Machine Learning with PyTorch — Complete 🎯 Learning Outcomes

By completing this curriculum, you'll be able to:

• Understand Machine Learning: Grasp fundamental concepts like supervised/unsupervised learning, bias-variance tradeoff, and model evaluation
• Master PyTorch: Build and train neural networks from scratch using PyTorch's powerful tools
• Solve Real Problems: Apply machine learning to computer vision, NLP, and other domains
• Deploy Models: Take your models from notebook to production
• Stay Current: Understand cutting-edge techniques like transformers and generative AI
• Think Like an ML Engineer: Develop intuition for when and how to apply different techniquesing Path 🚀

A comprehensive, beginner-to-advanced guide for learning machine learning and deep learning with PyTorch.
Perfect for those who want to understand both the theory and practice of modern AI.

📚 What You'll Learn

Complete Machine Learning & Deep Learning Curriculum

• 🎯 Machine Learning Fundamentals → Understanding core concepts, supervised/unsupervised learning
• 🔥 PyTorch Mastery → Tensors, autograd, neural networks, and advanced techniques
• 👁️ Computer Vision → Image classification, object detection, segmentation, transfer learning
• 🧠 Deep Learning Theory & Practice → From perceptrons to transformers
• 🎨 Generative Models → GANs, VAEs, and creative AI applications
• 📝 Natural Language Processing → Text processing, embeddings, RNNs, transformers
• 🔬 Real-World Projects → End-to-end ML projects with deployment

Our Learning Philosophy:

• 📊 Visual Learning: Rich visualizations, plots, and interactive examples
• 🎯 Theory + Practice: Clear explanations backed by hands-on code
• 🏗️ Progressive Complexity: From basics to advanced, step by step
• 🚀 Real-World Focus: Practical skills you can apply immediatelyte Learning Path 🚀

A structured, code-focused guide for mastering PyTorch, from fundamentals to advanced deep learning applications.
Built for developers who prefer learning by doing.

📚 What’s Inside

Code-First, Project-Based Curriculum

• ✅ Tensor Basics → Neural Networks → Autograd
• 🔄 Computer Vision (Classification, Detection, Segmentation)
• 🔄 Generative Models (GANs)
• 🔄 Natural Language Processing (Coming Soon)
• 🔄 Transformers (Coming Soon)
• 🔄 End-to-End Projects (Coming Soon)

We focus on practical implementations over theory-heavy explanations.
Just enough math to understand the ideas.

🎯 Learning Outcomes

By following this curriculum, you’ll be able to:

• Build neural networks from scratch using PyTorch
• Implement image recognition, segmentation, object detection, and generative models
• Understand and build transformers for NLP and vision
• Apply PyTorch to real-world projects and deploy models

🗂️ Repository Structure

├── 01_basics/
├── 02_computer_vision/
├── 03_nlp/               ← Coming Soon
├── 04_segmentation/      ← Coming Soon
├── 05_gans/              ← Coming Soon
├── 06_transformers/      ← Coming Soon
├── 07_projects/          ← Coming Soon
├── 08_utils/             ← Coming Soon
├── 09_images/
├── src/                  ← Scripts and reusable modules
├── examples/             ← Interactive apps (Gradio/Streamlit)
├── tests/                ← Unit and integration tests
├── requirements.txt
└── README.md

🚀 Quickstart Guide

git clone https://github.com/yourusername/pytorch-complete.git
cd pytorch-complete

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

Start exploring notebooks in 01_basics/.
For a complete walkthrough, follow the learning path progression below.

📊 Learning Path Progression

Stage	Folder	Focus Area	Status
Foundations	01_basics/	ML fundamentals, PyTorch basics, neural networks	✅ Complete
Computer Vision	02_computer_vision/	Image classification, CNNs, transfer learning	✅ Complete
Natural Language	03_nlp/	Text processing, RNNs, attention mechanisms	🔄 Coming Soon
Segmentation	04_segmentation/	Pixel-level prediction, U-Net, medical imaging	🔄 Coming Soon
Generative AI	05_gans/	GANs, VAEs, creative applications	🔄 Coming Soon
Transformers	06_transformers/	Attention, BERT, GPT, Vision Transformers	🔄 Coming Soon
Real Projects	07_projects/	End-to-end ML projects with deployment	🔄 Coming Soon

🎓 Skill Progression

Beginner → Intermediate → Advanced → Expert
    ↓           ↓            ↓         ↓
 01_basics  02_vision   03_nlp +   07_projects
             04_seg     05_gans
                       06_transformers

💻 Code-First Learning Philosophy

• Hands-On Code Examples
• Visual Explanations (Diagrams & Plots)
• Minimal Math, Maximum Implementation
• Ready-to-Use Patterns

🔧 Prerequisites

For Complete Beginners:

• Basic Python programming (variables, functions, loops)
• High school mathematics (algebra, basic statistics)
• Curiosity and willingness to learn!

What You DON'T Need:

• Advanced mathematics or statistics background
• Prior machine learning experience
• Deep learning knowledge
• PhD in computer science

What We'll Teach You:

• Machine learning concepts from the ground up
• Mathematics as needed (explained simply)
• PyTorch from beginner to advanced
• Industry best practices and real-world applications

🤝 Contributing

We welcome contributions!

git checkout -b feature/improvement
git commit -am 'Add new content'
git push origin feature/improvement

Open a Pull Request with your improvements.

📝 License

Licensed under the MIT License. See the LICENSE file for details.

Note: This repository is continuously updated. Check back regularly for new content!