Welcome to the ultimate Machine Learning blueprint repository! This project serves as a comprehensive codebase, template center, and notebook journal built while mastering "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron.
The core objective is to create ready-to-use boilerplate templates for real-world data science problems so we never have to reinvent the wheel for data pipelines and algorithms.
- 📁 Chapter 1: The Machine Learning Landscape
- Concepts: Supervised vs Unsupervised, Batch vs Online, Overfitting/Underfitting.
- Blueprints: Simple linear model to predict life satisfaction based on GDP.
- 📁 Chapter 2: End-to-End Machine Learning Project
- Concepts: Data cleaning, handling categorical text, custom pipelines, feature scaling.
- Blueprints:
Pipeline,ColumnTransformer,SimpleImputer,OneHotEncoder, Baseline models.
- 📁 Chapter 3: Classification
- Concepts: Binary vs Multiclass, Confusion Matrix, Precision/Recall Tradeoff, ROC/AUC curves.
- Blueprints:
SGDClassifier, evaluation metric visualizations.
- 📁 Chapter 4: Training Models
- Concepts: Linear Regression mechanics, Gradient Descent (Batch, Stochastic, Mini-batch), Polynomial Regression, Regularization (Ridge, Lasso, Elastic Net).
- 📁 Chapter 5: Support Vector Machines
- Concepts: Linear & Non-linear SVM Classification, SVM Regression, Under-the-hood mechanics.
- 📁 Chapter 6: Decision Trees
- Concepts: Training and visualising Decision Trees, CART algorithm, Gini Impurity vs Entropy.
- 📁 Chapter 7: Ensemble Learning & Random Forests
- Concepts: Voting Classifiers, Bagging/Pasting, Boosting (AdaBoost, Gradient Boosting), Stacking.
- 📁 Chapter 9: Unsupervised Learning Techniques
- Concepts: Clustering (K-Means, DBSCAN), Anomaly Detection, Mixture Models.
This repository is open-source and actively looking for contributions! Whether you want to fix a typo, add a new algorithm implementation, or optimize an existing pipeline, you are welcome here.
- Check out our Open Issues tab. Look for tasks labeled
good first issue. - Fork the repository.
- Create your feature branch (
git checkout -b feature/AmazingFeature). - Commit your changes and Push to the branch.
- Open a Pull Request (PR) and describe your changes!
Instead of memorizing library syntax, use this repo as your plug-and-play reference manual:
- Open the notebook corresponding to your ML problem.
- Copy the pre-tested data pipeline structure.
- Plug in your own custom dataset and start hyperparameter tuning!
- Developed by: Vinay Kumar Mandal
- Tech Stack: Python, Jupyter Notebooks, Scikit-Learn, Pandas, NumPy.
If you find these templates helpful for your projects, don't forget to Star ⭐ this repository!