From Data to Decisions: Building Your First Machine Learning Model"
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)
