🧠 Phase 2: Machine Learning (Days 16–45)

🎯 Objective

This phase focuses on building strong machine learning foundations using Scikit-learn with an engineering mindset.

The goal is not theory memorization —
but building, validating, and saving real ML models.

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📅 Phase Breakdown

📌 Week 3–4 (Days 16–30): ML Fundamentals

Core tools:

• Python
• NumPy
• Pandas
• Scikit-learn
• Joblib

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🏗 Foundation (Days 16–19)

Day 16

• What is Machine Learning
• Supervised vs Unsupervised
• Train/Test Split
• Pipeline mindset

Day 17

• Linear Regression implementation

Day 18

• Regression Metrics:
  • MAE
  • MSE
  • RMSE
  • R² Score

Day 19

• Feature Scaling
  • StandardScaler
  • Why scaling matters

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🧮 Classification (Days 20–22)

Day 20

• Logistic Regression

Day 21

• Classification Metrics:
  • Accuracy
  • Precision
  • Recall
  • F1 Score

Day 22

• Confusion Matrix
• ROC Curve
• ROC-AUC Score

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🌳 Tree-Based Models (Days 23–24)

Day 23

• Decision Tree (Regressor & Classifier)

Day 24

• Random Forest
• Overfitting vs Underfitting

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🔁 Model Validation & Optimization (Days 25–26)

Day 25

• Cross Validation
• Why single split is dangerous

Day 26

• Hyperparameter Tuning
• GridSearchCV
• Model comparison

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💾 Production Thinking (Days 27–29)

Day 27

• Model saving using:
  • joblib
  • pickle

Day 28

• End-to-end ML pipeline notebook
  • Preprocessing
  • Model
  • Evaluation
  • Saving

Day 29

• Code refactoring
• Clean structure
• Removing redundant steps

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🎤 Day 30 – Interview Preparation

• Common ML interview questions
• Bias-variance tradeoff
• Overfitting explanation
• Metric comparison scenarios
• Model selection reasoning

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🧠 Key Concepts Learned

• Proper train-test workflow
• Importance of cross-validation
• Difference between regression & classification metrics
• Tree models vs linear models
• Hyperparameter tuning process
• Pipeline building
• Model reproducibility

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📊 Skills Gained

✔ Implement ML models from scratch using Sklearn
✔ Evaluate using correct metrics
✔ Validate models properly
✔ Tune hyperparameters
✔ Save production-ready models
✔ Explain models confidently

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🛠 Tools Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Joblib

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🚀 Outcome

By the end of this phase:

• Able to build end-to-end ML workflows
• Understand evaluation deeply
• Think in pipeline structure
• Ready to move toward advanced ML & real-world projects

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👨‍💻 Long-Term Direction

This phase builds the foundation for:

• Advanced ML
• Model deployment
• AI Engineering path