## Machine Learning - Coursera

Notes from the Coursera Machine Learning courses from University of Washington on Coursera.

Machine Learning Foundations

#### Week 2 - Regression: Predicting House Prices

- Notes
- Predicting house prices using sq ft
- Regression models
- Regression coefficients
- Assignment 1
- Explore house sale data using SFrame

#### Week 2

- Assignment 2

#### Week 3

- Assignment 3

#### Week 4

#### Week 5

- Assignment 5

#### Week 6

Machine Learning: Regression

#### Week 1 - Simple Linear Regression

- Notes
- Predicting house prices using sq ft
- Regression models
- Regression Coefficients
- Residual Sum of Squares
- Optimize single variable function - closed form / Hill ascent & descent
- Optimize RSS - closed form approach
- Optimize RSS - Gradient approaching zero

- Assignment 1
- Explore house sale data using SFrame
- Train vs Test data
- Build generic simple linear regression function
- Calculate RSS
- Predict price given sq ft
- Predict sq ft given price
- Predict price given #bedrooms
- Test different models (compare RSS)

#### Week 2 - Multiple Regression

- Notes
- Multiple Regression model - single variable
- Multiple Regression model - multiple variable
- Coefficients of regression with multiple features
- Matrix Arithmetic
- Multiple Regression in matrix notation (single input)
- Multiple Regression in matrix notation (multiple inputs)
- Minimize Cost - Gradient Descent

- Assignment 2.1
- Explore house sale data using SFrame
- Train vs Test data
- Build multiple regression model using Square feet, number of bedrooms and number of bathrooms
- Explore model
- Interpretations of Coefficients
- Making Predictions
- RSS
- Add more features
- Comparing models

- Assignment 2.2
- Implement Gradient Descent Algorithm
- Compare single variable vs multiple variable models
- Compare with in built graphlot model

#### Week 3 - Assessing Performance

- Notes
- Access model performance
- Training error
- Generalization error
- Test error
- Sources of error -> irreducible error, bias and variance
- Variance Bian tradeoff

- Assignment 3
- Use matplotlib to visualize polynomial regressions
- Use a validation set to select a polynomial degree
- Assess the final fit using test data

**price vs sqft**

**price vs quadratic function(sqft)**

**price vs cubic function(sqft)**

**price vs 15th polynomial function(sqft)**

#### Week 4 - Ridge Regression

- Assignment 4

#### Week 5 - Feature Selection & Lasso

- Assignment 5

#### Week 6 - Nearest Neighbors & Kernel Regression

- Assignment 6