Week 1 – Machine Learning Foundations + First Project

📌 Goal: Build your technical intuition. Understand what happens before GenAI.

Topics Covered:

• Supervised vs. Unsupervised Learning
• Classification vs. Regression
• Exploratory Data Analysis (EDA)
• Data pipelines using pandas
• Model building using scikit-learn
• Evaluating performance (accuracy, MAE, confusion matrix, etc.)

Tools & Libraries:

• pandas, scikit-learn, Kaggle datasets

Project:

• One hands-on ML project (e.g., Titanic classification, House price regression)
• Submit Jupyter Notebook + GitHub repo

Outcome:

• ✅ You’ll understand the full ML flow: Data → Model → Evaluate → Improve
• ✅ You'll be able to explain your project to a non-technical audience
• ✅ Reflection exercise: "What surprised me about ML, and how does it relate to AI?"