• Must have some python programming experience.

• Beginner to intermediate learners familiar with basic Python, NumPy, and Pandas, looking to take their first steps into supervised machine learning with a focus on predicting continuous value.

Course Outline:

1. Introduction to Regression Problems
   • What is regression? Real-world examples (house prices, temperature forecasting, etc.)
   • Target variable: continuous vs. categorical
   • Key concepts: features, target, training/testing
2. Linear Regression
   • Intuition behind fitting a line to data
   • Assumptions of linear regression
   • Using scikit-learn to:
     1. Training a Linear Regression model
     2. Make predictions

• Evaluate using MAE, MSE and R²

• Hands-on: Build model using Linear Regression Algorithm

3. Decision Tree Regression
   • Visual intuition: splitting data into decision nodes
   • Pros and cons (non-linearity, overfitting risk)
   • Soring, grouping and aggregating
   • Using Decision Tree Regressor from scikit-learn
   • Hands-on: Compare with Linear Regression on same dataset
4. Random Forest Regression
   • Ensemble learning basics: what is a Random Forest?
   • How multiple trees improve accuracy and reduce overfitting
   • Training and evaluating a Random Forest Regressor
   • Feature importance and model interpretability
   • Hands-on: Compare with Linear Regression & Decision Tree on same dataset

Training material provided: Yes (Digital format)

Hands-on Lab: Instructions will be provided to install Jupyter notebook and other required python libraries. Students can opt to use ‘Google Colaboratory’ if they do not want to install these tools