@tom_doerr: Structured roadmaps for AI, ML, and LLM learning https://github.com/bishwaghimire/ai-learning-roadmaps…

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Summary

A comprehensive, open-source GitHub repository providing structured learning roadmaps and curated resources for mastering AI, machine learning, deep learning, and large language models from beginner to advanced levels. Designed for students and professionals, it covers foundational concepts, programming frameworks, career tracks, and emerging AI topics.

Structured roadmaps for AI, ML, and LLM learning https://github.com/bishwaghimire/ai-learning-roadmaps…
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Structured roadmaps for AI, ML, and LLM learning https://github.com/bishwaghimire/ai-learning-roadmaps…

bishwaghimire/ai-learning-roadmaps

Source: https://github.com/bishwaghimire/ai-learning-roadmaps

📚 AI / ML / DL Learning Resources Hub

A structured, end-to-end roadmap to master AI — from fundamentals to cutting-edge research.

A carefully curated, all-in-one repository designed to help Computer Science students, AI enthusiasts, and professionals who want to build strong foundations and progress confidently from beginner to advanced levels. This hub brings together the high-quality books, courses, playlists, research papers, tools, and learning roadmaps covering: Artificial Intelligence, Machine Learning, Deep Learning, Data Science, Transformers, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and MLOps, all organized in a clear, practical, and industry-relevant manner.

The resources are selected to balance theory, intuition, and real-world application, allowing learners to follow modules sequentially or in parallel based on their goals.

⭐ Recommended resources highlight high-impact content widely used in academia, research, and industry, ensuring you focus on what truly matters in modern AI.

Stars Forks Contributions Welcome

Table of Contents

Getting Started

Before starting your AI / Machine Learning journey, ensure that your development environment is properly set up.
Having the right tools in place will help you focus on learning concepts instead of fixing setup issues.

S.NoTool / ConceptResource
1Python (3.10+)Download Python (Official)
2VS CodeVisual Studio Code Download
3Virtual Environment (venv)Python venv Documentation
4NotebooksGoogle Colab / Jupyter Notebook
5Python LibrariesEssential Python Libraries for AI/ML

How to Use This Repository

  1. Start with the AI Roadmap if you are new
  2. Move into ML → DL → specialization (CV, NLP, LLMs, etc.)
  3. Choose your career track:
    • Engineer
    • MLOps / Production
    • Research Scientist
    • AI Safety / Policy

You do not need to follow everything linearly.
These roadmaps are modular but connected.

Learning Roadmaps (Foundations → Advanced)

A complete, structured, and research-grade roadmap collection for Artificial Intelligence
From foundations → specialization → production → research & safety Each roadmap is independent, deep, and industry + research aligned.

Foundations

Specialization Roadmaps

Computer Vision

Natural Language Processing

  • NLP Roadmap
    Text processing → transformers → modern NLP systems

Large Language Models

  • LLM Roadmap
    Pretraining, fine-tuning, alignment, evaluation

Generative AI

Retrieval-Augmented Generation

  • RAG Roadmap
    Vector search, embeddings, system design, evaluation

Engineering & Production

MLOps & Production AI

Research, Safety & Long-Term AI

Research Scientist (PhD-Level)

AI Safety & Alignment

Career-Oriented Learning Paths

Suggested learning sequences based on career goals, industry roles, and research tracks.
These are guidelines, not strict rules — feel free to adapt based on your background.

GoalRecommended Order
Beginner / CS StudentAI → Math → Python → ML → DL
AI EngineerAI → ML → DL → CV / NLP → LLM
Applied ML EngineerML → DL → Feature Engineering → Model Tuning → Deployment
Data ScientistMath → Python → ML → Statistics → Data Science
GenAI EngineerAI → DL → LLM → GenAI → RAG
Computer Vision EngineerML → DL → CV → Multimodal Models
NLP EngineerML → DL → NLP → Transformers → LLM
MLOps EngineerML → DL → MLOps → Production Systems
Research Scientist (PhD-Level)ML → DL → Theory → Research Scientist Roadmap
AI Safety / PolicyAI → LLM → AI Safety & Alignment

The Math Behind It All

This repository contains a curated list of foundational mathematics resources required for AI, Machine Learning, and Data Science.
The resources are organized by subject, difficulty level, and resource type (Book, YouTube Playlist, University Course).

S.NAreaAI/ML-Relevant FocusBest ResourceTypeLevel
1Linear AlgebraVectors, matrices, geometric intuitionEssence of Linear Algebra – 3Blue1BrownYouTube PlaylistBeginner
2Linear AlgebraMatrix operations for ML modelsMIT OCW – Linear Algebra (18.06)University CourseBeginner
3Linear AlgebraEigenvalues, SVD, PCALinear Algebra and Its Applications – Gilbert StrangBookIntermediate
4Linear AlgebraMatrix factorization, embeddingsAdvanced Linear Algebra – Steven RomanBookAdvanced
5CalculusDerivatives & gradients intuitionKhan Academy – CalculusYouTube / CourseBeginner
6CalculusBackpropagation, multivariable gradientsMIT OCW – Multivariable CalculusUniversity CourseIntermediate
7CalculusDeep learning optimization theoryCalculus – Michael SpivakBookAdvanced
8ProbabilityRandom variables, distributionsHarvard Stat 110 – ProbabilityUniversity CourseBeginner
9ProbabilityBayes theorem, uncertaintyKhan Academy – ProbabilityYouTube / CourseBeginner
10ProbabilityProbabilistic ML foundationsA First Course in Probability – Sheldon RossBookIntermediate
11StatisticsData understanding & evaluationKhan Academy – StatisticsYouTube / CourseBeginner
12StatisticsStatistics for Data Science & MLStatistics – Full Lecture for Data Science (YouTube)YouTubeBeginner → Intermediate
13StatisticsBias–variance, inferenceStatistical Inference – Casella & BergerBookIntermediate
14StatisticsBayesian machine learningMIT OCW – Bayesian StatisticsUniversity CourseAdvanced
15OptimizationGradient descent, convex optimizationConvex Optimization – Boyd & VandenbergheBookIntermediate
15.1⭐OptimizationConvex optimization fundamentals (Stanford – Stephen Boyd)Convex Optimization (YouTube Lecture)YouTubeIntermediate → Advanced
15.2OptimizationOptimization for Machine LearningOptimization in ML – Intro LectureYouTubeIntermediate
15.3⭐OptimizationGradient descent & modern optimizers (SGD → Adam)Deep Learning Optimizers ExplainedYouTubeBeginner → Intermediate
15.4OptimizationAdaptive optimization methodsAdagrad, RMSprop, Adam ExplainedYouTubeIntermediate
15.5OptimizationConvex optimization in ML practiceConvex Optimization in Machine LearningYouTubeIntermediate
16OptimizationTraining deep neural networksNumerical Optimization – Nocedal & WrightBookAdvanced
16.1OptimizationOptimization methods for deep learningOptimization Methods in Deep LearningYouTubeIntermediate
16.2OptimizationAdam optimizer (deep dive)Adam Optimization Algorithm ExplainedYouTubeIntermediate

Programming & Framework Foundations

This section covers the core programming and tooling foundations required for Machine Learning and Deep Learning.

S.NTechnologyBest BookBest YouTube PlaylistBest University Course
1PythonPython Crash Course – Eric MatthesLearn Python in 4 HoursMITx: Introduction to Computer Science and Programming Using Python
2NumPyPython for Data Analysis – Wes McKinneyNumPy Tutorial – freeCodeCamp.orgPython for Data Science – NPTEL Official Course
3PandasPython for Data Analysis – Wes McKinneyPandas Tutorial – Corey SchaferData Analysis with Python – IBM (Coursera)
4MatplotlibPython Data Science Handbook – Jake VanderPlasMatplotlib Tutorial – SentdexData Science: Visualization – Harvard Online
5PyTorch / TensorFlowDeep Learning with PyTorch / Hands-On ML with TFPyTorch for Deep Learning & Machine Learning – freeCodeCamp.org
Or
PyTorch Tutorials - Patrick Loeber
/
TensorFlow For Beginners – freeCodeCamp.org		Stanford CS231n – Deep Learning for Computer Vision
/
TensorFlow in Practice – DeepLearning.AI (Coursera)

❗ Note: PyTorch dominates research and rapid experimentation, widely adopted in academia and cutting-edge ML research, while TensorFlow and PyTorch-based deployment tools (TorchServe, ONNX) are widely used in large-scale production systems due to their mature ecosystems and scalability.

Tools and Frameworks

A structured and collapsible list of essential tools used across AI, ML, DL, LLMs, and MLOps.
Focused on industry-standard and widely adopted tools.

Visualization & Analysis
  • Matplotlib — https://matplotlib.org/
  • Seaborn — https://seaborn.pydata.org/
  • Plotly — https://plotly.com/python/
Classical Machine Learning & Data Science
  • Scikit-learn — https://scikit-learn.org/
  • NumPy — https://numpy.org/
  • Pandas — https://pandas.pydata.org/
  • SciPy — https://scipy.org/
  • Statsmodels — https://www.statsmodels.org/
Core Deep Learning Frameworks
  • PyTorch — https://pytorch.org/
  • TensorFlow — https://www.tensorflow.org/
  • JAX — https://github.com/google/jax
NLP, Transformers & Model Hubs
  • Hugging Face (Transformers, Datasets, Hub) — https://huggingface.co/
  • spaCy — https://spacy.io/
  • NLTK — https://www.nltk.org/
LLM, RAG & AI Application Frameworks
  • LangChain — https://www.langchain.com/
  • LlamaIndex — https://www.llamaindex.ai/
  • Haystack — https://haystack.deepset.ai/
Vector Databases & Embedding Stores
  • FAISS — https://github.com/facebookresearch/faiss
  • Pinecone — https://www.pinecone.io/
  • Weaviate — https://weaviate.io/
  • Chroma — https://www.trychroma.com/
Experiment Tracking & MLOps
  • MLflow — https://mlflow.org/
  • Weights & Biases — https://wandb.ai/
  • DVC — https://dvc.org/
Deployment & Serving
  • FastAPI — https://fastapi.tiangolo.com/
  • Docker — https://www.docker.com/
  • Kubernetes — https://kubernetes.io/
  • TorchServe — https://pytorch.org/serve/
Cloud AI Platforms
  • AWS SageMaker — https://aws.amazon.com/sagemaker/
  • Google Vertex AI — https://cloud.google.com/vertex-ai
  • Azure Machine Learning — https://azure.microsoft.com/en-us/products/machine-learning

⚠️ Note:
This list is intentionally curated. Tools are chosen based on adoption, stability, and relevance across AI subfields.

Research Papers and Blogs

Core & Foundational Papers

  • Attention Is All You Need — https://arxiv.org/abs/1706.03762
  • BERT: Pre-training of Deep Bidirectional Transformers — https://arxiv.org/abs/1810.04805
  • GPT-3: Language Models are Few-Shot Learners — https://arxiv.org/abs/2005.14165
  • Generative Adversarial Networks (GANs) — https://arxiv.org/abs/1406.2661

Modern LLM & System Design

  • Retrieval-Augmented Generation (RAG) — https://arxiv.org/abs/2005.11401

Computer Vision

  • ResNet: Deep Residual Learning — https://arxiv.org/abs/1512.03385
  • Vision Transformer (ViT) — https://arxiv.org/abs/2010.11929
  • YOLOv4: Optimal Speed & Accuracy for Object Detection — https://arxiv.org/abs/2004.10934
  • U-Net: Biomedical Image Segmentation — https://arxiv.org/abs/1505.04597

Special & Interdisciplinary

  • AlphaFold: Protein Structure Prediction — https://www.nature.com/articles/s41586-021-03819

Official & Research Blogs

Community & Practical Learning Blogs

AI / ML Communities & Discussion Platforms

Learn continuously, ask questions, follow trends, and network

Reddit

  • https://www.reddit.com/r/MachineLearning
  • https://www.reddit.com/r/datascience
  • https://www.reddit.com/r/LocalLLaMA

Discord

  • Hugging Face Discord — https://discord.com/invite/hugging-face-879548962464493619
  • OpenAI Community (Official Discord) — https://discord.com/servers/openai-974519864045756446
  • Learn AI Together (AI / ML Study Group) — https://discord.com/invite/learn-ai-together
  • MLSpace (Machine Learning Community) — https://discord.com/invite/4RMwz64gdH

Telegram

Telegram links can change often; these are curated and commonly used entry points.

  • Machine Learning & Artificial Intelligence | Data Science https://t.me/datasciencefree
  • Machine Learning - https://t.me/DataScienceM
  • Python Data Science Machine Learning - https://t.me/DataScience9
  • ML Research Hub - https://t.me/DataScienceT
  • AI & Deep Learning - https://t.me/deeplearning005
  • Artificial Intelligence - https://t.me/Artificial_intelligence_in
  • Deep Learning & AI Updates — https://t.me/DeepLearning_ai

Other Communities

  • GitHub Discussions

    • Explore the Discussions tab on major AI/ML repos Examples:
      • https://github.com/huggingface/transformers/discussions
      • https://github.com/pytorch/pytorch/discussions

  • Stack Overflow (Tags)

    • Machine Learning — https://stackoverflow.com/questions/tagged/machine-learning
    • Deep Learning — https://stackoverflow.com/questions/tagged/deep-learning
    • NLP — https://stackoverflow.com/questions/tagged/nlp

Key & Emerging AI Topics

High-impact areas shaping modern AI research and industry applications.

Foundations & Model Architectures

  • Transformers & Attention
  • Large Language Models (LLMs)
  • Multimodal AI (Text, Image, Audio, Video)

LLM Systems & Applications

  • Retrieval-Augmented Generation (RAG)
  • AI Agents & Tool-Using Models

Training, Optimization & Alignment

  • Reinforcement Learning with Human Feedback (RLHF)
  • Model Fine-Tuning & Evaluation

Production & Lifecycle

  • MLOps
  • Model Deployment & Monitoring

Safety, Ethics & Governance

  • AI Safety & Alignment
  • Responsible & Explainable AI

Contribution

We welcome contributions from everyone, whether you are a beginner, practitioner, or researcher. You can help by adding new resources, suggesting improvements, fixing broken links, or sharing your insights to make this repository even more helpful.

Before submitting your changes, please review the CONTRIBUTING file for guidelines on how to contribute effectively. Every contribution counts and helps the community learn faster and better!

License

This repository is licensed under the MIT License.

Acknowledgements

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