Exercise goal

• Learn the basics of Machine Learning
• Learn what an ML model
• Learn your first ML model which is Linear Regression
• Learn how to evaluate models through a 'Loss function'
• Learn how to optimize a model through Gradient Descent

Data

The file housing_prices.csv (see ./data/housing_prices.csv).

Data source/credit: Kaggle.

Task

• Follow the Jupyter Notebook and complete the required tasks:

linear-regression.ipynb

Resources on linear regression

• [Mandatory for Beginners] Indepth Theoretical Videos for Linear Regression from Andrew NG's Machine Learning Course

  • [Video] Linear Regression in one variable (Refer videos from 2.1 - 2.7)
  • [Video] Linear Regression with multiple variables (Refer videos from 4.1 - 4.7)

• [Optional] Alternative Explanation for Linear Regression

  • [Video] StatQuest: Linear Models Pt.1 - Linear Regression

• [Optional] Read this article if you have a basic idea of Linear Regression

  • [Blog] Everything You Need To Know About Linear Regression

• [Mandatory for Beginners] After getting the theoretical background for Linear Regression learn how to implement it practically using Sklearn

  • [Video] Linear Regression Python Sklearn [FROM SCRATCH]