparison
• Lecture 12: Estimation of the Regularization Coefficients using CV and comparison [Notebook]
• Lecture 12: Estimation of the Regularization Coefficients using CV and comparison [Notebook]
• Lecture 11: Regularization
• Lecture 11: Regularization [Notebook]
• Lecture 11: Regularization [Notebook]
• Lecture 11: Regularization [Notebook]

Lime

• Lecture 35: Interpreting Prediction Models
• Lecture 35: Interpreting Prediction Models [Notebook]

linear algeba

• Advanced Section 1: Linear Algebra and Hypothesis Testing

linear regression

• S-Section 02: kNN and Linear Regression [Notebook]
• S-Section 02: kNN and Linear Regression
• Lecture 6: Linear Regression [Notebook]
• Lecture 6: Linear Regression [Notebook]
• Lecture 6: Linear Regression [Notebook]
• Lecture 6: Linear Regression [Notebook]
• Lecture 5: kNN & Linear Regression
• Lecture 5: kNN & Linear Regression [Notebook]
• Lecture 5: kNN & Linear Regression [Notebook]
• Lecture 5: kNN & Linear Regression [Notebook]

logistic regression

• S-Section 06: PCA and Logistic Regression [Notebook]
• S-Section 06: PCA and Logistic Regression
• Lecture 18: Multiclass Logistic Regression
• Lecture 18: Multiclass Logistic Regression [Notebook]
• Lecture 18: Multiclass Logistic Regression [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 17: kNN classification and Logistic Regression II
• Lecture 17: kNN classification and Logistic Regression II [Notebook]
• Lecture 17: kNN classification and Logistic Regression II [Notebook]
• Lecture 15: kNN classification and Logistic Regression I
• Lecture 15: kNN classification and Logistic Regression I [Notebook]
• Lecture 15: kNN classification and Logistic Regression I [Notebook]

logistics

• Lecture 1: Introduction

matplotlib

• Lecture 14: Visualization
• Lecture 14: Visualization [Notebook]
• S-Section 01: Introduction to Web Scraping [Notebook]
• S-Section 01: Introduction to Web Scraping

Missing Data

• Lecture 25: Boosting Methods for Regression
• Lecture 25: Boosting Methods for Regression [Notebook]
• Lecture 25: Boosting Methods for Regression [Notebook]
• Lecture 19: Missing Data
• Lecture 19: Missing Data [Notebook]

MLP

• Lecture 28: Neural Networks 1 - Perceptron & MLP
• Lecture 28: Neural Networks 1 - Perceptron & MLP [Notebook]
• Lecture 28: Neural Networks 1 - Perceptron & MLP [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
• S-Section 04: Regularization and Model Selection [Notebook]
• S-Section 03: Multiple Linear and Polynomial Regression [Notebook]
• S-Section 03: Multiple Linear and Polynomial Regression
• Lecture 7: Model Selection
• Lecture 7: Model Selection [Notebook]
• Lecture 7: Model Selection [Notebook]
• Lecture 7: Model Selection [Notebook]
• Lecture 7: Model Selection [Notebook]

models

• Lecture 13: Thinking critically about models, data, and debugging
• Lecture 13: Thinking critically about models, data, and debugging [Notebook]

MSE

• Lecture 5: kNN & Linear Regression
• Lecture 5: kNN & Linear Regression [Notebook]
• Lecture 5: kNN & Linear Regression [Notebook]
• Lecture 5: kNN & Linear Regression [Notebook]

multiple linear regression

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

• Lecture 33: Full Example of Regression & Classification FFNN
• Lecture 33: Full Example of Regression & Classification FFNN [Notebook]
• S-Section 10: Feed Forward Neural Networks: Regularization and Adam optimizer [Notebook]
• S-Section 10: Feed Forward Neural Networks: Regularization and Adam optimizer
• Lecture 32: Regularization methods - Weight decay, data augmentation and dropout
• Lecture 32: Regularization methods - Weight decay, data augmentation and dropout [Notebook]
• Lecture 32: Regularization methods - Weight decay, data augmentation and dropout [Notebook]
• Lecture 30: Neural Networks 3: Design Choices II & Gradient Descent
• Lecture 30: Neural Networks 3: Design Choices II & Gradient Descent [Notebook]
• Lecture 30: Neural Networks 3: Design Choices II & Gradient Descent [Notebook]
• S-Section 09: Feed forward neural networks [Notebook]
• S-Section 09: Feed forward neural networks
• Lecture 29:Neural Networks 2 - Anatomy of NN & Design Choices
• Lecture 29:Neural Networks 2 - Anatomy of NN & Design Choices [Notebook]
• Lecture 28: Neural Networks 1 - Perceptron & MLP
• Lecture 28: Neural Networks 1 - Perceptron & MLP [Notebook]
• Lecture 28: Neural Networks 1 - Perceptron & MLP [Notebook]

nonparametric

• Lecture 13: Thinking critically about models, data, and debugging
• Lecture 13: Thinking critically about models, data, and debugging [Notebook]

normalization

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

Optimizers

• Advanced Section 6: Deep into Solvers
• Lecture 31: Neural Networks 4 -Back Propagation, SGD
• Lecture 31: Neural Networks 4 -Back Propagation, SGD [Notebook]
• Lecture 31: Neural Networks 4 -Back Propagation, SGD [Notebook]

overfitting

• S-Section 10: Feed Forward Neural Networks: Regularization and Adam optimizer [Notebook]
• S-Section 10: Feed Forward Neural Networks: Regularization and Adam optimizer

pairplot

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

pandas

• Lecture 4: Advanced PANDAS
• Lecture 4: EDA + PANDAS [Notebook]
• Lecture 4: EDA + PANDAS [Notebook]
• Lecture 4: EDA + PANDAS [Notebook]
• S-Section 01: Introduction to Web Scraping [Notebook]
• S-Section 01: Introduction to Web Scraping
• Lecture 3: Web Scraping + PANDAS
• Lecture 3: Web Scraping + PANDAS [Notebook]
• Lecture 3: Web Scraping + PANDAS [Notebook]

parametric

• Lecture 13: Thinking critically about models, data, and debugging
• Lecture 13: Thinking critically about models, data, and debugging [Notebook]

parsing

• Lecture 3: Web Scraping + PANDAS
• Lecture 3: Web Scraping + PANDAS [Notebook]
• Lecture 3: Web Scraping + PANDAS [Notebook]

PCA

• Advanced Section 4: Mathematical Foundations of PCA
• Lecture 20: PCA
• Lecture 20: PCA [Notebook]

Perceptron

• Lecture 28: Neural Networks 1 - Perceptron & MLP
• Lecture 28: Neural Networks 1 - Perceptron & MLP [Notebook]
• Lecture 28: Neural Networks 1 - Perceptron & MLP [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

plotting

• Lecture 14: Visualization
• Lecture 14: Visualization [Notebook]

polynomial regression

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

principal components analysis

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

probability

• Lecture 8: Probability
• Lecture 8: Probability [Notebook]
• Lecture 8: Probability [Notebook]

R-squared

• Lecture 5: kNN & Linear Regression
• Lecture 5: kNN & Linear Regression [Notebook]
• Lecture 5: kNN & Linear Regression [Notebook]
• Lecture 5: kNN & Linear Regression [Notebook]

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
• Lecture 25: Boosting Methods for Regression
• Lecture 25: Boosting Methods for Regression [Notebook]
• Lecture 25: Boosting Methods for Regression [Notebook]
• Lecture 24: Tuning Hyperparameters
• Lecture 24: Tuning Hyperparameters [Notebook]
• Lecture 24: Tuning Hyperparameters [Notebook]
• S-Section 07: Bagging and Random Forest [Notebook]
• S-Section 07: Bagging and Random Forest

regex

• Lecture 2: Data + RegEx
• Lecture 2: Data + RegEx [Notebook]

regression

• S-Section 10: Feed Forward Neural Networks: Regularization and Adam optimizer [Notebook]
• S-Section 10: Feed Forward Neural Networks: Regularization and Adam optimizer

regular expressions

• Lecture 2: Data + RegEx
• Lecture 2: Data + RegEx [Notebook]

regularization

• S-Section 10: Feed Forward Neural Networks: Regularization and Adam optimizer [Notebook]
• S-Section 10: Feed Forward Neural Networks: Regularization and Adam optimizer
• Lecture 32: Regularization methods - Weight decay, data augmentation and dropout
• Lecture 32: Regularization methods - Weight decay, data augmentation and dropout [Notebook]
• Lecture 32: Regularization methods - Weight decay, data augmentation and dropout [Notebook]
• S-Section 04: Regularization and Model Selection
• S-Section 04: Regularization and Model Selection [Notebook]
• Advanced Section 2: Methods of regularization and their justifications
• Lecture 11: Regularization
• Lecture 11: Regularization [Notebook]
• Lecture 11: Regularization [Notebook]
• Lecture 11: Regularization [Notebook]

requests

• Lecture 3: Web Scraping + PANDAS
• Lecture 3: Web Scraping + PANDAS [Notebook]
• Lecture 3: Web Scraping + PANDAS [Notebook]

ridge

• S-Section 04: Regularization and Model Selection
• S-Section 04: Regularization and Model Selection [Notebook]
• Lecture 12: Estimation of the Regularization Coefficients using CV and comparison
• Lecture 12: Estimation of the Regularization Coefficients using CV and comparison [Notebook]
• Lecture 12: Estimation of the Regularization Coefficients using CV and comparison [Notebook]
• Lecture 11: Regularization
• Lecture 11: Regularization [Notebook]
• Lecture 11: Regularization [Notebook]
• Lecture 11: Regularization [Notebook]

SHAP

• Lecture 35: Interpreting Prediction Models
• Lecture 35: Interpreting Prediction Models [Notebook]

sklearn

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

Stacking

• Advanced Section 5: Stacking and mixture of experts

standardization

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

standarization

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

statsmodels

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

Stochastic Gradient Descent

• Advanced Section 6: Deep into Solvers
• Lecture 31: Neural Networks 4 -Back Propagation, SGD
• Lecture 31: Neural Networks 4 -Back Propagation, SGD [Notebook]
• Lecture 31: Neural Networks 4 -Back Propagation, SGD [Notebook]
• S-Section 09: Feed forward neural networks [Notebook]
• S-Section 09: Feed forward neural networks

tensorflow

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

testing data

• Lecture 13: Thinking critically about models, data, and debugging
• Lecture 13: Thinking critically about models, data, and debugging [Notebook]

The Data Science Process

• Lecture 1: Data Science Demo [Notebook]
• Lecture 1: Data Science Demo [Notebook]

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

training data

• Lecture 13: Thinking critically about models, data, and debugging
• Lecture 13: Thinking critically about models, data, and debugging [Notebook]

Tuning

• Lecture 24: Tuning Hyperparameters
• Lecture 24: Tuning Hyperparameters [Notebook]
• Lecture 24: Tuning Hyperparameters [Notebook]

underfitting

• S-Section 10: Feed Forward Neural Networks: Regularization and Adam optimizer [Notebook]
• S-Section 10: Feed Forward Neural Networks: Regularization and Adam optimizer

visualization

• Lecture 14: Visualization
• Lecture 14: Visualization [Notebook]

web scraping

• Lecture 3: Web Scraping + PANDAS
• Lecture 3: Web Scraping + PANDAS [Notebook]
• Lecture 3: Web Scraping + PANDAS [Notebook]

Weight Decay

• Lecture 32: Regularization methods - Weight decay, data augmentation and dropout
• Lecture 32: Regularization methods - Weight decay, data augmentation and dropout [Notebook]
• Lecture 32: Regularization methods - Weight decay, data augmentation and dropout [Notebook]