🤖 AI/ML GenAI Learning Repository

A structured learning repository covering Mathematics, Machine Learning, Deep Learning, and Generative AI with hands-on code, notebooks, and projects.

Getting Started • Contents • Projects • Contributing

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📚 About This Repository

This repository is a comprehensive learning journey through the world of Artificial Intelligence and Machine Learning. It covers everything from foundational mathematics to cutting-edge Generative AI, with practical implementations, Jupyter notebooks, and real-world projects.

Whether you're a beginner starting your AI journey or an experienced practitioner looking to explore GenAI, this repository provides structured learning paths and hands-on code examples.

🎯 What You'll Learn

• Mathematics Foundations: Linear Algebra, Calculus, Probability & Statistics
• Machine Learning: Supervised & Unsupervised Learning, Model Evaluation
• Deep Learning: Neural Networks, CNNs, RNNs, Transformers
• Generative AI: LLMs, Diffusion Models, GANs, Prompt Engineering
• Practical Applications: End-to-end ML/AI projects

🗂️ Repository Structure

ai-ml-genai-learning/
├── Introduction/           # Getting started with AI/ML
├── start-python/          # Python fundamentals for ML
├── Mathematics/           # Math foundations
│   ├── linear-algebra/
│   ├── calculus/
│   └── statistics/
├── Machine-Learning/      # ML algorithms and techniques
│   ├── supervised/
│   ├── unsupervised/
│   └── model-evaluation/
├── Deep-Learning/         # Neural networks and architectures
│   ├── fundamentals/
│   ├── cnn/
│   ├── rnn/
│   └── transformers/
├── Generative-AI/         # GenAI and LLMs
│   ├── llms/
│   ├── diffusion-models/
│   ├── gans/
│   └── prompt-engineering/
└── Projects/              # End-to-end projects

🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• Jupyter Notebook or JupyterLab
• Basic understanding of Python programming

Installation

1. Clone the repository:

git clone https://github.com/mukeshllawat1/ai-ml-genai-learning.git
cd ai-ml-genai-learning

2. Create a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install required packages:

pip install -r requirements.txt

4. Launch Jupyter Notebook:

jupyter notebook

📖 Learning Path

🔰 Beginner Track

1. Start with Introduction/ to understand AI/ML basics
2. Complete start-python/ for Python fundamentals
3. Study Mathematics/ foundations
4. Begin with basic Machine-Learning/ algorithms

🎓 Intermediate Track

1. Deep dive into Machine-Learning/ techniques
2. Explore Deep-Learning/ fundamentals
3. Build projects using supervised and unsupervised learning
4. Study neural network architectures

🚀 Advanced Track

1. Master Deep-Learning/ architectures (CNNs, RNNs, Transformers)
2. Explore Generative-AI/ models and techniques
3. Work on advanced Projects/
4. Experiment with LLMs and prompt engineering

💻 Key Topics Covered

Machine Learning

• Linear Regression, Logistic Regression
• Decision Trees, Random Forests
• Support Vector Machines (SVM)
• K-Means, DBSCAN Clustering
• Principal Component Analysis (PCA)
• Model Evaluation and Cross-Validation

Deep Learning

• Feedforward Neural Networks
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs, LSTMs)
• Transformer Architecture
• Transfer Learning
• Model Optimization Techniques

Generative AI

• Large Language Models (LLMs)
• GPT, BERT, T5 architectures
• Diffusion Models (Stable Diffusion, DALL-E)
• Generative Adversarial Networks (GANs)
• Prompt Engineering techniques
• Fine-tuning and RLHF

🎨 Projects

This repository includes hands-on projects that demonstrate real-world applications:

• Image Classification: Build CNN models for image recognition
• Text Generation: Create text using RNNs and Transformers
• Sentiment Analysis: Analyze text sentiment using NLP
• Style Transfer: Apply artistic styles to images
• Chatbot Development: Build conversational AI systems
• Image Generation: Create images using GANs and Diffusion models

More projects coming soon!

🛠️ Tools & Libraries

• Core: NumPy, Pandas, Matplotlib, Seaborn
• Machine Learning: Scikit-learn, XGBoost
• Deep Learning: TensorFlow, PyTorch, Keras
• NLP: Transformers (Hugging Face), spaCy, NLTK
• Generative AI: OpenAI API, Langchain, Stable Diffusion
• Visualization: Plotly, TensorBoard

📝 Notebooks

All code examples are provided as Jupyter notebooks with detailed explanations, visualizations, and exercises. Each notebook includes:

• Clear problem statements
• Step-by-step implementations
• Visualizations and plots
• Exercises for practice
• Additional resources

🤝 Contributing

Contributions are welcome! If you'd like to contribute:

1. Fork the repository
2. Create a new branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Please ensure your code follows best practices and includes appropriate documentation.

📧 Contact

Mukesh Llawat

• GitHub: @mukeshllawat1

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🌟 Acknowledgments

• Inspired by the amazing AI/ML community
• Thanks to all contributors and learners
• Special thanks to open-source projects that made this possible

📚 Resources

Books

• "Deep Learning" by Ian Goodfellow
• "Hands-On Machine Learning" by Aurélien Géron
• "Pattern Recognition and Machine Learning" by Christopher Bishop

Courses

• Stanford CS229: Machine Learning
• Fast.ai Practical Deep Learning
• DeepLearning.AI Specializations

Papers

• "Attention Is All You Need" (Transformers)
• "Generative Adversarial Networks" (GANs)
• "Denoising Diffusion Probabilistic Models"

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