Artificial Intelligence Roadmap — Fundamentals to Advanced

1. Prerequisites

1.1 Programming fundamentals

1.2 Software engineering fundamentals

1.3 Data fundamentals

1.4 Computer science fundamentals

1.5 Hardware fundamentals

2. AI Landscape

2.1 Artificial Intelligence fundamentals

2.2 Core AI disciplines

2.3 AI system types

2.4 AI development lifecycle

2.5 AI roles

3. Mathematics for AI

3.1 Linear algebra

3.2 Calculus

3.3 Probability

3.4 Statistics

3.5 Optimization

3.6 Information theory

4. Python AI Ecosystem

4.1 Core libraries

4.2 Deep learning frameworks

4.3 NLP and LLM libraries

4.4 Computer vision libraries

4.5 MLOps libraries

4.6 Inference libraries

5. Data Engineering for AI

5.1 Data collection

5.2 Data storage

5.3 Data formats

5.4 Data cleaning

5.5 Data labeling

5.6 Data pipelines

6. Exploratory Data Analysis

6.1 Dataset understanding

6.2 Visualization

6.3 Statistical analysis

6.4 Data leakage analysis

7. Classical Machine Learning

7.1 ML fundamentals

7.2 Supervised learning

7.3 Unsupervised learning

7.4 Semi-supervised and weakly supervised learning

7.5 Feature engineering

7.6 Model selection

7.7 Classical ML evaluation

8. Deep Learning Foundations

8.1 Neural network basics

8.2 Activation functions

8.3 Loss functions

8.4 Optimization in deep learning

8.5 Regularization

8.6 Neural network architectures

8.7 Training diagnostics

9. Computer Vision

9.1 Image fundamentals

9.2 CNNs

9.3 Vision architectures

9.4 Vision tasks

9.5 Object detection

9.6 Image segmentation

9.7 Vision data engineering

10. Natural Language Processing

10.1 Text fundamentals

10.2 Tokenization

10.3 Classical NLP

10.4 Word embeddings

10.5 Sequence models

10.6 NLP evaluation

11. Transformers

11.1 Transformer fundamentals

11.2 Attention mechanisms

11.3 Transformer model families

11.4 Training transformers

11.5 Transformer limitations

12. Large Language Models

12.1 LLM fundamentals

12.2 LLM capabilities

12.3 LLM limitations

12.4 LLM model selection

12.5 LLM APIs

12.6 LLM decoding

13. Prompt Engineering

13.1 Prompt fundamentals

13.2 Prompting techniques

13.3 Structured output

13.4 Prompt evaluation

13.5 Prompt security

14. Retrieval-Augmented Generation

14.1 RAG fundamentals

14.2 Document ingestion

14.3 Chunking strategies

14.4 Embeddings

14.5 Vector databases

14.6 Retrieval strategies

14.7 RAG evaluation

14.8 Advanced RAG

15. AI Agents

15.1 Agent fundamentals

15.2 Agent architectures

15.3 Tool use

15.4 Model Context Protocol

15.5 Agent memory

15.6 Agent planning

15.7 Agent evaluation

15.8 Agent safety

16. Multimodal AI

16.1 Multimodal fundamentals

16.2 Vision-language models

16.3 Audio AI

16.4 Video AI

16.5 Multimodal RAG

17. Generative AI

17.1 Generative modeling fundamentals

17.2 Diffusion models

17.3 Generative adversarial networks

17.4 Variational autoencoders

17.5 Generative AI product patterns

17.6 Generative AI risks

18. Fine-Tuning and Adaptation

18.1 Fine-tuning fundamentals

18.2 Parameter-efficient fine-tuning

18.3 LLM fine-tuning data

18.4 Alignment methods

18.5 Fine-tuning evaluation

18.6 Fine-tuning operations

19. Reinforcement Learning

19.1 RL fundamentals

19.2 Classical RL

19.3 Deep RL

19.4 RLHF and preference optimization

19.5 RL applications

20. Recommendation Systems

20.1 Recommender fundamentals

20.2 Collaborative filtering

20.3 Content-based recommendation

20.4 Deep recommenders

20.5 Recommender evaluation

21. Graph Machine Learning

21.1 Graph fundamentals

21.2 Graph neural networks

21.3 Graph ML tasks

21.4 Knowledge graphs and AI

22. Time-Series AI

22.1 Time-series fundamentals

22.2 Classical time-series models

22.3 Deep time-series models

22.4 Time-series tasks

22.5 Time-series evaluation

23. Evaluation and Benchmarking

23.1 Evaluation fundamentals

23.2 ML evaluation

23.3 LLM evaluation

23.4 RAG evaluation

23.5 Agent evaluation

23.6 Evaluation methods

23.7 Evaluation risks

24. AI Safety and Security

24.1 AI safety fundamentals

24.2 LLM security risks

24.3 Adversarial ML

24.4 Guardrails

24.5 AI application security

24.6 Privacy

24.7 Responsible AI

25. Explainability and Interpretability

25.1 Explainability fundamentals

25.2 Classical ML explainability

25.3 Deep learning interpretability

25.4 LLM interpretability

26. MLOps

26.1 MLOps fundamentals

26.2 Experiment management

26.3 Feature stores

26.4 Model registry

26.5 ML pipelines

26.6 Model monitoring

27. LLMOps and AgentOps

27.1 LLMOps fundamentals

27.2 Prompt operations

27.3 LLM observability

27.4 Agent operations

27.5 Model gateway

28. Deployment and Inference

28.1 Deployment patterns

28.2 Model serving

28.3 LLM inference optimization

28.4 Quantization

28.5 Distillation and compression

28.6 Edge AI deployment

29. AI Infrastructure

29.1 Compute infrastructure

29.2 Distributed training

29.3 Storage infrastructure

29.4 Network infrastructure

29.5 Kubernetes for AI

29.6 Cost optimization

30. AI Product Engineering

30.1 Product discovery

30.2 AI UX

30.3 Human-in-the-loop workflows

30.4 AI product metrics

30.5 AI product risks

31. AI Governance and Compliance

31.1 Governance fundamentals

31.2 Documentation artifacts

31.3 AI risk management

31.4 Regulatory awareness

31.5 AI audit readiness

32. AI Ethics and Societal Impact

32.1 Fairness

32.2 Transparency

32.3 Accountability

32.4 Environmental impact

33. AI for Software Engineering

33.1 Code intelligence

33.2 AI coding agents

33.3 Code RAG

33.4 AI-assisted SDLC

33.5 Risks in AI coding

34. AI Research Literacy

34.1 Reading papers

34.2 Research evaluation

34.3 Keeping up to date

34.4 Reproducibility

35. Current Ecosystem Awareness

35.1 Core ML ecosystem

35.2 GPU and acceleration ecosystem

35.3 LLM ecosystem

35.4 Safety and governance ecosystem

OWASP Top 10 for LLM Applications awareness
NIST AI Risk Management Framework awareness
NIST Generative AI Profile awareness
AI model cards awareness
Dataset cards awareness
Red teaming practices awareness
Prompt injection testing awareness
AI incident response awareness
AI audit readiness awareness

35.5 Deployment ecosystem

36. Learning Path

36.1 Phase 1 — Foundations

36.2 Phase 2 — Classical machine learning

36.3 Phase 3 — Deep learning

36.4 Phase 4 — NLP, vision, and multimodal

36.5 Phase 5 — LLM engineering

36.6 Phase 6 — Agents and AI applications

36.7 Phase 7 — MLOps and production

36.8 Phase 8 — Advanced AI engineering

37. Practical Projects

37.1 Project 1 — Classical ML Pipeline

37.2 Project 2 — Deep Learning Image Classifier

37.3 Project 3 — NLP Text Classifier

37.4 Project 4 — RAG Document Assistant

37.5 Project 5 — Tool-Calling Agent

37.6 Project 6 — AI Coding Assistant for a Repository

37.7 Project 7 — Fine-Tuned Domain Assistant

37.8 Project 8 — LLM Evaluation Platform

37.9 Project 9 — AI Observability Stack

37.10 Project 10 — Production Inference Service

37.11 Project 11 — Multimodal Assistant

37.12 Project 12 — AI Governance Registry

38. Competency Checklist

38.1 Junior AI competency

38.2 Mid-level AI competency

38.3 Senior AI competency

38.4 Advanced AI engineering competency

38.5 AI safety and governance competency