Machine Learning Tutorial 0/98 lessons ~6 min read Lesson 8
Feature Scaling
What is Feature Scaling?
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Focus
7 guided sections
Practice signal
Examples included
Career prep
Foundation builder
Introduction
What is Feature Scaling? Normalize or standardize features so algorithms compare values fairly. Machine learning systems learn patterns from data instead of hard-coded rules.
Understanding the topic
How Feature Scaling works:
- Normalize or standardize features so algorithms compare values fairly.
- Prepare or explore data as needed.
- Train or apply the model/technique.
- Evaluate results and iterate.
| Term | Description |
|---|---|
| Feature Scaling | Normalize or standardize features so algorithms compare values fairly |
| Training data | Examples used to learn patterns. |
| Features | Input variables (columns) fed to the model. |
| Target / label | What you predict (supervised learning). |
Step-by-step explanation
- Understand — Learn when and why to use Feature Scaling.
- Prepare data — Load, clean, and split datasets.
- Apply — Fit model or run algorithm in Python/sklearn.
- Evaluate — Measure accuracy, loss, or cluster quality.
Execution workflow
1Feature Scaling workflow
1 / 4Understand
Learn when and why to use Feature Scaling.
Best practices
- Split data into train/validation/test before tuning.
- Scale numeric features when algorithms are distance-based.
- Always evaluate on held-out data — not training accuracy alone.
Common mistakes
- Training on test data (data leakage).
- Ignoring class imbalance in classification metrics.
- Using accuracy alone on imbalanced datasets.
Summary
Feature Scaling is a core machine learning topic. Normalize or standardize features so algorithms compare values fairly
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