Struggling with inconsistent data in your ML models? 🤔 Feature Scaling is one of the most important preprocessing steps in Machine Learning that ensures your algorithms work efficiently and accurately.
In this video, you’ll learn: ✅ What Feature Scaling is & why it matters ✅ When to apply Feature Scaling in ML workflows ✅ Common techniques:
Min-Max Scaling (Normalization)
Standardization (Z-score Normalization)
Robust Scaling (for outliers)
Log Transformation (fixing skewed data) ✅ Advanced applications in Regression, Classification, and Deep Learning ✅ How to implement Feature Scaling in Python using Scikit-learn & Pandas
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