{"id":50660964,"url":"https://github.com/yash27007/python-bootcamp","last_synced_at":"2026-06-08T02:31:05.572Z","repository":{"id":313051207,"uuid":"1049840511","full_name":"yash27007/python-bootcamp","owner":"yash27007","description":"This repository contains Python code covering everything from basics to advanced AI, ML, and Data Science concepts. 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If you find this helpful, please leave a ⭐\n\n---\n\n## Course Roadmap\n\n```\n01-python-foundation  →  02-statistics  →  03-data-analysis\n       ↓\n04-feature-engineering  →  05-machine-learning  →  06-deep-learning\n       ↓\n07-nlp  →  08-mlops-deployment  →  projects (beginner → advanced → ai-llm)\n```\n\n---\n\n## Curriculum\n\n| Section | Topic | Status | Contents |\n|---------|-------|--------|----------|\n| [01](./01-python-foundation/) | **Python Foundation** | ✅ Complete | Basics, Control Flow, Data Structures, Functions, Modules, File I/O, OOP, Advanced, Logging, Threading, Memory, Flask, Streamlit |\n| [02](./02-statistics/) | **Statistics** | ✅ Complete | Descriptive Stats, Probability, Inferential Statistics |\n| [03](./03-data-analysis/) | **Data Analysis** | ✅ Complete | NumPy, Pandas, Data Manipulation, Reading Data, Matplotlib, Seaborn, SQLite, EDA Projects |\n| [04](./04-feature-engineering/) | **Feature Engineering** | ✅ Complete | Missing Values, Outliers, Encoding, Imbalanced Data |\n| [05](./05-machine-learning/) | **Machine Learning** | ✅ Complete | Linear→Polynomial Regression, Regularization, Logistic, SVM, Naive Bayes, KNN, Trees, Ensembles, Boosting, XGBoost, PCA, K-Means, Hierarchical, DBSCAN, Isolation Forest, LOF |\n| [06](./06-deep-learning/) | **Deep Learning** | 🚧 Coming soon | ANN, CNN, RNN, LSTM/GRU, Attention \u0026 Transformers |\n| [07](./07-nlp/) | **NLP** | 🚧 Coming soon | Text Preprocessing, BOW/TF-IDF, Word2Vec, Deep Learning NLP |\n| [08](./08-mlops-deployment/) | **MLOps \u0026 Deployment** | 🚧 Coming soon | Docker, Git, End-to-End Projects, MLflow/DVC, BentoML |\n| [projects](./projects/) | **Projects** | 🔄 Growing | Beginner: Titanic EDA, Iris Classifier, House Prices, Student Performance (all with notebooks) |\n\n---\n\n## Quick Start\n\n### Prerequisites\n\n- Python 3.12+\n- [uv](https://docs.astral.sh/uv/getting-started/installation/) (fast Python package manager)\n\n### Setup (one-time)\n\n```bash\ngit clone https://github.com/yash27007/python-bootcamp.git\ncd python-bootcamp\n\n# Install all dependencies and create the virtual environment\nuv sync\n\n# Activate the virtual environment\nsource .venv/bin/activate        # Linux / macOS\n# .venv\\Scripts\\activate         # Windows\n```\n\n### Running Notebooks\n\n```bash\n# After activating .venv\njupyter lab\n```\n\nNavigate to any section folder and open a `.ipynb` file.\n\n### Running Scripts\n\n```bash\n# Example: multi-threading demo\npython 01-python-foundation/04-multi-threading/multi-threading.py\n\n# Example: Flask API\npython 01-python-foundation/05-flask/app.py\n\n# Example: Streamlit app\nstreamlit run 01-python-foundation/06-streamlit/main.py\n```\n\n---\n\n## Section Structure\n\nEach section follows a consistent layout:\n\n```\nXX-section-name/\n├── README.md               ← what's covered and prerequisites\n├── notes.md                ← theory notes with formulas and diagrams\n└── topic.ipynb             ← practical code with comments\n```\n\nFor practical-only topics (scripts, web apps) there may be `.py` files instead of notebooks.\n\n---\n\n## What's Inside Each Section\n\n### 01 – Python Foundation\n13 sub-topics from basic syntax to web frameworks: variables, control flow, data structures, functions, modules, file I/O, OOP, iterators/generators, logging, concurrency, memory management, Flask, Streamlit.\n\n### 02 – Statistics\nThe mathematical bedrock of ML:\n- **Descriptive Statistics** – central tendency, dispersion, correlation\n- **Probability** – rules, distributions, Bayes' theorem, CLT\n- **Inferential Statistics** – CIs, hypothesis testing, t-tests, ANOVA, chi-square\n\n### 03 – Data Analysis *(coming soon)*\nNumPy, Pandas, data manipulation, reading from multiple sources, Matplotlib, Seaborn, SQLite, and three real-world EDA projects (Red Wine, Flight Price, Google Play Store).\n\n### 04 – Feature Engineering\nPreparing raw data for ML models:\n- Handling missing values (mean/median/KNN/MICE imputation)\n- Detecting and treating outliers (IQR, Z-score, Winsorization)\n- Encoding categorical features (label, one-hot, ordinal, target)\n- Dealing with class imbalance (SMOTE, class weights)\n\n### 05 – Machine Learning\n18 sub-topics covering every major algorithm: linear through polynomial regression, regularisation, logistic regression, SVM, Naïve Bayes, KNN, decision trees, random forest, AdaBoost, gradient boosting, XGBoost, PCA (with eigen decomposition), K-Means (K-Means++, elbow method), hierarchical clustering (Ward linkage, dendrograms), DBSCAN, silhouette analysis, isolation forest, local outlier factor, and DBSCAN-based anomaly detection.\n\n### 06 – Deep Learning *(coming soon)*\nANN from scratch (activations, optimisers, dropout), CNN for images, RNN for sequences, LSTM/GRU for long-range dependencies, and the full Transformer architecture (self-attention through decoder).\n\n### 07 – NLP *(coming soon)*\nText preprocessing with NLTK, classical feature extraction (BOW, N-Grams, TF-IDF), dense word embeddings (Word2Vec, AvgWord2Vec), deep learning-based NLP, and four end-to-end projects.\n\n### 08 – MLOps \u0026 Deployment *(coming soon)*\nDocker, Git/GitHub, two full-scale projects (Student Performance + Sensor Fault Detection with MongoDB/MLflow/DVC/GitHub Actions), and BentoML for serving models as APIs.\n\n---\n\n## Dependencies\n\nAll dependencies are managed via [uv](https://docs.astral.sh/uv/). The `pyproject.toml` at the root includes everything needed:\n\n- **Core:** numpy, pandas, scipy, statsmodels\n- **Visualisation:** matplotlib, seaborn\n- **ML:** scikit-learn, imbalanced-learn\n- **Notebooks:** jupyter, ipykernel\n- **Web:** flask, streamlit\n- **Utilities:** requests, beautifulsoup4\n\nA single `uv sync` installs them all.\n\n---\n\n## Projects\n\nEnd-to-end projects organised by difficulty — from beginner EDA to production MLOps and LLM applications.\n\n| Tier | Projects |\n|------|---------|\n| [Beginner](./projects/beginner/) | Titanic EDA, Iris Classifier, House Price Prediction, Student Performance |\n| [Intermediate](./projects/intermediate/) | Customer Churn, Fraud Detection, Time Series, Sentiment Analysis |\n| [Advanced](./projects/advanced/) | Image CNN, Transformer from Scratch, MLOps Pipeline, Sensor Fault Detection |\n| [AI / LLM](./projects/ai-llm/) | Document Q\u0026A (RAG), Chatbot with Memory, Multi-doc Summariser, LLM Fine-tuning |\n\nSee [projects/README.md](./projects/README.md) for full details and how to add your own project.\n\n---\n\n## Resources\n\nCurated free books, blogs, courses, cheatsheets, and YouTube channels to go deeper:\n\n→ **[RESOURCES.md](./RESOURCES.md)**\n\nHighlights:\n- [ISLR](https://www.statlearning.com) — free ML textbook PDF\n- [fast.ai](https://course.fast.ai) — best practical DL course\n- [Karpathy's Zero to Hero](https://karpathy.ai/zero-to-hero.html) — build GPT from scratch\n- [Jay Alammar's Blog](https://jalammar.github.io) — best visual transformer explainers\n- [HuggingFace Learn](https://huggingface.co/learn) — NLP, LLM, Agents courses\n\n---\n\n## Contributing\n\nContributions are welcome! Whether it's fixing a typo, adding examples, filling in a \"coming soon\" section, or adding a project:\n\n→ **[CONTRIBUTING.md](./CONTRIBUTING.md)**\n\nQuick steps:\n1. Fork the repository\n2. Create a feature branch (`git checkout -b feature/add-numpy-section`)\n3. Commit your changes (follow the notebook conventions in CONTRIBUTING.md)\n4. Open a pull request using the PR template\n\nGitHub issue templates are available for [bug reports](./.github/ISSUE_TEMPLATE/bug-report.md) and [content requests](./.github/ISSUE_TEMPLATE/content-request.md).\n\n---\n\n## License\n\n[MIT License](./LICENSE)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyash27007%2Fpython-bootcamp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fyash27007%2Fpython-bootcamp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyash27007%2Fpython-bootcamp/lists"}