https://github.com/yash27007/python-bootcamp
This repository contains Python code covering everything from basics to advanced AI, ML, and Data Science concepts. All topics are organized in separate folders for easy navigation.
https://github.com/yash27007/python-bootcamp
ai beginner-friendly data-science machine-learning python
Last synced: about 1 month ago
JSON representation
This repository contains Python code covering everything from basics to advanced AI, ML, and Data Science concepts. All topics are organized in separate folders for easy navigation.
- Host: GitHub
- URL: https://github.com/yash27007/python-bootcamp
- Owner: yash27007
- License: mit
- Created: 2025-09-03T15:13:21.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2026-05-12T08:00:48.000Z (about 2 months ago)
- Last Synced: 2026-05-12T10:05:32.487Z (about 2 months ago)
- Topics: ai, beginner-friendly, data-science, machine-learning, python
- Language: Jupyter Notebook
- Homepage:
- Size: 5.14 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Zero to Hero: Python, Data Science, ML & AI
A structured, hands-on course repository covering everything from Python basics to MLOps and LLM applications — following the [Complete Machine Learning, NLP Bootcamp (MLOps & Deployment)](https://www.udemy.com/course/complete-machine-learning-nlp-bootcamp-mlops-deployment/) curriculum, extended with GenAI and LLM application projects.
> A one-stop learning resource for ML, DS, and AI. If you find this helpful, please leave a ⭐
---
## Course Roadmap
```
01-python-foundation → 02-statistics → 03-data-analysis
↓
04-feature-engineering → 05-machine-learning → 06-deep-learning
↓
07-nlp → 08-mlops-deployment → projects (beginner → advanced → ai-llm)
```
---
## Curriculum
| Section | Topic | Status | Contents |
|---------|-------|--------|----------|
| [01](./01-python-foundation/) | **Python Foundation** | ✅ Complete | Basics, Control Flow, Data Structures, Functions, Modules, File I/O, OOP, Advanced, Logging, Threading, Memory, Flask, Streamlit |
| [02](./02-statistics/) | **Statistics** | ✅ Complete | Descriptive Stats, Probability, Inferential Statistics |
| [03](./03-data-analysis/) | **Data Analysis** | ✅ Complete | NumPy, Pandas, Data Manipulation, Reading Data, Matplotlib, Seaborn, SQLite, EDA Projects |
| [04](./04-feature-engineering/) | **Feature Engineering** | ✅ Complete | Missing Values, Outliers, Encoding, Imbalanced Data |
| [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 |
| [06](./06-deep-learning/) | **Deep Learning** | 🚧 Coming soon | ANN, CNN, RNN, LSTM/GRU, Attention & Transformers |
| [07](./07-nlp/) | **NLP** | 🚧 Coming soon | Text Preprocessing, BOW/TF-IDF, Word2Vec, Deep Learning NLP |
| [08](./08-mlops-deployment/) | **MLOps & Deployment** | 🚧 Coming soon | Docker, Git, End-to-End Projects, MLflow/DVC, BentoML |
| [projects](./projects/) | **Projects** | 🔄 Growing | Beginner: Titanic EDA, Iris Classifier, House Prices, Student Performance (all with notebooks) |
---
## Quick Start
### Prerequisites
- Python 3.12+
- [uv](https://docs.astral.sh/uv/getting-started/installation/) (fast Python package manager)
### Setup (one-time)
```bash
git clone https://github.com/yash27007/python-bootcamp.git
cd python-bootcamp
# Install all dependencies and create the virtual environment
uv sync
# Activate the virtual environment
source .venv/bin/activate # Linux / macOS
# .venv\Scripts\activate # Windows
```
### Running Notebooks
```bash
# After activating .venv
jupyter lab
```
Navigate to any section folder and open a `.ipynb` file.
### Running Scripts
```bash
# Example: multi-threading demo
python 01-python-foundation/04-multi-threading/multi-threading.py
# Example: Flask API
python 01-python-foundation/05-flask/app.py
# Example: Streamlit app
streamlit run 01-python-foundation/06-streamlit/main.py
```
---
## Section Structure
Each section follows a consistent layout:
```
XX-section-name/
├── README.md ← what's covered and prerequisites
├── notes.md ← theory notes with formulas and diagrams
└── topic.ipynb ← practical code with comments
```
For practical-only topics (scripts, web apps) there may be `.py` files instead of notebooks.
---
## What's Inside Each Section
### 01 – Python Foundation
13 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.
### 02 – Statistics
The mathematical bedrock of ML:
- **Descriptive Statistics** – central tendency, dispersion, correlation
- **Probability** – rules, distributions, Bayes' theorem, CLT
- **Inferential Statistics** – CIs, hypothesis testing, t-tests, ANOVA, chi-square
### 03 – Data Analysis *(coming soon)*
NumPy, Pandas, data manipulation, reading from multiple sources, Matplotlib, Seaborn, SQLite, and three real-world EDA projects (Red Wine, Flight Price, Google Play Store).
### 04 – Feature Engineering
Preparing raw data for ML models:
- Handling missing values (mean/median/KNN/MICE imputation)
- Detecting and treating outliers (IQR, Z-score, Winsorization)
- Encoding categorical features (label, one-hot, ordinal, target)
- Dealing with class imbalance (SMOTE, class weights)
### 05 – Machine Learning
18 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.
### 06 – Deep Learning *(coming soon)*
ANN 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).
### 07 – NLP *(coming soon)*
Text 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.
### 08 – MLOps & Deployment *(coming soon)*
Docker, Git/GitHub, two full-scale projects (Student Performance + Sensor Fault Detection with MongoDB/MLflow/DVC/GitHub Actions), and BentoML for serving models as APIs.
---
## Dependencies
All dependencies are managed via [uv](https://docs.astral.sh/uv/). The `pyproject.toml` at the root includes everything needed:
- **Core:** numpy, pandas, scipy, statsmodels
- **Visualisation:** matplotlib, seaborn
- **ML:** scikit-learn, imbalanced-learn
- **Notebooks:** jupyter, ipykernel
- **Web:** flask, streamlit
- **Utilities:** requests, beautifulsoup4
A single `uv sync` installs them all.
---
## Projects
End-to-end projects organised by difficulty — from beginner EDA to production MLOps and LLM applications.
| Tier | Projects |
|------|---------|
| [Beginner](./projects/beginner/) | Titanic EDA, Iris Classifier, House Price Prediction, Student Performance |
| [Intermediate](./projects/intermediate/) | Customer Churn, Fraud Detection, Time Series, Sentiment Analysis |
| [Advanced](./projects/advanced/) | Image CNN, Transformer from Scratch, MLOps Pipeline, Sensor Fault Detection |
| [AI / LLM](./projects/ai-llm/) | Document Q&A (RAG), Chatbot with Memory, Multi-doc Summariser, LLM Fine-tuning |
See [projects/README.md](./projects/README.md) for full details and how to add your own project.
---
## Resources
Curated free books, blogs, courses, cheatsheets, and YouTube channels to go deeper:
→ **[RESOURCES.md](./RESOURCES.md)**
Highlights:
- [ISLR](https://www.statlearning.com) — free ML textbook PDF
- [fast.ai](https://course.fast.ai) — best practical DL course
- [Karpathy's Zero to Hero](https://karpathy.ai/zero-to-hero.html) — build GPT from scratch
- [Jay Alammar's Blog](https://jalammar.github.io) — best visual transformer explainers
- [HuggingFace Learn](https://huggingface.co/learn) — NLP, LLM, Agents courses
---
## Contributing
Contributions are welcome! Whether it's fixing a typo, adding examples, filling in a "coming soon" section, or adding a project:
→ **[CONTRIBUTING.md](./CONTRIBUTING.md)**
Quick steps:
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/add-numpy-section`)
3. Commit your changes (follow the notebook conventions in CONTRIBUTING.md)
4. Open a pull request using the PR template
GitHub issue templates are available for [bug reports](./.github/ISSUE_TEMPLATE/bug-report.md) and [content requests](./.github/ISSUE_TEMPLATE/content-request.md).
---
## License
[MIT License](./LICENSE)