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.
- Getting Started
- How to Use This Repository
- Learning Roadmaps
- Career-Oriented Learning Paths
- The Math Behind It All
- Programming & Framework Foundations
- Tools and Frameworks
- Research Papers and Blogs
- AI / ML Communities & Discussion Platforms
- Key & Emerging AI Topics
- Contribution
- License
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.No | Tool / Concept | Resource |
|---|---|---|
| 1 | Python (3.10+) |
Download Python (Official) |
| 2 | VS Code |
Visual Studio Code Download |
| 3 | Virtual Environment (venv) |
Python venv Documentation |
| 4 | Notebooks |
Google Colab / Jupyter Notebook |
| 5 | Python Libraries |
Essential Python Libraries for AI/ML |
- Start with the AI Roadmap if you are new
- Move into ML → DL → specialization (CV, NLP, LLMs, etc.)
- 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.
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.
-
AI Roadmap
Big-picture AI: concepts, history, paradigms, and learning paths -
Data Science Roadmap
Math, statistics, data analysis, visualization, and applied data workflows -
Machine Learning Roadmap
Supervised, unsupervised, classical ML → modern ML -
Deep Learning Roadmap
Neural networks, CNNs, RNNs, Transformers
- Computer Vision Roadmap
Image classification, detection, segmentation, multimodal vision
- NLP Roadmap
Text processing → transformers → modern NLP systems
- LLM Roadmap
Pretraining, fine-tuning, alignment, evaluation
- Generative AI Roadmap
Diffusion, GANs, LLMs, multimodal GenAI systems
- RAG Roadmap
Vector search, embeddings, system design, evaluation
- MLOps & Production AI Roadmap
Deployment, monitoring, scalability, reliability
- Research Scientist Roadmap
Theory, experiments, paper writing, frontier research
- AI Safety & Alignment Roadmap
Ethics, alignment, governance, policy, long-term risk
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.
| Goal | Recommended Order |
|---|---|
| Beginner / CS Student | AI → Math → Python → ML → DL |
| AI Engineer | AI → ML → DL → CV / NLP → LLM |
| Applied ML Engineer | ML → DL → Feature Engineering → Model Tuning → Deployment |
| Data Scientist | Math → Python → ML → Statistics → Data Science |
| GenAI Engineer | AI → DL → LLM → GenAI → RAG |
| Computer Vision Engineer | ML → DL → CV → Multimodal Models |
| NLP Engineer | ML → DL → NLP → Transformers → LLM |
| MLOps Engineer | ML → DL → MLOps → Production Systems |
| Research Scientist (PhD-Level) | ML → DL → Theory → Research Scientist Roadmap |
| AI Safety / Policy | AI → LLM → AI Safety & Alignment |
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.N | Area | AI/ML-Relevant Focus | Best Resource | Type | Level |
|---|---|---|---|---|---|
| 1 | Linear Algebra | Vectors, matrices, geometric intuition | Essence of Linear Algebra – 3Blue1Brown | YouTube Playlist | Beginner |
| 2 | Linear Algebra | Matrix operations for ML models | MIT OCW – Linear Algebra (18.06) | University Course | Beginner |
| 3 | Linear Algebra | Eigenvalues, SVD, PCA | Linear Algebra and Its Applications – Gilbert Strang | Book | Intermediate |
| 4 | Linear Algebra | Matrix factorization, embeddings | Advanced Linear Algebra – Steven Roman | Book | Advanced |
| 5 | Calculus | Derivatives & gradients intuition | Khan Academy – Calculus | YouTube / Course | Beginner |
| 6 | Calculus | Backpropagation, multivariable gradients | MIT OCW – Multivariable Calculus | University Course | Intermediate |
| 7 | Calculus | Deep learning optimization theory | Calculus – Michael Spivak | Book | Advanced |
| 8 | Probability | Random variables, distributions | Harvard Stat 110 – Probability | University Course | Beginner |
| 9 | Probability | Bayes theorem, uncertainty | Khan Academy – Probability | YouTube / Course | Beginner |
| 10 | Probability | Probabilistic ML foundations | A First Course in Probability – Sheldon Ross | Book | Intermediate |
| 11 | Statistics | Data understanding & evaluation | Khan Academy – Statistics | YouTube / Course | Beginner |
| 12 | Statistics | Statistics for Data Science & ML | Statistics – Full Lecture for Data Science (YouTube) | YouTube | Beginner → Intermediate |
| 13 | Statistics | Bias–variance, inference | Statistical Inference – Casella & Berger | Book | Intermediate |
| 14 | Statistics | Bayesian machine learning | MIT OCW – Bayesian Statistics | University Course | Advanced |
| 15 | Optimization | Gradient descent, convex optimization | Convex Optimization – Boyd & Vandenberghe | Book | Intermediate |
| 15.1⭐ | Optimization | Convex optimization fundamentals (Stanford – Stephen Boyd) | Convex Optimization (YouTube Lecture) | YouTube | Intermediate → Advanced |
| 15.2 | Optimization | Optimization for Machine Learning | Optimization in ML – Intro Lecture | YouTube | Intermediate |
| 15.3⭐ | Optimization | Gradient descent & modern optimizers (SGD → Adam) | Deep Learning Optimizers Explained | YouTube | Beginner → Intermediate |
| 15.4 | Optimization | Adaptive optimization methods | Adagrad, RMSprop, Adam Explained | YouTube | Intermediate |
| 15.5 | Optimization | Convex optimization in ML practice | Convex Optimization in Machine Learning | YouTube | Intermediate |
| 16 | Optimization | Training deep neural networks | Numerical Optimization – Nocedal & Wright | Book | Advanced |
| 16.1 | Optimization | Optimization methods for deep learning | Optimization Methods in Deep Learning | YouTube | Intermediate |
| 16.2 | Optimization | Adam optimizer (deep dive) | Adam Optimization Algorithm Explained | YouTube | Intermediate |
This section covers the core programming and tooling foundations required for Machine Learning and Deep Learning.
❗ 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.
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.
- 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
- Retrieval-Augmented Generation (RAG) — https://arxiv.org/abs/2005.11401
- 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
- AlphaFold: Protein Structure Prediction — https://www.nature.com/articles/s41586-021-03819
- Towards Data Science
- Machine Learning Mastery
- Hugging Face Blog
- KDnuggets
- BAIR Blog – Berkeley AI Research
- FastML
- GeeksforGeeks – ML & AI
Learn continuously, ask questions, follow trends, and network
- https://www.reddit.com/r/MachineLearning
- https://www.reddit.com/r/datascience
- https://www.reddit.com/r/LocalLLaMA
- 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 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
-
GitHub Discussions
- Explore the Discussions tab on major AI/ML repos Examples:
-
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
High-impact areas shaping modern AI research and industry applications.
- Transformers & Attention
- Large Language Models (LLMs)
- Multimodal AI (Text, Image, Audio, Video)
- Retrieval-Augmented Generation (RAG)
- AI Agents & Tool-Using Models
- Reinforcement Learning with Human Feedback (RLHF)
- Model Fine-Tuning & Evaluation
- MLOps
- Model Deployment & Monitoring
- AI Safety & Alignment
- Responsible & Explainable AI
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!
This repository is licensed under the MIT License.
Built with ❤️ for the global AI & Computer Science community.