A comprehensive collection of learning pathways mapping the machine learning landscape for different roles and backgrounds. Like a detailed atlas, this repository guides you through various territories of ML, helping you chart the most effective route to your learning destination.
This repository aims to:
- Provide structured learning paths tailored to different roles and backgrounds
- Offer flexibility in learning by modularizing content
- Enable learners to choose paths based on their goals and existing knowledge
- Bridge the gap between theory and practical applications
graph LR
A[Entry] --> B[SDE to AI]
A --> C[ML Engineer]
A --> D[Product Manager]
A --> E[Data Scientist]
Each learning path is carefully curated for specific roles:
- SDE to AI: Helps software engineers leverage their programming background to transition into AI development
- ML Engineer: Focuses on building and deploying ML systems at scale
- Product Manager: Emphasizes AI product development, evaluation, and lifecycle management
- Data Scientist: Concentrates on data analysis, model development, and statistical inference
- Linear Algebra to LLM: Provides a mathematical foundation journey from basic linear algebra to understanding and building large language models
- AWS Certification for AI/ML: Guides professionals through AWS certifications and services specifically tailored for machine learning deployment and operations in the cloud
Our modules cover a wide range of topics, organized into major categories. For detailed descriptions, prerequisites, and learning objectives of each module, please refer to our modules guide.
- Mathematics: Linear algebra, calculus, probability & statistics
- Programming: Python fundamentals, coding best practices
- Algorithms: Data structures, complexity analysis, optimization
- Data Processing: Data cleaning, analysis, and visualization
- Machine Learning: Supervised/unsupervised learning, model evaluation
- Deep Learning: Neural networks, architectures, training techniques
- Natural Language Processing: Text processing, transformers, BERT
- Computer Vision: Image processing, CNNs, object detection
- Generative AI: GANs, diffusion models, text-to-image
- Large Language Models: Transformers architecture, fine-tuning, prompt engineering
- Recommender Systems: Collaborative filtering, content-based systems
- AI Tools: Popular frameworks, development environments, practical applications
- MLOps: CI/CD for ML, model versioning, experiment tracking
- Deployment: Model serving, API development, containerization
- System Design: Scalable ML systems, microservices architecture
- Project Management: End-to-end ML project lifecycle
Each module is designed to be self-contained while building upon foundational concepts. Mix and match these modules based on your chosen learning path or create your own custom learning journey. See modules.md for comprehensive information about each module's content, requirements, and learning outcomes.
We welcome contributions! Please see our CONTRIBUTING.md for guidelines.
This project is licensed under the MIT License - see the LICENSE.md file for details.
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