A comprehensive journey through Python, Machine Learning, Deep Learning, and AI projects. This repository documents daily progress, notes, and hands-on code implementations over 100 consecutive days.
This is a structured learning challenge where I commit to coding for at least 1 hour every day for 100 days, focusing on building expertise in:
- Python Fundamentals & Advanced Concepts
- Data Science & Analysis
- Machine Learning (Supervised & Unsupervised Learning)
- Deep Learning (Neural Networks, CNNs, RNNs)
- Artificial Intelligence (NLP, Computer Vision, etc.)
- Real-world AI Projects
- Python basics and syntax
- Data structures and algorithms
- Working with libraries (NumPy, Pandas)
- Data visualization (Matplotlib, Seaborn)
- Supervised learning algorithms
- Unsupervised learning techniques
- Data preprocessing and feature engineering
- Model evaluation and validation
- Practical ML projects
- Neural network architecture
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Transfer learning
- Deep learning frameworks (TensorFlow, PyTorch)
- Natural Language Processing (NLP)
- Computer Vision applications
- Reinforcement Learning basics
- End-to-end AI projects
- Production-ready code
100-days-/
├── README.md # This file
├── day_1/ # Day 1 materials
│ ├── notes.md # Daily notes and concepts
│ └── code/ # Code implementations
├── day_2/
│ ├── notes.md
│ └── code/
├── ...
└── day_100/ # Final day
├── notes.md
└── code/
Each day folder contains:
- notes.md - Key concepts, learnings, and theory
- code/ - Python scripts and Jupyter notebooks
- resources.md (optional) - Links to articles, tutorials, and references
- Python 3.8+
- TensorFlow / Keras
- PyTorch
- Scikit-learn
- NumPy - Numerical computing
- Pandas - Data manipulation and analysis
- Matplotlib - Data visualization
- Seaborn - Statistical data visualization
- Plotly - Interactive visualizations
- Jupyter Notebook - Interactive coding and documentation
- Google Colab - Cloud-based development
- Git & GitHub - Version control
| Phase | Days | Status | Focus Area |
|---|---|---|---|
| Python Foundations | 1-20 | ✅ Complete | Basics & Libraries |
| ML Fundamentals | 21-50 | 🔄 In Progress | Core Algorithms |
| Deep Learning | 51-75 | ⏳ Upcoming | Neural Networks |
| Advanced AI & Projects | 76-100 | ⏳ Upcoming | Applications |
Current Progress: Day [X] / 100
- Python 3.8 or higher
- Jupyter Notebook or Google Colab
- Git for version control
-
Clone the repository:
git clone https://github.com/therai0/100-days-.git cd 100-days- -
Install required packages:
pip install numpy pandas matplotlib seaborn scikit-learn tensorflow jupyter
-
Start Jupyter Notebook:
jupyter notebook
-
Navigate to any day's folder and explore the code!
Each day follows this pattern:
- Morning (30 min) - Review previous concepts
- Main Learning (45 min) - New topic/project
- Practice (30+ min) - Hands-on coding
- Documentation (15 min) - Notes and reflections
- Day X-Y: Project Name - Brief Description
- Day X-Y: Project Name - Brief Description
- Day X-Y: Project Name - Brief Description
More projects will be added as the challenge progresses.
- Coursera - Machine Learning Specialization
- Fast.ai - Practical Deep Learning
- Google AI Learning Hub
- Kaggle Learn
- "Hands-On Machine Learning" by Aurélien Géron
- "Deep Learning" by Goodfellow, Bengio, and Courville
- "Python for Data Analysis" by Wes McKinney
- ✅ Day 10 - First ML Model
- ⏳ Day 30 - Complete ML Project
- ⏳ Day 50 - Deep Learning Fundamentals
- ⏳ Day 75 - Complex Neural Network
- ⏳ Day 100 - Capstone Project
This is a personal learning journey, but I welcome:
- Suggestions for improvement
- Pointing out errors or bugs
- Alternative approaches to problems
- Resource recommendations
Feel free to open an issue or pull request!
- GitHub: @therai0
- Email: [Your Email]
- LinkedIn: [Your LinkedIn]
This project is open source and available under the MIT License.
- Start from Day 1 - Follow the sequential progression
- Read daily notes - Each day has a comprehensive explanation of concepts
- Review code - Study the implementations for each topic
- Practice - Try to implement concepts on your own
- Experiment - Modify code and test your understanding
If you find this repository helpful, please:
- ⭐ Star this repository
- 🍴 Fork it and use it as a template
- 📢 Share it with others learning Python and AI
- 💬 Leave feedback and suggestions
Last Updated: October 18, 2025
Status: 🔄 Ongoing Challenge
Goal: Master Python, ML, DL, and AI in 100 Days! 🎯
Happy Learning! 🚀