Overview: This session introduces participants to the practical steps of building an ML model—from understanding the problem and preparing the data, to training, evaluating, and improving a basic model. It bridges theory with hands-on practice using a real dataset.

Key Highlights:

What is Machine Learning really? (short intro)

Types of ML problems (Classification, Regression)

Data preprocessing & feature selection

Training a model (using Scikit-learn or another beginner-friendly library)

Evaluating performance (accuracy, confusion matrix, etc.)

Real-world use cases

Tips to move from beginner to intermediate (projects, resources, tools)