Feature Scaling Secrets ⚖️ in Machine Learning | AI ML 2026 🚀

Описание: In this 10‑minute educational video, Dhaarini AI-Tech Research Academy explains Feature Scaling—a critical preprocessing step in Machine Learning—step by step in English. 🌐

🔹 The Hook (0:00 – 1:30): Recipe analogy (500 grams vs. 1 teaspoon) to show unit distortion. Visual comparison of unscaled vs. scaled data where "Salary" dwarfs "Age."
🔹 Why Scaling Matters (1:30 – 3:30): Gradient Descent optimization, KNN distance metrics, Ridge/Lasso regularization—all explained with clear visuals.
🔹 Math Breakdown (3:30 – 6:30): Standardization (Z-score), Normalization (Min-Max), Robust Scaling, Max-Abs Scaling—formulas and use cases.
🔹 Sklearn Implementation (6:30 – 9:00): train_test_split, StandardScaler fit_transform, pipelines, and how to avoid data leakage.
🔹 Advanced Transforms (9:00 – 10:00): PowerTransformer (Yeo-Johnson/Box-Cox), and why tree-based models don’t need scaling.

🎯 Outcome: By the end of this video, students, researchers, and professionals will understand feature scaling techniques and learn how to implement them in Python using Scikit-learn.

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Feature Scaling tutorial, Machine Learning preprocessing, Data Science explained, Scikit-learn demo, Python ML, AI Training English, ML Projects, Normalization vs Standardization, Robust Scaling, Gradient Descent optimization

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