Free AI/ML Curriculum · Zero → Production

Master the fundamentals. build real systems. ship with confidence.
University-grade depth. industry-grade pragmatism. 100% free.

Foundations to MLOps. math to LLMs. code to production. no fluff.

Overview • How to use • Contributing • License

What is this

A living. community-maintained index of high-quality. free AI/ML resources. Optimized for self-study and team upskilling. Every entry links straight to the source with a one-liner so you can decide fast.

Quick start

1) Pick a goal. examples. learn the math basics. ship a PyTorch model. understand LLMs.
2) Jump to the matching section and follow items top to bottom for a solid path.
3) Build one small thing per week. even a notebook counts.

Curation principles

Free to access. no paywalls or trials. Preference for official docs. books. and enduring course sites. Practical order of study. minimal duplication. clear one-line summaries. Periodic refreshes as materials evolve.

_{Pro tip. Use your browser Find to jump to keywords like "pandas" or "transformers".}

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1) Mathematics & Probability for ML (Foundations)

1. MIT OCW 18.06 Linear Algebra — Prof. Gilbert Strang’s classic linear algebra course with full videos and notes. https://ocw.mit.edu/courses/18-06-linear-algebra-spring-2010/ ([MIT OpenCourseWare][1])
2. MIT OCW 18.06SC Linear Algebra (OCW Scholar) — Self-study edition with structured materials and problem sets. https://ocw.mit.edu/courses/18-06sc-linear-algebra-fall-2011/ ([MIT OpenCourseWare][2])
3. 3Blue1Brown: Essence of Linear Algebra — Visual intuition for vectors, matrices, transformations. https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab ([YouTube][3])
4. Convex Optimization (Boyd & Vandenberghe) [Book] — Definitive text with free PDF. https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf ([seas.ucla.edu][4])
5. The Matrix Cookbook — Handy formulas for matrix calculus and identities (free PDF). https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf ([math.uwaterloo.ca][5])
6. MIT OCW 6.041/6.041SC Probabilistic Systems Analysis — Probability theory with applications. https://ocw.mit.edu/courses/6-041sc-probabilistic-systems-analysis-and-applied-probability-fall-2013/ ([MIT OpenCourseWare][6])
7. Harvard Stat 110 (Blitzstein) — Probability course site with materials + problem sets. https://projects.iq.harvard.edu/stat110/home ([stat110.hsites.harvard.edu][7])
8. Harvard Stat 110 Full Lectures (YouTube) — Complete lecture playlist. https://www.youtube.com/playlist?list=PL2SOU6wwxB0uwwH80KTQ6ht66KWxbzTIo ([YouTube][8])
9. MIT OCW 18.05 Introduction to Probability & Statistics — Notes, assignments, exams. https://ocw.mit.edu/courses/18-05-introduction-to-probability-and-statistics-spring-2014/ ([MIT OpenCourseWare][9])
10. MIT OCW 18.650 Statistics for Applications — Mathematical statistics with real-world problems. https://ocw.mit.edu/courses/18-650-statistics-for-applications-fall-2016/ ([Stanford University][10])
11. ISLR — An Introduction to Statistical Learning [Book + Labs] — Free book site with labs. https://trevorhastie.github.io/ISLR/ ([trevorhastie.github.io][11])
12. Statistical Learning (Hastie & Tibshirani) — Course Videos — Companion MOOC playlist. https://www.youtube.com/playlist?list=PLoROMvodv4rOzrYsAxzQyHb8n_RWNuS1e ([YouTube][12])
13. LAFF: Linear Algebra — Foundations to Frontiers — Rigorous LA course (free-audit). https://www.mooc-list.com/course/linear-algebra-foundations-frontiers-edx ([edx.org][13])
14. Stanford “Introduction to Statistics” (online) — Practical probability & stats. https://online.stanford.edu/courses/xfds110-introduction-statistics ([online.stanford.edu][14])
15. Coursera: Mathematics for Machine Learning (Imperial) — LA, multivariate calc, PCA (audit free). https://www.coursera.org/specializations/mathematics-machine-learning ([Coursera][15])
16. Foundations of Data Science (Blum–Hopcroft–Kannan) [Book] — Theory-leaning, free PDF. https://home.ttic.edu/~avrim/book.html ([home.ttic.edu][16])
17. A Course in Machine Learning (Hal Daumé III) [Book] — Gentle theory intro, free PDF. http://ciml.info/ ([ciml.info][17])
18. Stanford Probability Cheatsheet — Two-page probability reference. https://stanford.edu/~shervine/teaching/cme-106/cheatsheet-probability ([Stanford University][18])
19. The Matrix Calculus You Need for Deep Learning — Practical matrix calculus notes. https://explained.ai/matrix-calculus/ ([explained.ai][19])
20. MIT OCW 18.01 Single Variable Calculus — Full calc I materials. https://ocw.mit.edu/courses/18-01-single-variable-calculus-fall-2006/ ([MIT OpenCourseWare][20])
21. The Elements of Statistical Learning (ESL) [Book] — Classic statistical ML text, free online. https://hastie.su.domains/ElemStatLearn/ ([hastie.su.domains][21])

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2) Python, Data Handling & Tools (NumPy/Pandas/Sklearn/Notebooks)

1. Harvard CS50P — Intro to Programming with Python — Full open course site. https://cs50.harvard.edu/python/courses/ ([edX][22])
2. MIT OCW 6.0001 — Intro to CS & Programming (Python) — Lectures, notes, assignments. https://ocw.mit.edu/courses/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/ ([MIT OpenCourseWare][23])
3. Python for Everybody (U-Michigan, Coursera) — Beginner-friendly Python (audit free). https://www.coursera.org/specializations/python ([Coursera][24])
4. Google’s Python Class — Free, self-paced Python course with exercises. https://developers.google.com/edu/python ([Google for Developers][25])
5. Kaggle Learn: Python — Short. hands-on Python micro-course in the browser. https://www.kaggle.com/learn/python ([kaggle.com][26])
6. NumPy User Guide — Official docs & tutorials. https://numpy.org/doc/stable/user/ ([numpy.org][27])
7. NumPy User Guide (PDF) — Offline reference. https://numpy.org/doc/stable/numpy-user.pdf ([numpy.org][28])
8. pandas User Guide — Official documentation. https://pandas.pydata.org/pandas-docs/stable/user_guide/index.html ([pandas.pydata.org][29])
9. 10 Minutes to pandas — Quick intro to core operations. https://pandas.pydata.org/docs/user_guide/10min.html ([pandas.pydata.org][30])
10. scikit-learn User Guide — End-to-end ML in Python. https://scikit-learn.org/stable/user_guide.html ([scikit-learn.org][31])
11. Scikit-learn: Getting Started — First steps and examples. https://scikit-learn.org/stable/getting_started.html ([scikit-learn.org][32])
12. scikit-learn MOOC (Inria) — Free, self-paced ML with sklearn. https://inria.github.io/scikit-learn-mooc/ ([inria.github.io][33])
13. SciPy Documentation — Scientific computing toolkit reference. https://docs.scipy.org/doc/ ([docs.scipy.org][34])
14. Matplotlib Tutorials (Official) — Plotting from basics to advanced. https://matplotlib.org/stable/tutorials/index.html ([matplotlib.org][35])
15. Project Jupyter Docs — Notebooks/JupyterLab docs and install. https://docs.jupyter.org/ ([docs.jupyter.org][36])
16. Jupyter: Running the Notebook — How to launch/use notebooks locally. https://docs.jupyter.org/en/latest/running.html ([docs.jupyter.org][37])
17. Google Colab (Home) — Free hosted notebooks with GPU/TPU access. https://colab.google.com/ ([colab.research.google.com][38])
18. Colab Overview (colab.google) — What Colab is and how to start. https://colab.google/ ([colab.google][39])
19. Kaggle Learn: Data Cleaning — Practical data prep exercises. https://www.kaggle.com/learn/data-cleaning ([kaggle.com][26])
20. Kaggle Learn: Pandas — Hands-on pandas lessons. https://www.kaggle.com/learn/pandas ([kaggle.com][26])
21. Scikit-learn MOOC (GitHub) — All notebooks and exercises. https://github.com/INRIA/scikit-learn-mooc ([GitHub][40])

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3) Core Machine Learning (Classical ML)

1. Andrew Ng — Machine Learning (Coursera) — Landmark ML MOOC (audit free). https://www.coursera.org/learn/machine-learning ([Coursera][41])
2. Google Machine Learning Crash Course — Interactive lessons + Colab labs. https://developers.google.com/machine-learning/crash-course ([Google for Developers][42])
3. Caltech “Learning From Data” — Course site with lectures. https://work.caltech.edu/telecourse.html ([work.caltech.edu][43])
4. Stanford CS229 — Machine Learning (Notes/Materials) — Classic graduate ML. https://cs229.stanford.edu/ ([cs229.stanford.edu][44])
5. CMU 10-601 — Intro to ML — Full materials and schedule (open). https://www.cs.cmu.edu/~10601/ ([cs.cmu.edu][45])
6. UC Berkeley CS189/289A — Open course resources (assignments/slides). https://people.eecs.berkeley.edu/~jrs/189/ ([CS 189/289A][46])
7. Cornell CS4780/5780 — ML — Course page with notes and resources. https://www.cs.cornell.edu/courses/cs4780/2023fa/ ([cs.cornell.edu][47])
8. University of Washington CSE546 (ML) — Grad-level ML with slides/notes. https://courses.cs.washington.edu/courses/cse546/ ([courses.cs.washington.edu][48])
9. MITx: Machine Learning with Python (edX) — From linear models to RL. https://www.edx.org/learn/machine-learning/massachusetts-institute-of-technology-machine-learning-with-python-from-linear-models-to-deep-learning ([edx.org][49])
10. IBM: Machine Learning with Python (edX) — Practical ML with Python. https://www.edx.org/learn/machine-learning/ibm-machine-learning-with-python-a-practical-introduction ([edx.org][50])
11. NPTEL: Introduction to Machine Learning (IIT) — Free Indian MOOC + YouTube lectures. https://onlinecourses.nptel.ac.in/noc21_cs85/preview • https://www.youtube.com/playlist?list=PLkFkyjewmXXOeGvzBjH2dlG57KmXA6qVo ([NPTEL Online Courses][51])
12. Elements of Statistical Learning (ESL) [Book] — Statistical ML classic (free). https://hastie.su.domains/ElemStatLearn/ ([hastie.su.domains][21])
13. Understanding Machine Learning (Shalev-Shwartz & Ben-David) [Book] — Theory focused, free PDF. https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/ ([archive.org][52])
14. A Course in Machine Learning (Daumé III) [Book] — ML fundamentals, free. http://ciml.info/ ([ciml.info][17])
15. ISLR — Intro to Statistical Learning [Book] — Applied ML with labs, free. https://trevorhastie.github.io/ISLR/ ([trevorhastie.github.io][11])
16. Statistical Learning — edX (Hastie/Tibshirani) — Free companion MOOC. https://www.edx.org/course/statistical-learning ([edx.org][53])
17. University of Washington ML Specialization (Coursera) — Foundations, regression, classification, clustering (audit free). https://www.coursera.org/specializations/machine-learning ([Coursera][54])
18. Microsoft Learn — ML Fundamentals — Concept modules with quizzes. https://learn.microsoft.com/en-us/training/modules/fundamentals-machine-learning/ ([Microsoft Learn][55])
19. Intro to Azure ML (Microsoft Learn) — Hands-on cloud ML basics. https://learn.microsoft.com/en-us/training/modules/intro-to-azure-ml/ ([Microsoft Learn][56])
20. Azure ML in a Day (Tutorial) — Train, register, deploy a model. https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-azure-ml-in-a-day ([Microsoft Learn][57])

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4) Deep Learning & Generative AI (Courses, Books, Framework Tutorials)

1. fast.ai — Practical Deep Learning for Coders — Hands-on DL course, free. https://course.fast.ai/ ([Practical Deep Learning for Coders][58])
2. MIT 6.S191 — Introduction to Deep Learning (OCW) — Lectures + labs. https://ocw.mit.edu/courses/6-s191-introduction-to-deep-learning-january-iap-2020/ ([MIT OpenCourseWare][59])
3. MIT 6.S191 — Course Site — Latest materials and project info. https://introtodeeplearning.com/ ([MIT Deep Learning 6.S191][60])
4. Stanford CS231n — Deep Learning for Computer Vision (site) — Syllabus, slides, assignments. https://cs231n.stanford.edu/2023/index.html ([cs231n.stanford.edu][61])
5. CS231n Notes — Accompanying lecture notes and guides. https://cs231n.github.io/ ([cs231n.github.io][62])
6. Stanford CS224n — NLP with Deep Learning (site) — Materials + assignments. https://web.stanford.edu/class/cs224n/ ([Stanford University][63])
7. CS224n — 2024 Lecture Playlist — Complete lectures on YouTube. https://www.youtube.com/playlist?list=PLoROMvodv4rOaMFbaqxPDoLWjDaRAdP9D ([YouTube][64])
8. Deep Learning (Goodfellow–Bengio–Courville) [Book] — Free online textbook. https://www.deeplearningbook.org/ ([deeplearningbook.org][65])
9. Dive into Deep Learning (D2L) [Interactive Book] — Open-source book with runnable notebooks. https://d2l.ai/ ([d2l.ai][66])
10. fast.ai — fastbook (2e) [Book] — Free online book paired with the course. https://fastai.github.io/fastbook2e/ ([fastai.github.io][67])
11. PyTorch Tutorials (Official) — Beginner → advanced DL tutorials. https://docs.pytorch.org/tutorials/ ([docs.pytorch.org][68])
12. TensorFlow Tutorials (Official) — End-to-end guides and Colab notebooks. https://www.tensorflow.org/tutorials ([TensorFlow][69])
13. Keras Developer Guides (Official) — Practical deep dives + Colab-ready notebooks. https://keras.io/guides/ ([keras.io][70])
14. Hugging Face — LLM Course — Build and deploy LLMs with 🤗 Transformers. https://huggingface.co/learn/llm-course/chapter1/1 ([huggingface.co][71])
15. Hugging Face — NLP Course — Modern NLP with Transformers (free, no ads). https://huggingface.co/learn/nlp-course/en/chapter1 ([huggingface.co][72])
16. Stanford CS25 — Transformers United (site) — Transformer systems seminar. https://web.stanford.edu/class/cs25/ ([Stanford University][73])
17. CS25 — Transformers United (YouTube) — Guest lectures from top researchers. https://www.youtube.com/playlist?list=PLoROMvodv4rNiJRchCzutFw5ItR_Z27CM ([YouTube][74])
18. Karpathy — Neural Networks: Zero to Hero — From-scratch DL/LLMs playlist. https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ ([YouTube][75])
19. LangChain (Open-Source) — Quickstart — Build LLM-powered apps & agents. https://docs.langchain.com/oss/python/langchain/quickstart ([docs.langchain.com][76])
20. DeepLearning.AI — Generative AI with LLMs (AWS) [Short Course] — Practical LLM apps (audit free). https://www.coursera.org/learn/generative-ai-with-llms ([Coursera][77])
21. PyTorch — 60-Minute Blitz — Fast intro to tensors, autograd, nn, and training loops. https://docs.pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html ([docs.pytorch.org][78])
22. TensorFlow — Quickstart for Beginners — Train a simple model end-to-end in Colab. https://www.tensorflow.org/tutorials/quickstart/beginner ([TensorFlow][79])

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5) Natural Language Processing (NLP)

1. Stanford CS224N: NLP with Deep Learning (course site) — Full graduate course with slides, assignments, and recommended readings. ([Stanford University][1])
2. Stanford CS224N (YouTube playlist) — Recorded lectures for the 2021 offering. ([YouTube][2])
3. Hugging Face — LLM/NLP Course — Hands-on intro to Transformers, Datasets, Tokenizers, and Accelerate, with code notebooks. ([huggingface.co][3])
4. Hugging Face — Learn hub — Central index of free HF courses, including NLP, CV, Audio, Agents, and more. ([huggingface.co][4])
5. Advanced NLP with spaCy (interactive course) — Practical pipelines, training, and production patterns using spaCy. ([Advanced NLP with spaCy][5])
6. Speech and Language Processing (Jurafsky & Martin, 3rd ed. draft) — Definitive, freely available modern NLP textbook manuscript. ([Stanford University][6])
7. NLTK Book (online) — Classic, beginner-friendly text with code using the Natural Language Toolkit. ([nltk.org][7])
8. fast.ai “A Code-First Introduction to NLP” (overview) — Practical, top-down NLP course emphasizing real projects. ([fast.ai][8])
9. fast.ai NLP — GitHub repo (notebooks & materials) — All notebooks and resources for the fast.ai NLP course. ([GitHub][9])
10. fast.ai NLP (YouTube playlist) — Complete lecture series videos. ([YouTube][10])
11. Oxford Deep NLP 2017 (YouTube) — Advanced lectures (Blunsom/DeepMind) on deep learning for NLP. ([YouTube][11])
12. Oxford Deep NLP 2017 — Slides & materials — Lecture PDFs and practicals to accompany the videos. ([GitHub][12])
13. Stanford CS224U: Natural Language Understanding (course site) — Project-oriented NLU course with slides, videos, and notebooks. ([Stanford University][13])
14. AllenNLP — Official Guide — Step-by-step tutorials for building NLP models with AllenNLP (on PyTorch). ([guide.allennlp.org][14])
15. AllenNLP Guide — “Your first model” — Quickstart text-classification walkthrough. ([guide.allennlp.org][15])
16. Hugging Face — LLMs & NLP primer — Conceptual intro to LLMs in modern NLP workflows. ([huggingface.co][16])
17. CS224N archived pages (historical materials) — Earlier offering with downloadable slides/notes. ([Stanford University][17])
18. CS224N course listing (Stanford Online) — Official information, links out to materials and recordings. ([online.stanford.edu][18])
19. NLTK project homepage — Library, corpora, and docs for classical NLP tasks. ([nltk.org][19])
20. CS224U background & resources — Curated refreshers and pointers for NLU topics. ([Stanford University][20])

6) Computer Vision (CV)

1. Stanford CS231n (course hub) — Deep learning for visual recognition, with notes, assignments, and references. ([cs231n.stanford.edu][21])
2. CS231n 2017 site (archived) — Full set of historical slides, assignments, and project guidance. ([cs231n.stanford.edu][22])
3. CS231n notes — Convolutional Networks — Detailed, math-light explanations of CNN building blocks. ([cs231n.github.io][23])
4. UC Berkeley CS280 (Spring 2025) — Graduate CV course with lecture schedule and posted slides. ([cs280-berkeley.github.io][24])
5. UC Berkeley CS280 (Spring 2024 site) — Prior term with slide links and reading list. ([cs280-berkeley.github.io][25])
6. OpenCV — Official Tutorials — End-to-end tutorials for core, imgproc, dnn, calib3d, etc. ([docs.opencv.org][26])
7. OpenCV modules overview — Map of all OpenCV modules and user guides. ([docs.opencv.org][27])
8. OpenCV-Python Tutorials (ReadTheDocs) — Python-focused, hands-on OpenCV lessons. ([opencv24-python-tutorials.readthedocs.io][28])
9. OpenCV-Python “Introduction” tutorial — Quickstart on installation and basic image ops. ([docs.opencv.org][29])
10. PyTorch — Transfer Learning for CV (official tutorial) — Fine-tune pretrained CNNs for new tasks. ([docs.pytorch.org][30])
11. PyTorch — TorchVision Object Detection Finetuning — Train Mask R-CNN on a small dataset. ([docs.pytorch.org][31])
12. Detectron2 (GitHub) — SOTA object detection/segmentation library by FAIR with model zoo. ([GitHub][32])
13. MMDetection (ReadTheDocs) — OpenMMLab’s modular detection toolbox with extensive guides. ([mmdet.readthedocs.io][33])
14. MMDetection (GitHub) — Codebase, configs, and model zoo for many detectors/segmenters. ([GitHub][34])
15. Ultralytics YOLOv5 Docs — Production-ready detection with tutorials and examples. ([docs.ultralytics.com][35])
16. Ultralytics YOLOv5 (GitHub) — Repo with training, export, and deployment recipes. ([GitHub][36])
17. CS231n materials mirror (Archive.org) — Backup of course content/videos for offline access. ([archive.org][37])
18. Berkeley CS280 — Catalog entry — Official description and topics covered. ([www2.eecs.berkeley.edu][38])
19. CS280 — Current class listing — Current term logistics and pointers. ([classes.berkeley.edu][39])
20. Deep Learning for Computer Vision (YT playlist) — Additional lecture series on modern DL-for-CV methods. ([YouTube][40])

7) Reinforcement Learning (RL)

1. Sutton & Barto — Reinforcement Learning: An Introduction (2nd ed.) — Author page with links to the freely available text. ([incompleteideas.net][41])
2. RL Textbook (PDF mirror) — Downloadable PDF of the book. ([Stanford University][42])
3. OpenAI — Spinning Up in Deep RL — Intro tutorials, key papers, and reference implementations. ([spinningup.openai.com][43])
4. Spinning Up (PDF) — All Spinning Up docs bundled for offline reading. ([spinningup.openai.com][44])
5. David Silver’s RL Course (YouTube playlist) — Classic 10-lecture series from DeepMind/UCL. ([YouTube][45])
6. Stanford CS234 (course site, Winter 2025) — Graduate RL with lecture notes, assignments, and projects. ([Stanford University][46])
7. Stanford CS234 (modules/notes hub) — Organized lecture materials and checkpoints. ([Stanford University][47])
8. UC Berkeley CS285: Deep RL (course hub) — Up-to-date deep RL course with recordings and labs. ([rail.eecs.berkeley.edu][48])
9. CS285 — Official catalog entry — Syllabus, outcomes, and prerequisites. ([www2.eecs.berkeley.edu][49])
10. Spinning Up — Running Experiments — How to launch and monitor RL baselines locally. ([spinningup.openai.com][50])
11. David Silver RL Course (alternate playlist) — Mirror playlist for redundancy. ([YouTube][51])
12. Stanford CS234 (course listing page) — Archived class with slides and readings. ([Stanford University][52])
13. Stanford Online — CS234 overview — Outline and learning objectives, with links to public materials. ([online.stanford.edu][53])
14. OpenAI Spinning Up — Search hub — Quick access to algorithms, utils, and how-tos. ([spinningup.openai.com][54])
15. RL book (CC-licensed PDF mirror) — Another accessible copy for offline study. ([0-lucas.github.io][55])
16. David Silver — Lecture 1 direct link — Kick-off lecture on fundamentals and MDPs. ([YouTube][56])
17. Berkeley CS285 — Syllabus — Weekly topics, readings, and assignment flow. ([rail.eecs.berkeley.edu][57])
18. DeepMind/UCL RL (playlist mirror by Stanford Online) — Additional hosting of lectures. ([YouTube][58])
19. Spinning Up — Algorithms (PPO, SAC, TD3, etc.) — Friendly explanations plus code to modify. ([spinningup.openai.com][43])
20. Wired news: Sutton & Barto honored (context) — Background on RL’s impact and history. ([WIRED][59])

8) MLOps & Deployment

1. Full Stack Deep Learning (FSDL) — Free course on building, deploying, and monitoring ML systems end-to-end. ([Full Stack Deep Learning][60])
2. DataTalks.Club — MLOps Zoomcamp — Free, cohort-based curriculum with videos, homework, and projects. ([GitHub][61])
3. Made With ML — MLOps course — Practical, tool-agnostic guides for production ML. ([madewithml.com][62])
4. TensorFlow Extended (TFX) — Tutorials — Pipeline components, orchestration, and production patterns. ([TensorFlow][63])
5. TensorFlow Serving — Guide — Serve TF models via REST/gRPC with versioning. ([TensorFlow][64])
6. TorchServe — Docs — Production serving for PyTorch models with handlers and autoscaling. ([docs.pytorch.org][65])
7. ONNX Runtime — Docs — High-performance cross-platform inference for many frameworks. ([onnxruntime.ai][66])
8. NVIDIA Triton Inference Server — Docs — Multi-framework model serving with batching and ensembles. ([NVIDIA Docs][67])
9. MLflow — Documentation — Tracking, model registry, packaging, and deployment. ([mlflow.org][68])
10. MLflow — Getting Started — Quickstart tutorials to track and register models. ([mlflow.org][69])
11. MLflow — Tutorials & Examples — End-to-end examples for real workflows. ([mlflow.org][70])
12. Kubeflow Pipelines — Docs — Containerized ML pipelines on Kubernetes. ([Kubeflow][71])
13. KFP (ReadTheDocs) — SDK usage, concepts, and quickstarts. ([kubeflow-pipelines.readthedocs.io][72])
14. BentoML — Docs — Framework-agnostic serving and packaging of ML/LLM apps. ([BentoML Documentation][73])
15. BentoML — “Hello world” tutorial — Minimal example to deploy a model as an API. ([BentoML Documentation][74])
16. Ray Serve — Docs — Scalable model serving and microservice composition. ([docs.ray.io][75])
17. Seldon — Developer Docs — Kubernetes-native model deployment & monitoring. ([docs.seldon.ai][76])
18. Feast — Feature Store (Docs) — Define, validate, and serve features for online/offline ML. ([docs.feast.dev][77])
19. Evidently — Monitoring (Docs) — Open-source ML/LLM evaluation and production monitoring. ([docs.evidentlyai.com][78])
20. Great Expectations — Data validation (Docs) — Test and monitor data quality for dependable ML pipelines. ([docs.greatexpectations.io][79])
21. DVC — Data Version Control (Docs) — Git-like versioning for datasets, models, and pipelines. ([Data Version Control · DVC][80])
22. ZenML — Getting Started — MLOps framework to build reproducible pipelines across stacks. ([zenml.io][81])

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9) Time Series & Forecasting

1. Forecasting: Principles and Practice (fpp3) — Free online textbook using R and the tidyverts ecosystem.
2. Forecasting: Principles and Practice (fpp2, archive) — Prior edition with ARIMA, ETS, and practical tips.
3. Penn State STAT 510: Time Series Analysis — Open course notes on AR, MA, ARMA, ARIMA, SARIMA.
4. Statsmodels — Time Series Analysis — Python docs for ARIMA, SARIMAX, state space, VAR.
5. Prophet — Official Docs — Additive forecasting model with seasonality and holidays.
6. sktime — User Guide — Unified time-series toolbox for forecasting, classification, and pipelines.
7. Darts — Documentation — Python library with classical and deep models plus backtesting.
8. GluonTS — Docs — Probabilistic forecasting with DeepAR, Transformer, and more.
9. Kaggle Learn — Time Series — Hands-on, browser-based mini-course with exercises.
10. Nixtla — StatsForecast — Fast classical models at scale with AutoARIMA, ETS.
11. Nixtla — NeuralForecast — PyTorch forecasting models such as N-BEATS, TFT.
12. Nixtla — MLForecast — Gradient-boosted and tree-based forecasting templates.
13. pmdarima — Docs — Auto-ARIMA for Python with diagnostics and plotting.
14. fable — R package site — Tidy forecasting models and workflows.
15. tsibble — R package site — Tidy temporal data structures and indexing.
16. UEA & UCR Time Series Classification Repository — Benchmarks and datasets for practice.
17. Kaggle — M5 Forecasting: Accuracy — Large-scale retail time-series challenge to learn from.
18. Twitter AnomalyDetection (archived) — Seasonal hybrid ESD anomaly detection for time series.
19. Coursera — Practical Time Series Analysis (audit free) — Frequency-domain and ARIMA basics with labs.
20. statsmodels — State Space & SARIMAX examples — End-to-end notebooks for seasonal models.

10) Recommender Systems

1. Recommender Systems Specialization — GroupLens (audit free) — User-, item-, and model-based recommenders with projects.
2. GroupLens — Education hub — Open slides, readings, and course materials.
3. Mining of Massive Datasets (MMDS) — Book — Free text covering large-scale recommendation and SVD.
4. Matrix Factorization Techniques for Recommender Systems (Koren et al.) — Accessible survey from the Netflix Prize era.
5. Collaborative Filtering for Implicit Feedback Datasets (Hu, Koren, Volinsky) — ALS for implicit data, classic paper.
6. Microsoft Recommenders — GitHub — End-to-end examples, algorithms, and best practices.
7. TensorFlow Recommenders — Docs — Two-tower retrieval, ranking, and evaluation tutorials.
8. NVIDIA Merlin — Docs — Accelerated ETL, feature stores, training, and inference.
9. Implicit — Docs — Fast implicit MF, BPR, and nearest-neighbors for Python.
10. Surprise (scikit-surprise) — Docs — Easy baselines: SVD, KNN, NMF with cross-validation.
11. LightFM — Docs — Hybrid MF with content and collaborative signals.
12. RecBole — Docs — Unified framework supporting 90+ algorithms and benchmarks.
13. Cornac — Docs — Multimodal recommenders with evaluation utilities.
14. Spotlight — GitHub — PyTorch repo for MF, sequence models, and BPR.
15. Annoy — GitHub — Approximate nearest neighbors for candidate retrieval.
16. FAISS — Docs — Vector search library for large-scale similarity.
17. Milvus — Docs — Open-source vector database for recommendation pipelines.
18. Weaviate — Docs — Vector DB with hybrid search and modules.
19. HNSWLIB — GitHub — Efficient HNSW approximate nearest neighbors.
20. Google ScaNN — GitHub — High-recall vector search for retrieval-tier recommenders.

11) Bayesian Methods & Probabilistic Programming

1. Bayesian Methods for Hackers — Free, code-first PyMC3 book with notebooks.
2. Think Bayes 2e — Online book — Gentle Bayesian reasoning with Python.
3. Statistical Rethinking — 2023 lectures — Full video course by Richard McElreath.
4. PyMC — Docs — Modern PyMC with examples, samplers, and best practices.
5. PyMC Examples — Curated gallery of applied Bayesian models.
6. Stan — User’s Guide — Modeling language docs with primers and references.
7. Stan — Case Studies — Real-world modeling walk-throughs.
8. Pyro — Tutorials — Probabilistic programming on PyTorch with SVI and MCMC.
9. NumPyro — Docs — JAX-based PPL with fast HMC/NUTS and SVI.
10. TensorFlow Probability — Docs — Distributions, bijectors, variational inference.
11. Bayes Rules! — Online book — Applied Bayesian modeling with case studies.
12. Aki Vehtari — Bayesian Data Analysis course — Slides, labs, and R/Python examples.
13. Michael Betancourt — HMC concept tutorial — Deep conceptual intro to HMC/NUTS.
14. Introduction to MCMC for Machine Learning (Andrieu et al.) — Classic tutorial survey on MCMC methods.
15. Probabilistic Programming with PyMC — Book code — Open examples and notebooks.
16. StanCon talks & materials — Videos and slides across applied domains.
17. Pyro — SVI tutorials — Step-by-step variational inference guides.
18. Bayesian Workflow resources — Essays on diagnostics, priors, and modeling practice.
19. brms — Docs — High-level Bayesian modeling in R using Stan.
20. Probabilistic Programming Papers collection — Conference videos and materials from PPL community.

12) Graph ML & Network Science

1. Stanford CS224W — Machine Learning with Graphs — Course site with slides, notes, and assignments.
2. CS224W — YouTube playlist — Full lecture recordings.
3. Geometric Deep Learning — Book/notes — Unifying perspective on grids, groups, graphs, gauges.
4. PyTorch Geometric — Docs — Popular GNN library with tutorials and examples.
5. DGL — Docs — Deep Graph Library with multi-framework support.
6. NetworkX — Docs — Graph algorithms and analysis in Python.
7. Open Graph Benchmark (OGB) — Standard datasets and leaderboards for GNNs.
8. SNAP — Stanford Large Network Dataset Collection — Real networks for experimentation.
9. Graph Convolutional Networks (Kipf & Welling) — Seminal GCN paper.
10. GraphSAGE (Hamilton et al.) — Inductive representation learning on large graphs.
11. Graph Attention Networks (Veličković et al.) — Attention mechanism for GNNs.
12. StellarGraph — Docs — GNN library focused on applied examples.
13. Spektral — Docs — Keras-based GNN library with layers and datasets.
14. PyG — GraphGym — Configuration-driven GNN experimentation.
15. DGL — Tutorials — Hands-on node/edge/graph tasks.
16. Geometric Deep Learning — Lecture videos — Complete series from the authors.
17. Network Science — Barabási book — Free textbook on complex networks.
18. Awesome Graph Classification — Curated list of datasets, papers, and code.
19. OGB — Examples repo — Baselines and starter code for benchmarks.
20. PyG — Tutorials index — End-to-end tutorials for building GNNs.

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Thanks

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