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🎓 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.
📚 Curriculum Structure
🐍 Phase 1: Foundations (Modules 01-02)
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- 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
02_Statistics_Foundations.ipynb
- Central Tendency, Dispersion, Z-Scores
- Correlation, Hypothesis Testing (p-values)
- Links: Statistics Course
🔧 Phase 2: Data Science Toolbox (Modules 03-07)
- 03_NumPy_Practice.ipynb - Numerical Computing
- 04_Pandas_Practice.ipynb - Data Manipulation
- 05_Matplotlib_Seaborn_Practice.ipynb - Visualization
- 06_EDA_and_Feature_Engineering.ipynb - Real Titanic Dataset
- 07_Scikit_Learn_Practice.ipynb - Pipelines & GridSearch
🤖 Phase 3: Supervised Learning (Modules 08-14)
- 08_Linear_Regression.ipynb - Diamonds Dataset
- 09_Logistic_Regression.ipynb - Breast Cancer Dataset
- 10_Support_Vector_Machines.ipynb - Kernel Trick
- 11_K_Nearest_Neighbors.ipynb - Iris Dataset
- 12_Naive_Bayes.ipynb - Text Classification
- 13_Decision_Trees_and_Random_Forests.ipynb - Penguins Dataset
- 14_Gradient_Boosting_XGBoost.ipynb - Kaggle Champion
🔍 Phase 4: Unsupervised Learning (Modules 15-16)
- 15_KMeans_Clustering.ipynb - Elbow Method
- 16_Dimensionality_Reduction_PCA.ipynb - Digits Dataset
🧠 Phase 5: Advanced ML (Modules 17-20)
- 17_Neural_Networks_Deep_Learning.ipynb - MNIST with MLPClassifier
- 18_Time_Series_Analysis.ipynb - Air Passengers Dataset
- 19_Natural_Language_Processing_NLP.ipynb - Sentiment Analysis
- 20_Reinforcement_Learning_Basics.ipynb - Q-Learning Grid World
💼 Phase 6: Industry Skills (Modules 21-23)
- 21_Kaggle_Project_Medical_Costs.ipynb - Full Pipeline
- 22_SQL_for_Data_Science.ipynb - Database Integration
- 23_Model_Explainability_SHAP.ipynb - XAI with SHAP
🚀 Phase 7: Production & Deployment (Modules 24-26) ⭐ NEW!
- 24_Deep_Learning_TensorFlow.ipynb - TensorFlow/Keras & CNNs
- 25_Model_Deployment_Streamlit.ipynb - Web App Deployment
- 26_End_to_End_ML_Project.ipynb - Production Pipeline
🛠️ Setup Instructions
1. Install Dependencies
pip install -r requirements.txt
2. Launch Jupyter
jupyter notebook
3. Start Learning!
Open 01_Python_Core_Mastery.ipynb and work sequentially through Module 26.
🌐 Website Integration
This curriculum is designed to work seamlessly with the DataScience Learning Hub. Each ML module links to interactive visualizations and theory.
📊 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
📁 Key Files
- CURRICULUM_REVIEW.md - Quality assessment of all modules
- README_Resources.md - External learning resources
- requirements.txt - All dependencies
🎯 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
📈 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
🏆 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
🤝 Contributing
This curriculum is part of a personal learning journey integrated with aashishgarg13.github.io/DataScience/.
📝 License
For educational purposes. Feel free to learn and adapt!
Ready to become a Machine Learning Engineer? Start with 01_Python_Core_Mastery.ipynb! 🚀