Data Science: Foundations & Regression (Python) 

Exploring Machine Learning and its Types

Install Anaconda

Python and Jupyter Demo

Introduction

Reading from a CSV

Selecting data and finding the most common complaint type

Which borough has the most noise complaints?

Which weekday do people bike the most?

Which month was the snowiest?

Cleaning Messy Data

How to deal with timestamps

Loading data from SQL databases

Summary

Introduction

What is linear regression?

The advertising dataset

EDA questions on advertising data

Simple Linear Regression

Hypothesis testing and p-values

R squared

Multiple linear regression

Model and feature selection

Model evaluation

Handling categorical features

Summary

Introduction

Predicting a continuous response

Quick refresher on linear regression

Predicting a categorical response

Using logistic regression

Probability , odds, log-odds

What is logistic regression?

Interpreting logistic regression

Using logistic regression with categorical features

Advantages and disadvantages of logistic regression

Summary

Introduction

Train/test split

K-fold cross-validation

Cross-validation continued

Summary

Introduction

Overfitting

Overfitting with linear models

Regularizing linear models

Ridge and Lasso Regularization

Regularization using scikit-learn

Regularizing logistic models

Pipeline and GridSearchCV

Comparing regularized with unregularized models