DataScience

🎓 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)

  1. 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
    • Central Tendency, Dispersion, Z-Scores
    • Correlation, Hypothesis Testing (p-values)
    • Links: Statistics Course

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

  1. 03_NumPy_Practice.ipynb - Numerical Computing
  2. 04_Pandas_Practice.ipynb - Data Manipulation
  3. 05_Matplotlib_Seaborn_Practice.ipynb - Visualization
  4. 06_EDA_and_Feature_Engineering.ipynb - Real Titanic Dataset
  5. 07_Scikit_Learn_Practice.ipynb - Pipelines & GridSearch

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

  1. 08_Linear_Regression.ipynb - Diamonds Dataset
  2. 09_Logistic_Regression.ipynb - Breast Cancer Dataset
  3. 10_Support_Vector_Machines.ipynb - Kernel Trick
  4. 11_K_Nearest_Neighbors.ipynb - Iris Dataset
  5. 12_Naive_Bayes.ipynb - Text Classification
  6. 13_Decision_Trees_and_Random_Forests.ipynb - Penguins Dataset
  7. 14_Gradient_Boosting_XGBoost.ipynb - Kaggle Champion

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

  1. 15_KMeans_Clustering.ipynb - Elbow Method
  2. 16_Dimensionality_Reduction_PCA.ipynb - Digits Dataset

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

  1. 17_Neural_Networks_Deep_Learning.ipynb - MNIST with MLPClassifier
  2. 18_Time_Series_Analysis.ipynb - Air Passengers Dataset
  3. 19_Natural_Language_Processing_NLP.ipynb - Sentiment Analysis
  4. 20_Reinforcement_Learning_Basics.ipynb - Q-Learning Grid World

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

  1. 21_Kaggle_Project_Medical_Costs.ipynb - Full Pipeline
  2. 22_SQL_for_Data_Science.ipynb - Database Integration
  3. 23_Model_Explainability_SHAP.ipynb - XAI with SHAP

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

  1. 24_Deep_Learning_TensorFlow.ipynb - TensorFlow/Keras & CNNs
  2. 25_Model_Deployment_Streamlit.ipynb - Web App Deployment
  3. 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

🎯 Who Is This For?

📈 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:

🤝 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! 🚀