Fixed 90% of Beginner ML Mistakes using Wine dataset— Scaling, Regularization, Feature Selection
Welcome back to CodeWithYB!
Data Preprocessing part 2
In today’s masterclass, I break down the MOST misunderstood concepts in Machine Learning — concepts that, if done wrong, will completely destroy your model’s accuracy.
In this video, you’ll learn:
🔹 Why scaling your features is ESSENTIAL (and what happens if you don’t)
🔹 Standardization vs Min–Max Normalization — which one should you use?
🔹 Which ML algorithms require scaling and which ones don’t
🔹 What regularization REALLY does to fight overfitting
🔹 The true difference between L1 (Lasso) and L2 (Ridge) — no fluff
🔹 Why neither L1 nor L2 is “better”… and when each one wins
🔹 Regularization paths and what shrinking weights tell you about feature importance
🔹 Sequential Feature Selection (SBS) — a greedy but powerful feature reduction algorithm
This video is perfect for beginners AND intermediate practitioners who want to actually understand how ML models behave under scaling and regularization — not just memorize formulas.
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