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ML pipeline
Day-12 – Scikit-Learn Pipeline + End-to-End ML Workflow
Until now: You manually cleaned data Trained models separately Today you learn: How professional ML workflows are built 🚀 🎯 Goal of Day-…
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Feature Engineering
Day-11 – Feature Engineering + Data Cleaning
Welcome to Day-11 – Feature Engineering + Data Cleaning (REAL INDUSTRY WORK) Today is SUPER important. Most beginners think: “AI = model training” …
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underfitting
Day-10 – Train/Test Split + Overfitting
Today we will learn one of the MOST important concepts in Machine Learning. Without this: ❌ Models look good ❌ But fail in real world 🎯 Goal of D…
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Model Evaluation
Day-9 – Model Evaluation
🎯 Goal of Day-9 You will: ✅ Measure model performance ✅ Understand accuracy ✅ Learn precision & recall ✅ Use confusion matrix 🧠Why Evalua…
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Random Forest
Day-8 – Random Forest (Powerful Industry Model)
Now we upgrade from a single tree → multiple trees working together . 🎯 Goal of Day-8 You will: ✅ Understand ensemble learning ✅ Build Random Fo…
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ML
Day-7 – Decision Tree + Model Comparison
Now we move from basic ML → smarter ML models . 🎯 Goal of Day-7 We will: ✅ Understand Decision Tree ✅ Build a better classification model ✅ Comp…
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AI Engineer
Day-6 – Logistic Regression (Classification – Real-world ML)
Welcome to Day-6 – Logistic Regression (Classification) 🎯 Goal of Day-6 You will: ✅ Understand classification ✅ Build first classification model …
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