🚀 Module 2 Complete: Regression in Machine Learning!
Continuing my journey with #mlzoomcamp by @DataTalksClub! In this module, I dove deep into regression techniques to predict continuous values using a car price dataset.
Key takeaways:
• EDA & Data Prep: Missing value imputation, feature matrix preparation, and train/val/test splits.
• Linear Regression from Scratch: Implementing matrix vector formulas and linear algebra operations without relying solely on black-box libraries.
• Model Evaluation: Measuring model performance using Root Mean Squared Error (RMSE).
• Regularization & Feature Engineering: Handling multi-collinearity and stabilizing linear regression using Ridge Regularization (L2).
Onward to the next module! 💻📊
Shoutout to Alexey Grigorev and the DataTalks.Club team for a fantastic module.
Top comments (0)