Your complete gateway to 647+ free AI/ML courses, papers, tools, and datasets for beginners to advanced learners
| Metric | Value | Details |
|---|---|---|
| Total Resources | 647+ | Across all categories (updated Jan 22, 2026, 10:40 PM UTC+4) |
| Total Categories | 32 | Organized by topic & expertise level (+1 AI Evals NEW) |
| Average/Category | ~20 | Well-distributed across topics |
| Recent Growth | +6 resources (Jan 22, 2026) | Thursday: Prompt Engineering (+2), Generative AI/Agents (+2), Advanced NLP (+2) = +6 ✨ |
| Top Categories | NLP (62), Audio (41), Generative AI (40), Mathematics (38), AI Tools (37), GNN (33), Robotics (30), Prompt Engineering (33), Multimodal (24), Recommender (28), Healthcare (29), AI Evals (10) | Comprehensive coverage |
| 2025-2026 Content | 98%+ | Latest research & emerging trends prioritized |
| Free Resources | 100% | No paywalls, completely free |
| Quality Standard | High | All personally vetted |
| Last Updated | Jan 22, 2026, 10:40 PM UTC+4 | Daily verification & updates |
- Stanford CS229: Machine Learning by Andrew Ng - Comprehensive graduate-level Stanford course covering supervised learning, unsupervised learning, learning theory, reinforcement learning, SVMs, neural networks with full lecture notes and video recordings
- Matrix Calculus for Machine Learning and Beyond - Advanced mathematics covering differential calculus on vector spaces, gradients, Jacobians, Hessians with applications to optimization and large-scale ML algorithms
- Deep Learning Full Course 2026 (Simplilearn) - Comprehensive 10+ hour course covering CNNs, RNNs, LSTMs, attention mechanisms, transformers, GANs, YOLO object detection, and NLP fundamentals with hands-on TensorFlow/Keras code
- Dive into Deep Learning (d2l.ai) - Interactive open-source textbook with runnable Jupyter notebooks covering neural networks, CNNs, RNNs, attention mechanisms, transformers in both PyTorch and TensorFlow
- Stanford CS224N: Natural Language Processing with Deep Learning - World-renowned Stanford course by Christopher Manning covering neural networks, RNNs, LSTMs, transformers, language models, RLHF with full video lectures and materials
- Generative AI for Beginners: 21 Lessons (Microsoft) - Free comprehensive course by Microsoft covering generative AI fundamentals, LLMs, transformers, prompt engineering, building GenAI applications, RAG, fine-tuning, responsible AI
- Stanford CS231N Spring 2025 - Latest Offering - Latest 2025 edition with vision transformers, foundation models, diffusion-based image generation, and modern computer vision architectures with free video lectures
- Computational Thinking with Computer Vision - Educational course teaching computational thinking through computer vision applications with focus on critical thinking and AI ethics in modern systems
- ChatGPT Prompt Engineering for Developers (DeepLearning.AI & OpenAI) – Developer-focused short course teaching prompt patterns for summarization, classification, transformation, and multi-step workflows with real-world code examples.
- DSPy: Programming Language Models (Stanford) – Advanced framework for programmatic prompt optimization and declarative LLM workflows, compiling high-level specs into optimized prompts, retrieval, and tool calls.
- LangGraph: Building Stateful LLM Agents (LangChain AI) – Agentic framework for constructing robust, stateful LLM agents and workflows as graphs, with tools, memory, and error handling.
- Microsoft AutoGen: Multi-Agent Framework – Open-source multi-agent orchestration framework for building collaborative LLM agents with tool use, code execution, and human-in-the-loop workflows.
- Stanford CS224U: Natural Language Understanding – Advanced course on semantics, representation learning, and grounded language understanding tasks such as question answering and entailment.
- Oxford Deep Learning for Natural Language Processing (2017) – Classic deep-learning-for-NLP course materials covering RNNs, CNNs, seq2seq, attention, and early neural architectures.
- Sunday Jan 12: Data Science (+4), ML Fundamentals (+3), Mathematics (+5) = +12 resources
- Monday Jan 13: NLP (+7) = +7 resources
- Tuesday Jan 14: Robotics & Embodied AI (+4) = +4 resources
- Wednesday Jan 15: MLOps (+4), Edge AI (+3), AI Security (+3) = +10 resources
- Thursday Jan 16: Healthcare AI (+5), Finance AI (+5), Recommender Systems (+5) = +15 resources
- Friday Jan 17: Reinforcement Learning (+5), Time Series (+5), Audio/Speech (+4) = +14 resources
- Saturday Jan 18: [Rest day - no updates]
- Sunday Jan 19: Ethics (+3), Tools (+4), Evals (+10 NEW CATEGORY) = +17 resources
- Monday Jan 20 (Tuesday rotation): GenAI (+4), Prompt Eng (+4), NLP (+4) = +12 resources
- Wednesday Jan 21: ML Fundamentals (+2), Deep Learning (+2), NLP (+2), Computer Vision (+2) = +8 resources
- Thursday Jan 22 (TODAY): Prompt Engineering (+2), Generative AI/Agents (+2), Advanced NLP (+2) = +6 resources
- Total (Jan 12–22): 555 → 647 = +92 new resources in 11 days 🚀
Core Pattern: Jan 21 focused on core university/intermediate courses; Jan 22 added extended specializations in prompt engineering, agents, and advanced NLP.
Goal: Understand AI/ML fundamentals and build your first project
| Week | Focus | Resources | Time/Week |
|---|---|---|---|
| 1-2 | Foundations | Math for AI, ML Fundamentals | 10-12 hrs |
| 3 | Programming | Data Science Basics | 8-10 hrs |
| 4-5 | Hands-on | Datasets, First Project | 10-12 hrs |
| 6 | Ethics & Impact | AI Ethics, XAI Basics | 6-8 hrs |
Starting Point: Harvard CS50 AI (most beginner-friendly)
Goal: Master a specialization and build portfolio projects
| Path | Focus | Duration | Key Resources |
|---|---|---|---|
| Vision | Image understanding, detection, 2025 trends | 10 weeks | Computer Vision (27) → Multimodal AI (24) |
| Multimodal | Vision-language models, VLM architectures, agents | 10 weeks | Multimodal AI (24) → Generative AI (40) |
| Audio AI | Speech recognition, synthesis, voice agents | 10 weeks | Audio & Speech (41) → Generative AI |
| Interpretability | Understanding models, debugging, trust, fairness | 8-10 weeks | Explainable AI (9) → AI Ethics |
| NLP | Language models, transformers, LLMs, fine-tuning | 10 weeks | NLP (62) → Generative AI (40) |
| Evaluation | Benchmarking, testing, quality assurance | 8 weeks | AI Evals (10 NEW) → Production systems |
| Graph ML | Graph neural networks, knowledge graphs | 10 weeks | Graph Neural Networks (33) → Recommender Systems (28) |
| Production | MLOps, deployment, systems | 10 weeks | MLOps (18) → AI Hardware |
| Finance | Trading, risk, prediction | 10 weeks | Time Series → Finance AI (27) |
| Healthcare | Medical AI, diagnosis, imaging | 12 weeks | Computer Vision → Healthcare AI (29) |
| Robotics | Robot learning, embodied AI, autonomous systems | 12 weeks | Robotics & Embodied AI (30) → RL (29) |
| RL | Sequential decision-making, policy optimization, agents | 12 weeks | Reinforcement Learning (29) → Robotics |
| Time Series | Forecasting, foundation models, LLM reasoning | 10 weeks | Time Series (26) → Generative AI |
Starting Point: Choose your specialization above
Goal: Cutting-edge research, implementation, contribution
Recommended Path:
- Emerging Fields: Spatial Intelligence, World Models, Quantum AI
- Research: Research Papers, arXiv
- University Courses: Stanford CS224N/CS224U (NLP), Stanford CS224W (GNNs), MIT (Robotics)
- Implementation: Paper reproduction, open-source contribution
Goal: Build core knowledge and math foundations
| Category | Resources | Difficulty | Focus |
|---|---|---|---|
| Mathematics for AI | 38 | 🟢 | Linear algebra, calculus, stats |
| Machine Learning Fundamentals | 18 | 🟢 | Core ML concepts, CS229, matrix calculus |
| Data Science & Analytics | 11 | 🟢 | EDA, visualization, SQL |
Total: ~67 resources | Perfect for: Complete beginners
Goal: Master specialized AI/ML domains
| Category | Resources | Difficulty | Focus | Latest |
|---|---|---|---|---|
| Deep Learning & Neural Networks | 12 | 🟡 | Architectures, d2l.ai, Simplilearn | Foundation models |
| Natural Language Processing | 62 | 🟡🔴 | Language understanding | Stanford CS224N/CS224U, RAG, fine-tuning, instruction tuning, RLHF, GNNs for NLP, semantic search 2025–2026 |
| Computer Vision | 27 | 🟡🔴 | Image understanding | Stanford CS231N Spring 2025, OpenCV, CU Boulder |
| Reinforcement Learning | 29 | 🟡🔴 | Agent training, curriculum learning, meta-RL | Cambridge Advanced RL, curriculum automation, 2025 research |
| Generative AI | 40 | 🔴 | LLMs, diffusion, VLMs, agents | Llama 3.3, GPT-4.5, O3, DeepSeek-Janus, LLaVA-NeXT, LangGraph, AutoGen 2025–2026 |
| Graph Neural Networks | 33 | 🔴 | Graph learning | AAAI 2025, UVA, Graphcore, Distill.pub |
| Prompt Engineering | 33 | 🟡🔴 | LLM interaction | Claude 3.7, Gemini 2.0, DeepLearning.AI/OpenAI Dev course, DSPy, agentic AI, multi-turn, production MLOps 2026 |
| Time Series Forecasting | 26 | 🟡🔴 | Temporal prediction, foundation models, LLM reasoning | Transformers, TimeGPT, temporal reasoning, 2025 cutting-edge |
| Recommender Systems | 28 | 🟡🔴 | Personalization | Agentic AI, LLMs, RecSys 2025, GNNs |
| Audio & Speech | 41 | 🟡🔴 | Speech/Audio AI, emotional TTS, voice agents | 2025 Foundation models, TTIC workshop, zero-shot adaptation |
| Multimodal AI | 24 | 🟡🔴 | Cross-modal learning | VLM architectures, robotics, medical |
| Robotics & Embodied AI | 30 | 🟡🔴 | Autonomous systems, robot learning | MIT 2025, Stanford embodied foundation models, VizFlyt |
Total: ~387+ resources | Perfect for: Ready to specialize
Ensuring AI quality, fairness, and interpretability
| Category | Resources | Difficulty | Latest | Market Trend |
|---|---|---|---|---|
| AI Evals & Evaluation | 10 | 🟡 | 2026 NEW: Leaderboards, HELM, DeepEval, Ragas | Quality assurance critical |
| Explainable AI (XAI) | 9 | 🟡🔴 | 2025 guide, fairness XAI, ViT interpretability, Captum | Market: $30B by 2032 |
| AI Ethics | 30 | 🟢 | Fairness, accountability, product ethics, NIST frameworks | Regulatory driven |
| AI Security & Privacy | 22 | 🟡 | Red teaming, privacy, purple-teaming, threat modeling | Growing importance |
Total: ~71 resources | Perfect for: Responsible AI & quality focus
Real-world AI in specific fields
| Domain | Resources | Difficulty | Latest Updates | Impact |
|---|---|---|---|---|
| AI for Healthcare | 29 | 🟡 | Medical VLM benchmarks, multimodal imaging, FDA compliance, fairness in diagnostics | Medical diagnosis |
| AI for Finance | 27 | 🟡 | Agentic trading with LLMs, workspace platforms, FLAG-TRADER (ACL 2025), Alpha-GPT 2.0 | Trading, risk |
| Robotics & Embodied AI | 30 | 🟡🔴 | MIT 2025 robot learning, embodied foundation models, aerial robotics | Autonomous systems |
Total: ~113+ resources | Perfect for: Domain specialists
Take models to production
| Category | Resources | Difficulty | Focus |
|---|---|---|---|
| MLOps | 18 | 🟡 | Pipelines, automation, experiment tracking, monitoring |
| AI Tools & Frameworks | 37 | 🟢 | PyTorch, TensorFlow, Claude API, CrewAI, MLflow |
| AI Hardware & Acceleration | 10 | 🟡🔴 | GPU/TPU, CUDA, edge AI |
| AI Security & Privacy | 22 | 🟡 | Red teaming, privacy, threat modeling |
| AI Ethics | 30 | 🟢 | Responsible AI, fairness |
| Edge AI & IoT | 14 | 🟡 | Edge deployment, TinyML |
Total: ~149+ resources | Perfect for: Production engineers
Cross-modal learning and next-generation AI
| Category | Resources | Difficulty | Focus | Latest |
|---|---|---|---|---|
| Multimodal AI | 24 | 🟡🔴 | VLM, cross-modal, vision-language-action | VLM architectures, robotics, medical |
| Spatial Intelligence | 12 | 🔴 | 3D, embodied AI | Emerging |
| World Models | 16 | 🔴 | Simulation, forecasting | Research frontier |
| Quantum AI | 13 | 🔴 | Quantum computing | Early research |
Total: 65 resources | Perfect for: Multimodal & emerging tech specialists
Weeks 1-4: Fundamentals (Stanford CS224N/CS224U, HuggingFace NLP Course, CMU Advanced NLP)
→
Weeks 5-8: Advanced Techniques (Fine-tuning, instruction tuning, RLHF, parameter-efficient methods, RAG)
→
Weeks 9-12: Production Deployment (Agents, prompt engineering, inference optimization, semantic search)
Resources: 62 | Tools: PyTorch, Hugging Face, OpenAI API, LangChain, LangGraph
Final: Production LLM application with fine-tuning, RAG, and deployment
Weeks 1-4: Fundamentals (David Silver course, Hugging Face DRL, Spinning Up basics)
→
Weeks 5-8: Advanced Techniques (Policy gradients, actor-critic, curriculum learning, meta-RL)
→
Weeks 9-12: Application & Research (Robotics, RL agents, cutting-edge 2025 methods)
Resources: 29 | Tools: Gymnasium, Stable Baselines3, PyTorch
Final: Custom RL agent with curriculum learning or robotic application
Weeks 1-3: Classical Methods (ARIMA, statsmodels, Prophet, exponential smoothing)
→
Weeks 4-7: Deep Learning & Foundation Models (LSTM, Transformers, TimeGPT, zero-shot)
→
Weeks 8-10: Advanced Reasoning (LLM temporal reasoning, agentic forecasting, production)
Resources: 26 | Tools: statsmodels, Prophet, PyTorch, TimeGPT
Final: End-to-end time series system with foundation models
Weeks 1-4: Fundamentals (Digital speech processing, Whisper, datasets, basic ASR/TTS)
→
Weeks 5-8: Advanced Systems (Emotional TTS, voice agents, multimodal speech, streaming)
→
Weeks 9-12: Production Voice AI (Real-time deployment, edge optimization, personalization)
Resources: 41 | Tools: Whisper, ChatTTS, ESPnet, PyTorch
Final: Production voice AI system with emotional synthesis and agents
Weeks 1-4: Fundamentals (Vision-Language Models, CLIP, OpenCV courses)
→
Weeks 5-8: Advanced VLMs (LLaVA architecture, medical VLM benchmarks, VLA robotics)
→
Weeks 9-12: Production Systems (Multimodal agents, edge deployment, applications)
Resources: 24 | Tools: PyTorch, Hugging Face, OpenCV
Final: End-to-end multimodal application (image understanding + reasoning + action)
Weeks 1-3: Fundamentals (PyTorch Geometric, UVA tutorial, Distill.pub)
→
Weeks 4-7: Advanced Models (Stanford CS224W, AAAI 2025 tutorial, graphons)
→
Weeks 8-10: Production (Recommendation systems, knowledge graphs, molecular ML)
Resources: 33 | Tools: PyTorch Geometric, DGL, NetworkX
Final: End-to-end graph ML application (node classification or recommendation system)
Weeks 1-4: Fundamentals (ROS2 tutorials, LeRobot course, Articulated Robotics)
→
Weeks 5-8: Advanced Techniques (MIT modern robot learning, imitation learning, RL)
→
Weeks 9-12: Real Robots (Sim-to-real transfer, embodied foundation models, deployment)
Resources: 30 | Tools: ROS2, MuJoCo, LeRobot, PyTorch
Final: Fully functional robot learning system (simulation + real robot optional)
Weeks 1-2: Fundamentals (Leaderboards, benchmarks, evaluation metrics)
→
Weeks 3-5: Advanced Frameworks (DeepEval, Ragas, Promptfoo, testing pipelines)
→
Weeks 6-8: Production Systems (Safety evaluation, continuous testing, compliance)
Resources: 10 | Tools: DeepEval, Ragas, Promptfoo, HELM
Final: Comprehensive AI evaluation & continuous quality monitoring system
Weeks 1-3: Fundamentals (MLOps Zoomcamp, Databricks, Google Cloud MLOps guide)
→
Weeks 4-7: Advanced Techniques (Model serving, monitoring, CI/CD for ML, Kubernetes)
→
Weeks 8-10: Production Systems (MadeWithML, deployment at scale, automation)
Resources: 18 | Tools: MLflow, Kubernetes, Prefect, DVC
Final: End-to-end ML production pipeline with monitoring & automation
Weeks 1-3: Fundamentals (AI Security basics, adversarial ML, HackTheBox intro)
→
Weeks 4-7: Advanced Red Teaming (Microsoft PyRIT, Stanford CS330i, real-world scenarios)
→
Weeks 8-10: Production Security (Google SAIF framework, threat modeling, compliance)
Resources: 22 | Tools: PyRIT, HarmBench, TensorFlow, ART
Final: Comprehensive AI security assessment & red-teaming framework
Weeks 1-2: Mathematics (Khan Academy Linear Algebra, 3Blue1Brown visualizations, MIT 18.05)
→
Weeks 3-4: ML Fundamentals (Andrew Ng course, StatQuest, Kaggle intro)
→
Weeks 5-6: Data Science (Python for Data Science Handbook, Pandas tutorials, EDA)
Resources: 67 (Math 38 + ML 18 + DS 11) | Tools: Python, Pandas, NumPy, Matplotlib
Final: Complete beginner portfolio with math foundations, ML algorithms, data analysis
We welcome contributions! Adding resources is easy:
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- [Resource Name](URL) - Description | Difficulty | Duration
- Submit a pull request
✅ 100% free (no paywalls) ✅ Reputable source ✅ Active/maintained ✅ Relevant to AI/ML ✅ Difficulty tagged ✅ Globally accessible
✅ Completely free - No payment required ever ✅ Globally accessible - Available worldwide ✅ University courses - MIT, Stanford, Harvard (FREE) ✅ Open datasets - Research datasets ✅ Open-source tools - Free software ✅ Academic papers - arXiv pre-prints ✅ YouTube educational - Official verified channels
❌ Pirated content ❌ Paywalled resources ❌ Subscription-required ❌ Authentication walls
MIT License - see LICENSE for details
- 📃 Issues: Report bugs
- 🗣️ Discussions: Community Q&A
- 🔧 Contribute: Add resources
- 🌐 GitHub: FREE-AI-RESOURCES
- Last Updated: January 22, 2026, 10:40 PM UTC+4
- Active Maintenance: ✅ Yes
- Update Frequency: Multiple times daily
- Growth Rate: +6 resources (Thu Jan 22 - Prompt Eng, Agents, Advanced NLP), +8 resources (Wed Jan 21 - Core courses), +12 resources (Tue Jan 20 - Bi-weekly refresh), +17 resources (Sun Jan 19 - Evals NEW), +14 resources (Sat Jan 17), +15 resources (Fri Jan 16), +10 resources (Wed Jan 15), +4 resources (Tue Jan 14), +7 resources (Mon Jan 13), +12 resources (Sun Jan 12)
- 11-Day Growth: 555 → 647 = +92 resources 🚀
- Path to 650+: January 23, 2026 (expected)
- 2026 Growth: 647+ resources (Jan 22), 32 categories, 98%+ 2025-2026 content
Status: ✅ Active & Growing Goal: #1 free AI/ML resource repository on GitHub Mission: Democratizing AI education globally
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