https://github.com/ahmadjajja/ai_n_datascience
AI n Data Science
https://github.com/ahmadjajja/ai_n_datascience
Last synced: 28 days ago
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AI n Data Science
- Host: GitHub
- URL: https://github.com/ahmadjajja/ai_n_datascience
- Owner: Ahmadjajja
- Created: 2024-10-01T08:11:21.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-12T16:13:43.000Z (10 months ago)
- Last Synced: 2025-04-05T19:34:07.252Z (6 months ago)
- Language: Python
- Homepage:
- Size: 20.1 MB
- Stars: 8
- Watchers: 1
- Forks: 9
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# AI & Data Science Course
## Clear steps to follow
- **CGPA:** To show you are a genuine student.
- **Typing Speed:** 100+ wpm for writing quickly.
- **LeetCode:** 200+ problems for strong coding and problem-solving skills.
- **Regular LinkedIn Writing:** At least 2 posts a week to learn in public, improve writing skills and make personal brand.
- **Learn AI & Data Science Skills:** To get started in the tech field.
- **Participate in International Hackathons:** For hands-on real-world experience, improving English communication skills, and working under tight deadlines.
- **Teaching (Optional):** For building confidence, delivering your thought process to an audience, leadership, and better communication.
## Course Syllabus
### 1. Git & GitHub
- Version control basics
- Collaborating on projects using GitHub
### 2. Python Programming
- Core Python syntax and concepts
- Object Oriented Programming (OOP)
- Writing clean and efficient code
### 3. FastAPI / Flask Server & Database (Postgres)
- Building RESTful APIs with FastAPI or Flask
- Database design and management using PostgreSQL
### 4. Langchain LLM (GPTs, Gemini)
- Understanding Large Language Models (LLMs)
- Implementing GPTs and other LLMs for various applications
### 5. Machine Learning & Deep Learning Theory
- Supervised and unsupervised learning
- Neural networks and deep learning fundamentals
### 6. Libraries and Tools:
- **Scikit-learn:** Machine learning library for classical models
- **NumPy & Pandas:** Data manipulation and analysis
- **Matplotlib:** Data visualization
- **TensorFlow (Keras) & PyTorch:** Deep learning frameworks
### 7. Docker
- Containerization concepts
- Deploying applications using Docker
### 8. Cloud Platforms (AWS, Google Cloud, Azure)
- Introduction to cloud computing
- Deploying and managing applications on cloud platforms
## How to Contribute
1. **Fork this repository:** Click the "Fork" button at the top-right of this page to create your own copy of this repo.
2. **Clone the forked repository:** On your machine, use `git clone ` to clone your copy.
3. **Create a new branch:** Use `git checkout -b ` to create and switch to a new branch.
4. **Make changes:** Add your contributions or improvements.
5. **Commit and push your changes:** Use `git add .`, `git commit -m "Your message"`, and `git push origin `.
6. **Create a pull request:** Go to your forked repository on GitHub and click on "New pull request."
Feel free to explore, learn, and contribute!