You will learn more advanced techniques in Regression , distinguish between L1 , L2 methods and understand model validation
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In Class Instruction: 4 Hours
- In Class code along Dataset: Iowa Housing Prices
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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
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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
- Why?
- L1 + intuition about its effect
- L2 + intuition about its effect
- L1 + L2 (short mention)
- A worked-out example
After this session , you'll be able to
- Understand the various problems of Linear Regression
- Understand the various problems of Linear Regression
- Learn about ways to handle Non-Linear Data
- Understand Regularization and its types
- Distinguish between L1 and L2
- Understand Bias-Variance Trade-off
- Learn about Model Validation
Check the Jupyter Notebook in the top right of the screen
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.