A comprehensive learning repository for engineers at all levels to navigate the AI landscape. From foundational concepts to production-ready implementations, this resource guides you through AI/ML fundamentals, Large Language Models, prompt engineering, and modern GenAI tools.
This repository serves engineers across the experience spectrum:
New Graduates will find structured learning paths that build from mathematical foundations through practical AI applications, with hands-on projects to develop portfolio-worthy skills.
Mid-Level Engineers transitioning to AI will discover how to leverage their existing software engineering expertise while filling knowledge gaps in ML/LLM concepts and tooling.
Senior Engineers can dive directly into production considerations, MLOps practices, and advanced topics like agent architectures and responsible AI governance.
| Directory | Description |
|---|---|
| ai-fundamentals/ | What is AI, history, how models work, training explained, predictive vs generative AI |
| Directory | Description |
|---|---|
| foundations/ | Mathematical prerequisites, ML basics, deep learning, and LLM fundamentals |
| learning-paths/ | Structured tracks organized by experience level and role |
| prompt-engineering/ | Techniques, patterns, and best practices for effective prompting |
| llm-systems/ | RAG, retrieval, agents, tools, and multimodal systems |
| Directory | Description |
|---|---|
| genai-tools/ | Guides for GitHub Copilot, Windsurf, Claude AI, Gemini, and ChatGPT |
| agents/ | Agent architectures, customization, and implementation patterns |
| Directory | Description |
|---|---|
| practical-skills/ | Building, debugging, evaluating, and operating AI features |
| production-ml-llm/ | MLOps, deployment patterns, monitoring, and governance |
| best-practices/ | Evaluation strategies, security, UX design, and reproducibility |
| Directory | Description |
|---|---|
| career-and-self-development/ | Career paths, interview prep, portfolios, and growth strategies |
| ethics-and-responsible-ai/ | Fairness, safety, privacy, and organizational guidelines |
| Directory | Description |
|---|---|
| resources/ | Curated external links, reading lists, and reference materials |
| projects-and-templates/ | Starter projects and documentation templates |
- Start with ai-fundamentals/what-is-ai.md to understand what AI is
- Learn the vocabulary in ai-fundamentals/key-terminology.md
- Understand ai-fundamentals/how-models-work.md with visual diagrams
- Learn ai-fundamentals/training-models.md to see how AI learns
- Compare approaches in ai-fundamentals/predictive-vs-generative.md
- Then continue to the New Graduate track below
- Start with foundations/math-for-ml.md for mathematical prerequisites
- Progress through foundations/ml-fundamentals.md
- Learn LLM basics in foundations/llm-fundamentals.md
- Practice with prompt-engineering/fundamentals.md
- Build your first project using projects-and-templates/
- Set up your tools with genai-tools/
- Review foundations/ml-fundamentals.md for core concepts
- Deep dive into foundations/llm-fundamentals.md
- Master prompt-engineering/ techniques
- Learn deployment patterns in production-ml-llm/deployment-patterns.md
- Understand llm-systems/rag-and-retrieval.md
- Start with llm-systems/ for advanced LLM concepts
- Explore agents/ for agent architectures
- Review production-ml-llm/ for deployment strategies
- Study best-practices/evaluation-for-llms.md
- Implement security measures from best-practices/security-for-ai-apps.md
For Self-Paced Learning: Follow the learning paths appropriate to your level. Each section includes explanations, examples, and "Try This" exercises.
As a Reference: Use the table of contents and search functionality to find specific topics when you need them.
For Team Onboarding: Share relevant learning paths with new team members. The structured approach helps standardize AI knowledge across teams.
For Project Planning: Reference the best practices, templates, and checklists when starting new AI features.
This is a living document. As AI evolves rapidly, contributions to keep content current are welcome! See CONTRIBUTING.md for detailed guidelines.
Quick contribution ideas:
- Fix errors or outdated information
- Add new examples or exercises
- Improve explanations
- Add coverage of new topics
- Translate content
This project is licensed under the MIT License - see the LICENSE file for details.
This means you are free to use, modify, and distribute this content for any purpose, including commercial use.
Navigate the AI landscape with confidence. Start your journey today.