You will learn more advanced techniques in Regression , distinguish between L1 , L2 methods and understand model validation

• In Class Instruction: 4 Hours

  • In Class code along Dataset: Iowa Housing Prices

• Project Dataset:Iowa Housing Prices

  • Estimated Time to complete Project Tasks: 1 Hour
  • Total sub tasks within the Project: 6
  • Complexity of sub tasks : Mid to High

• Skills Rehearsed

  • Apply Advanced Linear Regression techniques in Python using sklearn

• Recap of previous session
• Instructor Concept building
• In Class Quiz Administration
• Periodic Recap - Closer to the end of session
• In Class Assignments - Motivation

1. Why?
2. L1 + intuition about its effect
3. L2 + intuition about its effect
4. L1 + L2 (short mention)
5. A worked-out example

After this session , you'll be able to

1. Understand the various problems of Linear Regression
2. Understand the various problems of Linear Regression
3. Learn about ways to handle Non-Linear Data
4. Understand Regularization and its types
5. Distinguish between L1 and L2
6. Understand Bias-Variance Trade-off
7. Learn about Model Validation

Check the Jupyter Notebook in the top right of the screen

1. Ridge and Lasso in Python
2. Overview of Bias-Variance Trade-Off

Iowa Dataset

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this dataset will help you determine aspects which influence price of a property other than sq.ft. area and locality.