# 🎓 Complete Machine Learning & Data Science Curriculum

## **26 Modules • From Zero to Production-Ready ML Engineer**

Welcome to the most comprehensive, hands-on Data Science practice curriculum ever created. This series takes you from **Core Python** to deploying **production ML systems**.

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## 📚 **Curriculum Structure**

### **🐍 Phase 1: Foundations (Modules 01-02)**

1. **[01_Python_Core_Mastery.ipynb](./01_Python_Core_Mastery.ipynb)**
   - **Basics**: Strings, F-Strings, Slicing, Data Structures
   - **Intermediate**: Comprehensions, Generators, Decorators
   - **Advanced**: OOP (Dunder Methods, Static Methods), Async/Await
   - **Expert**: Multithreading vs Multiprocessing (GIL), Singleton Pattern
   
2. **[02_Statistics_Foundations.ipynb](./02_Statistics_Foundations.ipynb)**
   - Central Tendency, Dispersion, Z-Scores
   - Correlation, Hypothesis Testing (p-values)
   - Links: [Statistics Course](https://aashishgarg13.github.io/DataScience/complete-statistics/)

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### **🔧 Phase 2: Data Science Toolbox (Modules 03-07)**

3. **[03_NumPy_Practice.ipynb](./03_NumPy_Practice.ipynb)** - Numerical Computing
4. **[04_Pandas_Practice.ipynb](./04_Pandas_Practice.ipynb)** - Data Manipulation
5. **[05_Matplotlib_Seaborn_Practice.ipynb](./05_Matplotlib_Seaborn_Practice.ipynb)** - Visualization
6. **[06_EDA_and_Feature_Engineering.ipynb](./06_EDA_and_Feature_Engineering.ipynb)** - Real Titanic Dataset
7. **[07_Scikit_Learn_Practice.ipynb](./07_Scikit_Learn_Practice.ipynb)** - Pipelines & GridSearch

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### **🤖 Phase 3: Supervised Learning (Modules 08-14)**

8. **[08_Linear_Regression.ipynb](./08_Linear_Regression.ipynb)** - Diamonds Dataset
9. **[09_Logistic_Regression.ipynb](./09_Logistic_Regression.ipynb)** - Breast Cancer Dataset
10. **[10_Support_Vector_Machines.ipynb](./10_Support_Vector_Machines.ipynb)** - Kernel Trick
11. **[11_K_Nearest_Neighbors.ipynb](./11_K_Nearest_Neighbors.ipynb)** - Iris Dataset
12. **[12_Naive_Bayes.ipynb](./12_Naive_Bayes.ipynb)** - Text Classification
13. **[13_Decision_Trees_and_Random_Forests.ipynb](./13_Decision_Trees_and_Random_Forests.ipynb)** - Penguins Dataset
14. **[14_Gradient_Boosting_XGBoost.ipynb](./14_Gradient_Boosting_XGBoost.ipynb)** - Kaggle Champion

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### **🔍 Phase 4: Unsupervised Learning (Modules 15-16)**

15. **[15_KMeans_Clustering.ipynb](./15_KMeans_Clustering.ipynb)** - Elbow Method
16. **[16_Dimensionality_Reduction_PCA.ipynb](./16_Dimensionality_Reduction_PCA.ipynb)** - Digits Dataset

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### **🧠 Phase 5: Advanced ML (Modules 17-20)**

17. **[17_Neural_Networks_Deep_Learning.ipynb](./17_Neural_Networks_Deep_Learning.ipynb)** - MNIST with MLPClassifier
18. **[18_Time_Series_Analysis.ipynb](./18_Time_Series_Analysis.ipynb)** - Air Passengers Dataset
19. **[19_Natural_Language_Processing_NLP.ipynb](./19_Natural_Language_Processing_NLP.ipynb)** - Sentiment Analysis
20. **[20_Reinforcement_Learning_Basics.ipynb](./20_Reinforcement_Learning_Basics.ipynb)** - Q-Learning Grid World

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### **💼 Phase 6: Industry Skills (Modules 21-23)**

21. **[21_Kaggle_Project_Medical_Costs.ipynb](./21_Kaggle_Project_Medical_Costs.ipynb)** - Full Pipeline
22. **[22_SQL_for_Data_Science.ipynb](./22_SQL_for_Data_Science.ipynb)** - Database Integration
23. **[23_Model_Explainability_SHAP.ipynb](./23_Model_Explainability_SHAP.ipynb)** - XAI with SHAP

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### **🚀 Phase 7: Production & Deployment (Modules 24-26)** ⭐ NEW!

24. **[24_Deep_Learning_TensorFlow.ipynb](./24_Deep_Learning_TensorFlow.ipynb)** - TensorFlow/Keras & CNNs
25. **[25_Model_Deployment_Streamlit.ipynb](./25_Model_Deployment_Streamlit.ipynb)** - Web App Deployment
26. **[26_End_to_End_ML_Project.ipynb](./26_End_to_End_ML_Project.ipynb)** - Production Pipeline

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## 🛠️ **Setup Instructions**

### **1. Install Dependencies**
```bash
pip install -r requirements.txt
```

### **2. Launch Jupyter**
```bash
jupyter notebook
```

### **3. Start Learning!**
Open `01_Python_Core_Mastery.ipynb` and work sequentially through Module 26.

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## 🌐 **Website Integration**

This curriculum is designed to work seamlessly with the **[DataScience Learning Hub](https://aashishgarg13.github.io/DataScience/)**. Each ML module links to interactive visualizations and theory.

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## 📊 **What Makes This Curriculum Unique?**

✅ **26 Complete Modules** - From Python basics to production deployment  
✅ **Real Datasets** - Titanic, MNIST, Kaggle Insurance, and more  
✅ **Website Integration** - Links to visual demos for every concept  
✅ **Industry-Ready** - Includes SQL, SHAP, Design Patterns, Async programming  
✅ **Production Skills** - TensorFlow, Streamlit, Model Deployment  
✅ **Git-Ready** - Initialized with version control  

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## 📁 **Key Files**

- **[CURRICULUM_REVIEW.md](./CURRICULUM_REVIEW.md)** - Quality assessment of all modules
- **[README_Resources.md](./README_Resources.md)** - External learning resources
- **[requirements.txt](./requirements.txt)** - All dependencies

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## 🎯 **Who Is This For?**

- 🎓 **Students** learning Data Science from scratch
- 💼 **Professionals** preparing for DS/ML interviews
- 🧑‍💻 **Developers** transitioning to ML engineering
- 🏆 **Kagglers** wanting structured practice

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## 📈 **Learning Path**

**Beginner** (Weeks 1-4): Modules 01-07  
**Intermediate** (Weeks 5-8): Modules 08-16  
**Advanced** (Weeks 9-12): Modules 17-23  
**Expert** (Weeks 13-14): Modules 24-26  

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## 🏆 **After Completion**

You will be able to:
- ✅ Build end-to-end ML systems
- ✅ Deploy models as web applications
- ✅ Compete in Kaggle competitions
- ✅ Pass ML engineering interviews
- ✅ Explain model decisions with SHAP

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## 🤝 **Contributing**

This curriculum is part of a personal learning journey integrated with [aashishgarg13.github.io/DataScience/](https://aashishgarg13.github.io/DataScience/).

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## 📝 **License**

For educational purposes. Feel free to learn and adapt!

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**Ready to become a Machine Learning Engineer?** Start with `01_Python_Core_Mastery.ipynb`! 🚀