A structured collection of hands-on notebooks, practical implementations, mini projects, and learning resources covering Python, Data Science, Machine Learning, Deep Learning, and Artificial Intelligence.
This repository documents my structured learning journey in Data Science, Machine Learning, Deep Learning, and Artificial Intelligence.
It contains well-organized Jupyter notebooks, practical implementations, mini projects, datasets, presentations, and learning notes covering the complete Data Science workflow—from Python programming and data analysis to Deep Learning and Web Development for Data Scientists.
Beyond the practical implementations available in this repository, I also explored Large Language Models (LLMs), AI-assisted development workflows, Git for Data Scientists, and Google Colab as part of my learning journey.
The objective of this repository is to build a strong foundation in Data Science while maintaining a clean, well-documented portfolio of my learning and practical work.
- ✅ Python Programming
- ✅ Data Analysis & Visualization
- ✅ Machine Learning
- ✅ Deep Learning & Neural Networks
- ✅ Web Development for Data Scientists
- 📚 Explored Large Language Models (LLMs) & AI Workflows
- 🚧 Building a RAG AI Teaching Assistant
- 🐍 Python Basics
- 🔢 NumPy
- 🐼 Pandas
- 📊 Data Visualization
- 🌐 Data Collection
- 📈 Probability & Statistics
- 🤖 Machine Learning
- ⚙️ Scikit-Learn
- 🧠 Deep Learning & Neural Networks
- 🌐 Web Development for Data Scientists
In addition to the practical implementations available in this repository, I also explored:
- 🤖 Large Language Models (LLMs)
- 🚀 Leveraging AI as a Data Scientist
- 🛠️ Git for Data Scientists
- ☁️ Google Colab
These topics focused on modern AI workflows, development tools, and industry best practices, complementing the practical implementations in this repository.
data-science-journey/
│
├── 📁 01-Python Basics # Python fundamentals
├── 📁 02-NumPy # Numerical computing
├── 📁 03-Pandas # Data manipulation & analysis
├── 📁 04-Data-Visualization # Matplotlib & Seaborn
├── 📁 05-Data-Collection # Requests & BeautifulSoup
├── 📁 06-Probability Distributions # Statistics fundamentals
├── 📁 07-Machine-Learning # Machine Learning fundamentals
├── 📁 08-Scikit-Learn # Practical ML implementations
├── 📁 09-Deep Learning & Neural Networks # TensorFlow & Neural Networks
├── 📁 10-Web Development for Data Scientists # Flask fundamentals
├── 📁 Projects # Mini Projects
└── 📄 README.md # Repository overview
| Project | Description |
|---|---|
| 🏠 House Price Prediction | End-to-end regression project using Scikit-Learn covering preprocessing, feature engineering, pipelines, model training, and prediction. |
| 🌸 Iris Flower Classification | Classification project demonstrating data preprocessing, model training, prediction, and accuracy evaluation. |
| 📱 Smartphone Prediction | Machine Learning classification project built using Scikit-Learn. |
| 🔢 MNIST Handwritten Digit Classification | Deep Learning project using TensorFlow and Keras for handwritten digit recognition. |
Every module in this repository follows a structured learning approach:
- 📖 Concept Notes
- 💻 Hands-on Jupyter Notebooks
- 📊 Practical Examples
- 📁 Real-world Datasets
- 🚀 Mini Projects
- 📝 Dedicated README Documentation
- Python
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Scikit-Learn
- TensorFlow
- Keras
- Flask
- Jinja2
- Jupyter Notebook
- VS Code
- Anaconda
- Google Colab
- Git
- GitHub
- Data Analysis
- Data Visualization
- Data Preprocessing
- Machine Learning
- Deep Learning
- Neural Networks
- Model Evaluation
Naitik Gupta
- 💻 GitHub: https://github.com/naitik2043
- 💼 LinkedIn: https://www.linkedin.com/in/naitik2043/
Thank you for visiting my repository!
This repository reflects my continuous journey of learning, practicing, and building projects in Data Science, Machine Learning, Deep Learning, and Artificial Intelligence.
I will continue expanding it with new projects, better implementations, and modern AI applications as I progress.
If you found this repository helpful, consider giving it a ⭐ Star. Feedback, suggestions, and contributions are always welcome!