Learn Generative AI from scratch with hands-on Python code, Large Language Models (LLMs), prompt engineering, fine-tuning, Retrieval-Augmented Generation (RAG), AI agents, multimodal models, quizzes, and real-world projects — all organized in one complete GitHub repository.
Welcome to a complete Generative AI learning hub designed for beginners, developers, and researchers.
This repository covers the full Generative AI ecosystem, including:
- Large Language Models (LLMs)
- Transformer Architecture
- Prompt Engineering & System Prompts
- Embeddings & Vector Databases
- Retrieval-Augmented Generation (RAG)
- Fine-Tuning (LoRA, QLoRA, PEFT)
- AI Agents & Tool Calling
- Multimodal AI (Text, Image, Audio)
- OpenAI, Gemini, Claude, LLaMA, Mistral
- LangChain, LlamaIndex, Hugging Face
- Real-world Generative AI applications
Whether you're building ChatGPT-like apps, document chatbots, AI copilots, or autonomous agents, this repo is your A-to-Z Generative AI guide.
Keywords:
generative ai course, llm with python, prompt engineering, rag pipeline, ai agents, generative ai github repository, free generative ai resources
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🧠 End-to-End Learning
From GenAI basics to advanced agentic and multimodal systems. -
🛠 Hands-On & Practical
Python scripts, Jupyter/Colab notebooks, and real-world projects. -
🌍 Community-Driven
Open-source learning with global contributors. -
🤖 Modern AI Stack
OpenAI APIs, Hugging Face, LangChain, vector databases, and open-source LLMs.
- Introduction to Generative AI
- Why Learn Generative AI
- How to Get Involved
- Course Roadmap
- Generative AI Resources
🚀 Fork & Star the Repo
Show support and stay updated.
👩💻 Follow Structured Lessons
Beginner-to-advanced GenAI tutorials.
🛠️ Contribute Code & Content
Add notebooks, improve docs, or write new tutorials.
🧪 Experiment & Build
Create chatbots, RAG systems, and AI agents.
🤝 Collaborate
Join discussions, review PRs, and share ideas.
📌 Share Knowledge
Submit blogs, videos, tools, or research papers.
- Add Generative AI tutorials (LLMs, RAG, Agents)
- Convert lessons into Colab notebooks
- Add OpenAI / Gemini / Claude examples
- Create real-world GenAI projects
- Add quizzes & interview questions
- Improve documentation & blogs
- Translate content into other languages
- Create videos from blogs
- What is Generative AI?
- History & Evolution of LLMs
- Discriminative vs Generative Models
- Attention Mechanism
- Tokenization
- Training vs Inference
- Zero-shot & Few-shot Prompting
- System Prompts
- Prompt Optimization
- Text Embeddings
- Similarity Search
- FAISS / Chroma / Pinecone
- RAG Architecture
- Document Chatbots
- Production Best Practices
- LoRA & QLoRA
- Instruction Tuning
- Evaluation
- Function Calling
- Autonomous Agents
- Multi-Agent Systems
- Text-to-Image
- Image-to-Text
- Audio & Video Models
- APIs
- Cost Optimization
- Monitoring & Safety
- ChatGPT-like Assistant
- PDF Chatbot with RAG
- AI Research Agent
- Multimodal AI App
You can support this project by becoming a sponsor on GitHub Sponsors or via bank transfer — please contact me at 📧 mushtaqmsit@gmail.com.
🔗 Enbroll in Complate Computer Vision Course
If link is not working then you need to create account in couresteach.com then you click on course
📬 Need Help? Connect with us on WhatsApp
Goal: Understand core generative AI concepts
| Topic Name/Tutorial | Video | Code | Note | Difficulty |
|---|---|---|---|---|
| 1-Generative AI as a Subset of Deep Learning | 1-2 |  |
blog | Beginer |
| 2-Is It Generative AI or Not | 1-2 | |
Note | Beginer |
| 3-Defining Generative AI A Formal Approach | 1-2-3 | |
Note | Beginer |
| 4-Transformers, Hallucinations, and Prompt Design | 1-2-3 | |
Note | Beginer |
| 5-The evolution of Generative AI | 1-2 | |
Note | Beginer |
| 6-An Overview of Generative AI Model Types | 1-2 | |
Note | Beginer |
| 7-Generative AI for Code Generation | 1-2 | |
Note | Beginer |
| Topic Name/Tutorial | Video | Code | Note | Extra Resoruces |
|---|---|---|---|---|
| 1-What is Large Language model | 1-2 | |
blog | 1 |
| Topic Name/Tutorial | Video | Code | Note | Difficulty |
|---|---|---|---|---|
| 1-Introduction to Vertex AI Studio | 1-2 | |
blog | Beginer |
| Topic Name/Tutorial | Video | Code | Note | Difficulty |
|---|---|---|---|---|
| 1-Introduction to Responsible AI | 1-2 | |
blog | Beginer |
| Title/link | Description | Reading Status |
|---|---|---|
| ✅1- MIT 6.S087: Foundation Models & Generative AI (2024) | by Michigan Online,Youtube | Pending |
| ✅2- Generative AI for Beginners by Microsoft | Introduction to Generative AI and LLMs (Part 1 of 18) | Generative AI for Beginners |
| ✅3- Generative AI for Beginners by Microsoft | Introduction to Generative AI and LLMs (Part 1 of 18) | Generative AI for Beginners |
| ✅3- Beginner: Introduction to Generative AI by Google | Introduction to Generative AI and LLMs (Part 1 of 18) | Generative AI for Beginners |
| ✅4- Generative AI by Linkden | Introduction to Generative AI and LLMs (Part 1 of 18) | Generative AI for Beginners |
| ✅4- Generative AI with Large Language Models by cousera | Introduction to Generative AI and LLMs (Part 1 of 18) | Generative AI for Beginners |
| ✅4-Generative Adversarial Networks (GANs) Specialization by Coursera | Introduction to Generative AI and LLMs (Part 1 of 18) | Generative AI for Beginners |
| ✅5-Understanding Deep Learning | Introduction to Generative AI and LLMs (Part 1 of 18) | Generative AI for Beginners |
| ✅6-Free and paid generative AI courses to learn new AI skills | Introduction to Generative AI and LLMs (Part 1 of 18) | Generative AI for Beginners |
| ✅7-Generative AI: Foundation Models and Platforms | Introduction to Generative AI and LLMs (Part 1 of 18) | Generative AI for Beginners |
| ✅8-Introduction to Generative AI Learning Path Specialization | Introduction to Generative AI and LLMs (Part 1 of 18) | Generative AI for Beginners |
| ✅5-Coursera Generative AI Courses You Must Know in 2026 | Road Map on Coggle | -- |
| 🌟9-Generative AI with Large Language Models | Introduction to Generative AI and LLMs (Part 1 of 18) | Generative AI for Beginners |
| ✅10-Building LLMs from scratch | Introduction to Generative AI and LLMs (Part 1 of 18) | Generative AI for Beginners |
| ✅11-Best Resources to Learn LLMs | Here are four best resources to help you learn LLMs, from fundamental NLP concepts to building your own LLMs from scratch | Generative AI for Beginners |
| 🌟12-Generative AI Engineering with LLMs Specialization | Introduction to Generative AI and LLMs (Part 1 of 18) | Generative AI for Beginners |
| ✅13-Top Free Courses on Large Language Models | Introduction to Generative AI and LLMs (Part 1 of 18) | Generative AI for Beginners |
| Title/link | Description | Code |
|---|---|---|
| ✅1- Road Map | Road Map on Coggle | --- |
| ✅2-visionbrick) | Road Map on Coggle | --- |
| ✅3-computer Vision Study Plan) | Road Map on Coggle | --- |
| ✅4-LearnHub) | Road Map on Coggle | --- |
| ✅5-Master Machine Learning & Generative AI) | Road Map on Coggle | --- |
| ✅6-Foundations of Large Language Models and the Transformer Revolution BY AI tutor) | Road Map on Coggle | --- |
| Title/link | Description | Code |
|---|---|---|
| ✅1- Generative AI Roadmap | Road Map on Coggle | --- |
| Title/link | Description | Code |
|---|---|---|
| ✅1- Jeff Heaton | It is Videos and github | --- |
| ✅2- First Principles of Computer Vision | It is Videos and github | --- |
| ✅3-Yannic Kilcher | It is Videos and github | --- |
| ✅4-AI-ML-Roadmap-from-scratch | It is Videos and github | --- |
| ✅5-Free AI Agents Resources | It is Videos and github | --- |
| ✅6-LLM Roadmap for 2026 | It is Videos and github | --- |
| ✅7-awesome-generative-ai-guide | It is Videos and github | --- |
| Title/link | Description | Code |
|---|---|---|
| ✅1-Generative AI Projects – Learn by Building | It is Videos and github | 1 |
| ✅2-Building a Multi-Tool AI Agent | It is Videos and github | 1 |
| ✅3-Build an AI Code Review Bot for GitHub | It is Videos and github | 1 |
| Title/link | Description | Code |
|---|---|---|
| ✅1- Foundations of Computer Vision | Antonio Torralba, Phillip Isola, and William Freeman | --- |
| ✅2- Computer Vision: Algorithms and Applications, 2nd ed | © 2022 Richard Szeliski, The University of Washington | --- |
| ✅3- Foundations of Computer Vision | Antonio Torralba, Phillip Isola, and William Freeman | --- |
| ✅4- Comprehensive Study Resource | A curated collection of books and references for Computer Vision, Machine Learning, Deep Learning, NLP, Python, and more. | |
| ✅5- AI-ML-Roadmap-from-scratch | A curated collection of books and references for Computer Vision, Machine Learning, Deep Learning, NLP, Python, and more. |
|---|
| Category | Models | Notes |
|---|---|---|
| Variational Autoencoders (VAEs) | AlexNet, VGG, ResNet, DenseNet, EfficientNet, ViT 🔴🔵, Swin Transformer 🔴🔵, ConvNeXt 🔵 | Image classification (CNNs and Transformers) |
| Generative Adversarial Networks (GANs) | R-CNN, Fast R-CNN, Faster R-CNN, YOLO, SSD, RetinaNet, DETR 🔴🔵, Mask R-CNN | Detects objects with bounding boxes or masks |
| Limited Boltzmann Machines (RBMs) | FCN, U-Net, DeepLab, PSPNet, SegFormer 🔴🔵, SAM 🔴🔵 | Pixel-level understanding of images |
| Transformer-based Language Models | Autoencoders, VAE, GAN, DCGAN, CycleGAN, StyleGAN, BigGAN, Diffusion Models (DDPM 🔵), DALL·E 🔴🔵, Stable Diffusion 🔵 | Image synthesis & generation |
Legend:
- 🔴 Transformer-based
- 🔵 Introduced after 2020
| Title/link | Description | Code |
|---|---|---|
| ✅1- Top Computer Vision Google Colab Notebooks | Here is a list of the top google colab notebooks that use computer vision to solve a complex problem such as object detection, classification etc: | --- |
| ✅2-roboflow/Notebooks | This repository offers a growing collection of computer vision tutorials. Learn to use SOTA models like YOLOv11, SAM 2, Florence-2, PaliGemma 2, and Qwen2.5-VL for tasks ranging from object detection, segmentation | --- |
| Title/link | Description | Status |
|---|---|---|
| ✅1- Generative AI for Beginners (Version 3) - A Course | 21 Lessons teaching everything you need to know to start building Generative AI applications | Pendin |
| ✅2- Understanding Deep Learning | 21 Lessons teaching everything you need to know to start building Generative AI applications | Pendin |
| ✅3- The Agent Factory Thesis | 21 Lessons teaching everything you need to know to start building Generative AI applications | Pendin |
| ✅4- Best AI/ML course for Beginners to advanced, recommendations? | 21 Lessons teaching everything you need to know to start building Generative AI applications | Pendin |
Understanding all the tools, frameworks, architectures, and ecosystems around Computer Vision can sometimes feel harder than understanding the models themselves.
Below are the ones I’ve explored and used enough to feel confident recommending.
Of course, these won’t solve every use case, and I’m not listing every supporting technology you might need to build real-world AI systems, but it’s a solid starting point.
| Tool / Framework | Description | Resources |
|---|---|---|
| ✅1- RBOT (ROI-Based Object Tracking) | An alternative to YOLO for custom object tracking. Unlike traditional deep learning models that require thousands of images per object, RBOT aims to learn from 50–100 samples and track objects without bounding box detection. | --- |
| ✅2- skimage (scikit-image) | Open-source Python library for image processing and computer vision, built on NumPy/SciPy. | Docs |
| ✅3- OpenCV | The most widely used library for image/video processing, feature extraction, filtering, and classical CV tasks. | Docs |
| ✅4- Ultralytics YOLO | State-of-the-art object detection and segmentation framework, supporting YOLOv5–YOLOv8. | Docs |
| ✅5- Detectron2 | Facebook AI’s modular framework for object detection, segmentation, and keypoint detection. | Docs |
| ✅6- TensorFlow | Google’s end-to-end machine learning and deep learning framework with strong support for production and deployment. | Docs |
| ✅7- PyTorch | Widely used deep learning framework from Meta, popular in research and CV applications due to its flexibility and ease of use. | Docs |
| ✅8- Keras | High-level API for building and training neural networks quickly, running on top of TensorFlow. | Docs |
| ✅9- FastAI | PyTorch-based library for rapid prototyping of CV and NLP models, with high-level abstractions. | Docs |
| ✅10- MMDetection | OpenMMLab’s powerful toolbox for object detection and instance segmentation, supporting hundreds of models. | Docs |
| ✅11- MONAI | PyTorch-based framework for medical imaging, specialized for segmentation, classification, and 3D imaging. | Docs |
| ✅12- Albumentations | Fast and flexible library for image augmentations, widely used in CV pipelines. | Docs |
| ✅13- DVC (Data Version Control) | A tool for versioning datasets and ML experiments, ensuring reproducibility in CV research. | Docs |
| Title/link | Description | Status |
|---|---|---|
| ✅1- Multimodal Data Analysis with Deep Learning | It is Videos and github | pending |
| ✅2-Deploying AI Agents to Production: Architecture, Infrastructure, and Implementation Roadmap | It is Videos and github | pending |
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Fork the repository
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Clone your forked repository using terminal or gitbash.
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Make changes to the cloned repository
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Add, Commit and Push
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Then in Github, in your cloned repository find the option to make a pull request
print("Start contributing for Computer Vision")
- Anybody interested in learning and contributing to computer Vision repository
- There are no hard prerequisites other than a dedication to learning
- Some experience with the following will be beneficial:,C++ Programming, Basic of Computer
- You can only work on issues that have been assigned to you.
- If you want to contribute the algorithm, it's preferrable that you create a new issue before making a PR and link your PR to that issue.
- If you have modified/added code work, make sure the code compiles before submitting.
- Strictly use snake_case (underscore_separated) in your file_name and push it in correct folder.
- Do not update the README.md.
Explore cutting-edge tools and Python libraries, access insightful slides and source code, and tap into a wealth of free online courses from top universities and organizations. Connect with like-minded individuals on Reddit, Facebook, and beyond, and stay updated with our YouTube channel and GitHub repository. Don’t wait — enroll now and unleash your Computer Vision potential!”
We would love your help in making this repository even better! If you know of an amazing Computer Vision course or you know intrested Computer Vision related tutorial/Video that isn't listed here, or if you have any suggestions for improvement in any course content, feel free to open an issue or submit a course contribution request.
Together, let's make this the best AI learning hub website! 🚀
Thanks goes to these Wonderful People. Contributions of any kind are welcome!🚀