🟪 1-Minute Summary
Supervised learning trains models on labeled data (input-output pairs) to predict outcomes for new data. Two types: Regression (continuous target: price, temperature) and Classification (categorical target: yes/no, categories). Process: train on labeled data → validate → test on unseen data. Success requires good features, sufficient data, and appropriate algorithm selection.
🟦 Core Notes (Must-Know)
What is Supervised Learning?
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Regression vs Classification
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The Supervised Learning Workflow
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- Data collection and labeling
- Train-test split
- Feature engineering
- Model selection
- Training
- Evaluation
- Tuning
Common Algorithms
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🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“What’s the difference between supervised and unsupervised learning?”
- [Answer: Supervised has labels, unsupervised doesn’t]
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“When would you use regression vs classification?”
- [Answer: Regression for continuous, classification for categorical]
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“Walk me through the supervised learning pipeline”
- [Answer framework to be filled in]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Not splitting data before any preprocessing
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Mistake 2: Using training accuracy to evaluate
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🟩 Mini Example (Quick Application)
Scenario
[House price prediction vs spam detection]
Solution
from sklearn.model_selection import train_test_split
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