Awesome AI Learn World

A curated catalog of AI learning resources — courses and the papers, books and docs that go with them — grouped by skill.

Every resource here meets the inclusion criteria: official source, a verifiable author, free or free to audit, still relevant, and teaching concepts rather than button clicks. The same catalog powers AI Learn World, where you can drop any of these onto your own learning board and track your progress.

Contents

• Learning Paths
• AI Foundations
• Machine Learning
• Deep Learning
• Computer Vision
• NLP
• LLMs
• Generative AI
• AI Agents
• Reinforcement Learning
• MLOps
• ML Systems
• Math
• Programming
• Computer Systems
• Databases
• Backend
• DevOps
• Data Engineering
• Technical Writing
• Staying Current

Learning Paths

Ready-made routes through the catalog, stage by stage. On AI Learn World you can start your board from one of them.

Hello AI World

The whole map in one board: programming and math, machine learning, deep learning, then large language models, ML systems or computer vision — and interview prep at the end. Pick the branch that matches the job you want.

Programming

1. Programming — You can write, test and debug a Python program on your own.
   • CS50's Introduction to Programming with Python
2. Data Structures & Algorithms — You can implement common data structures and algorithms, analyze their running time, and choose the right one for a problem.
   • 6.006 Introduction to Algorithms
3. Python for Data — You can work with NumPy arrays, and load, clean, explore and plot a dataset with pandas in a notebook.
   • Python Programming for Economics and Finance
   • Kaggle Learn: Pandas

Math

1. Calculus — You can take derivatives and partial derivatives, compute a gradient, and apply the chain rule.
   • Precalculus
   • Differential Calculus
   • Multivariable calculus
2. Linear Algebra — You can multiply matrices and explain, geometrically, what a matrix does to a vector.
   • Linear algebra
   • Essence of Linear Algebra
3. Probability & Statistics — You can work with probability, random variables and distributions, and test a hypothesis.
   • Statistics and probability

Machine Learning

1. Core Machine Learning — You can train, evaluate and compare regression and classification models.
   • Machine Learning Crash Course

Deep Learning

1. Neural Networks — You can implement backpropagation from scratch and build a small GPT.
   • Neural Networks
   • Neural Networks: Zero to Hero
2. Practice — You can train an image or text model in PyTorch and explain each step.
   • Practical Deep Learning for Coders
   • Dive into Deep Learning
3. Theory — You can explain why deep networks train: optimization, initialization, regularization and normalization.
   • Understanding Deep Learning
4. Attention — You can explain how attention works and why Transformers replaced recurrent networks.
   • The Illustrated Transformer
5. Generative Models — You can explain how a diffusion model turns noise into an image, and implement one.
   • Practical Deep Learning for Coders part 2: Deep Learning Foundations to Stable Diffusion

Large Language Models

1. Transformers & NLP — You can explain attention and implement a Transformer-based NLP model.
   • CS224N: Natural Language Processing with Deep Learning
2. LLM Training — You can explain pretraining, fine-tuning and LoRA, and fine-tune a small model.
   • Hugging Face LLM Course
3. RAG & Evaluation — You can build a retrieval-augmented app end to end, compare at least two retrieval approaches, and write up where its answers fail.
   • LLM Zoomcamp
4. Agents — You can build an agent that uses tools, and evaluate it.
   • Hugging Face AI Agents Course

ML Systems

1. MLOps — You can deploy a model, track experiments and monitor it in production.
   • MLOps Zoomcamp
2. ML System Design — You can design an ML system end to end and explain its trade-offs.
   • CMU 17-445/645 — Machine Learning in Production
3. Training at Scale — You can explain mixed precision, data and model parallelism, and quantization, and when each one helps.
   • The Ultra-Scale Playbook: Training LLMs on GPU Clusters
4. Portfolio Project — You can take one model from raw data to a deployed, tested service, show where it fails, and explain your trade-offs.
   • Made With ML — MLOps

Computer Vision

1. Classical CV — You can explain image formation, filtering, edges, features, camera calibration and stereo.
   • First Principles of Computer Vision
   • CS131: Computer Vision: Foundations and Applications
2. CNNs — You can explain how a CNN sees an image and why residual connections help.
   • CS231n: Deep Learning for Computer Vision
3. Vision Transformers — You can explain how ViT and CLIP apply attention to images.
   • An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
4. Portfolio Project — You can fine-tune a detector on your own dataset, evaluate it per class with mAP, show where it fails, and deploy it behind an API.
   • Fine-Tuning Object Detection Model on a Custom Dataset, Deployment in Spaces, and Gradio API Integration

Interview Prep

1. Coding & Behavioral — You can solve timed coding problems on data structures and algorithms. You have 3–5 STAR stories ready that show what you did on a team, the result in numbers, and what you learned.
   • Tech Interview Handbook
2. ML & DL Questions — You can answer machine learning and deep learning knowledge and system design questions under time pressure.
   • Introduction to Machine Learning Interviews Book
   • Machine Learning Q and AI
   • Deep Learning Interviews

AI Engineer

How large language models work, and how to build, evaluate and debug systems on top of them. Start after Foundations.

1. Transformers & NLP — You can explain attention and implement a Transformer-based NLP model.
   • CS224N: Natural Language Processing with Deep Learning
   • Go deeper: The Illustrated Transformer · CME 295 - Transformers & Large Language Models (Autumn 2025) · Attention Is All You Need · CMU 11-711 — Advanced Natural Language Processing · The Annotated Transformer · NLP Course | For You
2. LLM Training & Fine-tuning — You can explain pretraining, fine-tuning and LoRA, and fine-tune a small model.
   • Hugging Face LLM Course
   • Go deeper: CS336: Language Modeling from Scratch · LoRA: Low-Rank Adaptation of Large Language Models · Hugging Face smol-course · Build a Large Language Model (From Scratch) · Reinforcement Learning from Human Feedback and LLM Post-Training · The Ultra-Scale Playbook: Training LLMs on GPU Clusters · Training language models to follow instructions with human feedback · ·

Computer Vision Engineer

From image formation to CNNs, vision transformers and generative models. Start after Foundations.

1. Deep Learning — You can train an image or text model in PyTorch, explain each step, and explain how attention works.
   • Practical Deep Learning for Coders
   • The Illustrated Transformer
   • Go deeper: PyTorch Tutorials · MIT 6.S191 — Introduction to Deep Learning · UvA Deep Learning Tutorials
2. Classical Computer Vision — You can explain image formation, edges, features, camera calibration and stereo.
   • First Principles of Computer Vision
   • CS131: Computer Vision: Foundations and Applications
   • Go deeper: CS180/280A: Intro to Computer Vision and Computational Photography · Computer Vision: Algorithms and Applications, 2nd ed. · Foundations of Computer Vision
3. CNNs for Vision — You can explain how a CNN sees an image and why residual connections help.
   • CS231n: Deep Learning for Computer Vision
   • Go deeper: Community Computer Vision Course · Deep Residual Learning for Image Recognition

Deep Learning Engineer

Design, train and scale neural networks, and know what happens underneath the framework. Start after Foundations.

1. Deep Learning — You can train an image or text model in PyTorch, explain each step, and explain how attention works.
   • Practical Deep Learning for Coders
   • The Illustrated Transformer
   • Go deeper: MIT 6.S191 — Introduction to Deep Learning · AI Engineering from Scratch
2. Deep Learning in Depth — You can derive and implement optimization, regularization and normalization for deep networks.
   • Dive into Deep Learning
   • Understanding Deep Learning
   • Go deeper: CMU 11-785 — Introduction to Deep Learning · Deep Learning: Foundations and Concepts · Deep Learning
3. Architectures — You can explain and implement CNNs and Transformers, and say when to use each.
   • CS231n: Deep Learning for Computer Vision
   • CS224N: Natural Language Processing with Deep Learning
   • Go deeper: Deep Residual Learning for Image Recognition · Attention Is All You Need
4. Training & Efficiency — You can explain mixed precision, data and model parallelism, and quantization, and when each one helps.

Foundations

Programming, math, machine learning and neural networks — the shared base for the ML Engineer, AI Engineer and Computer Vision Engineer paths.

1. Programming — You can write, test and debug a Python program on your own.
   • CS50's Introduction to Programming with Python
   • Go deeper: CS50's Introduction to Computer Science · 6.100L Introduction to CS and Programming using Python · The Missing Semester of Your CS Education · Think Python · Python for Everybody
2. Data Structures & Algorithms — You can implement common data structures and algorithms, analyze their running time, and choose the right one for a problem.
   • 6.006 Introduction to Algorithms
   • Go deeper: Algorithms, Part I
3. Python for Data — You can work with NumPy arrays, and load, clean, explore and plot a dataset with pandas in a notebook.
   • Python Programming for Economics and Finance
   • Kaggle Learn: Pandas
   • Go deeper: Elements of Data Science · Kaggle Learn: Python · Kaggle Learn: Data Visualization · · · ·

ML Engineer

Train, ship and run models in production, and prepare for ML engineering interviews. Start after Foundations.

1. Deep Learning — You can train an image or text model in PyTorch, explain each step, and explain how attention works.
   • Practical Deep Learning for Coders
   • The Illustrated Transformer
   • Go deeper: PyTorch Tutorials · Dive into Deep Learning · MIT 6.S191 — Introduction to Deep Learning · Deep Learning Specialization · Understanding Deep Learning · UvA Deep Learning Tutorials · Deep Learning: Foundations and Concepts
2. Deployment & MLOps — You can deploy a model, track experiments and monitor it in production.
   • MLOps Zoomcamp
   • Go deeper: MLOps: Continuous delivery and automation pipelines in machine learning · The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction
3. ML System Design — You can design an ML system end to end and explain its trade-offs.
   • CMU 17-445/645 — Machine Learning in Production

AI Foundations

What AI is and the classic ideas behind it: search, planning, reasoning, probability.

• AI Fluency: Framework and foundations - Anthropic, Joseph Feller, Rick Dakan. course
• AI for Everyone - DeepLearning.AI, Andrew Ng. course · beginner · ~7 h
• CS 188 Introduction to Artificial Intelligence - UC Berkeley, Emma Pierson, Peyrin Kao. course
• CS221: Artificial Intelligence: Principles and Techniques - Stanford, Percy Liang. course
• CS50's Introduction to Artificial Intelligence with Python - Harvard, Brian Yu, David J. Malan. course
• Elements of AI - University of Helsinki, MinnaLearn. course
• MIT 6.034 — Artificial Intelligence - MIT OpenCourseWare, Patrick Henry Winston. course

Machine Learning

Learning from data: models, training, evaluation, generalization.

• 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning - MIT OpenCourseWare, Gilbert Strang. course
• 6.036 Introduction to Machine Learning - MIT Open Learning Library, MIT. course
• AMD AI Academy - AMD. course
• CS 228 - Probabilistic Graphical Models - Stanford, Stefano Ermon. course
• CS221: Artificial Intelligence: Principles and Techniques - Stanford, Percy Liang. course
• CS229: Machine Learning - Stanford, Jehangir Amjad, Anand Avati. course
• CS4780: Machine Learning for Intelligent Systems - Cornell University, Kilian Weinberger. course
• CS50's Introduction to Artificial Intelligence with Python - Harvard, Brian Yu, David J. Malan. course
• Data 100: Principles and Techniques of Data Science - UC Berkeley, UC Berkeley Data 100 staff. course
• Introduction to GenAI and ML 2025 Fall - 國立臺灣大學, 李宏毅. course · beginner · zh-TW
• Kaggle Learn: Intro to Machine Learning - Kaggle, Dan Becker. course · ~3 h
• Learning From Data - Caltech, Yaser Abu-Mostafa. course
• Machine Learning 2019 Spring - 國立臺灣大學, 李宏毅. course · zh-TW

Deep Learning

Neural networks, how they are trained, and the architectures that work.

• AI Engineering from Scratch - Rohit Ghumare. course · ~342 h · 20 units
  • Phase 0: Setup & Tooling (GitHub) github
  • Phase 1: Math Foundations (GitHub) github
  • Phase 2: ML Fundamentals (GitHub) github
  • Phase 3: Deep Learning Core (GitHub) github
  • Phase 4: Computer Vision (GitHub) github
  • Phase 5: NLP, Foundations to Advanced (GitHub) github
  • Phase 6: Speech & Audio (GitHub) github
  • Phase 7: Transformers Deep Dive (GitHub) github
  • Phase 8: Generative AI (GitHub) github
  • Phase 9: Reinforcement Learning (GitHub) github
  • Phase 10: LLMs from Scratch (GitHub) github

Computer Vision

Getting machines to understand images and video.

• 16-824: Visual Learning and Recognition - Carnegie Mellon University, Jun-Yan Zhu. course
• Community Computer Vision Course - Hugging Face, Hugging Face Community. course · beginner
• CS131: Computer Vision: Foundations and Applications - Stanford University, Juan Carlos Niebles, Adrien Gaidon, Silvio Savarese. course
• CS180/280A: Intro to Computer Vision and Computational Photography - UC Berkeley, Alexei Efros. course
• CS231n: Deep Learning for Computer Vision - Stanford University, Fei-Fei Li, Ehsan Adeli, Justin Johnson, Zane Durante, Tiange Xiang. course · ~27 h
• Deep Learning for Computer Vision - University of Michigan. course · ~26 h
• First Principles of Computer Vision - Columbia University, Shree Nayar. course
• Free Computer Vision Courses - OpenCV.org. course · 5 units
• Computer Vision: Algorithms and Applications, 2nd ed. - Richard Szeliski. book
• Foundations of Computer Vision - MIT Press, Antonio Torralba, Phillip Isola, William Freeman. book
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale - arXiv, Dosovitskiy et al. paper
• Deep Residual Learning for Image Recognition - arXiv, He et al. paper

NLP

Natural language processing: text and speech, from classic methods to Transformers.

• CMU 11-711 — Advanced Natural Language Processing - Carnegie Mellon University, Sean Welleck. course
• CS224N: Natural Language Processing with Deep Learning - Stanford, Diyi Yang, Yejin Choi. course
• Deep Learning for Human Language Processing 2020 Spring - 國立臺灣大學, 李宏毅. course · zh-TW
• Hugging Face Audio course - Hugging Face, Sanchit Gandhi, Matthijs Hollemans, Maria Khalusova, Vaibhav Srivastav. course
• Hugging Face LLM Course - Hugging Face. course
• NLP Course | For You - Lena Voita. course
• Introduction to Information Retrieval - Stanford University, Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze. book
• Speech and Language Processing (3rd ed. draft) - Stanford University, Dan Jurafsky, James H. Martin. book
• Attention Is All You Need - arXiv, Vaswani et al. paper
• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks - arXiv, Lewis et al. paper
• The Annotated Transformer - Harvard NLP, Sasha Rush, Austin Huang, Suraj Subramanian, Jonathan Sum, Khalid Almubarak, Stella Biderman. article
• The Illustrated Transformer - Jay Alammar. article

LLMs

Large language models: how they are built, trained, adapted and prompted.

• AI Engineering from Scratch - Rohit Ghumare. course · ~342 h · 20 units
  • Phase 0: Setup & Tooling (GitHub) github
  • Phase 1: Math Foundations (GitHub) github
  • Phase 2: ML Fundamentals (GitHub) github
  • Phase 3: Deep Learning Core (GitHub) github
  • Phase 4: Computer Vision (GitHub) github
  • Phase 5: NLP, Foundations to Advanced (GitHub) github
  • Phase 6: Speech & Audio (GitHub) github
  • Phase 7: Transformers Deep Dive (GitHub) github
  • Phase 8: Generative AI (GitHub) github
  • Phase 9: Reinforcement Learning (GitHub) github
  • Phase 10: LLMs from Scratch (GitHub) github

Generative AI

Models that generate: diffusion, VAEs, GANs, and building products on them.

• AMD AI Academy - AMD. course
• CS236: Deep Generative Models - Stanford, Stefano Ermon. course
• Generative AI for Beginners - Microsoft, Microsoft Cloud Advocates. course
• Generative AI with Large Language Models - DeepLearning.AI, Antje Barth, Chris Fregly, Shelbee Eigenbrode, Mike Chambers. course · intermediate · ~13 h
• Introduction to GenAI and ML 2025 Fall - 國立臺灣大學, 李宏毅. course · beginner · zh-TW
• Introduction to Generative AI 2024 Spring - 國立臺灣大學, 李宏毅. course · 20 units · zh-TW
• MIT 6.S184 — Introduction to Flow Matching and Diffusion Models - MIT, Peter Holderrieth, Ezra Erives. course
• Practical Deep Learning for Coders part 2: Deep Learning Foundations to Stable Diffusion - fast.ai, Jeremy Howard. course · ~30 h
• AI Engineering - O'Reilly, Chip Huyen. book
• Denoising Diffusion Probabilistic Models - arXiv, Jonathan Ho, Ajay Jain, Pieter Abbeel. paper
• Understanding Diffusion Models: A Unified Perspective - arXiv, Calvin Luo. paper
• What are Diffusion Models? - Lil'Log, Lilian Weng. article · 31 min

AI Agents

LLM systems that plan, use tools and act — and how to evaluate and govern them.

• Agentic AI MOOC - UC Berkeley, Dawn Song. course
• AI Agents in LangGraph - DeepLearning.AI, Harrison Chase, Rotem Weiss. course · intermediate · ~1.7 h
• AI Dev Tools Zoomcamp - DataTalks.Club, Alexey Grigorev, Bhavani Ravi, Moein Foroughi. course
• AI Engineering from Scratch - Rohit Ghumare. course · ~342 h · 20 units
  • Phase 0: Setup & Tooling (GitHub) github
  • Phase 1: Math Foundations (GitHub) github
  • Phase 2: ML Fundamentals (GitHub) github
  • Phase 3: Deep Learning Core (GitHub) github
  • Phase 4: Computer Vision (GitHub) github
  • Phase 5: NLP, Foundations to Advanced (GitHub) github
  • Phase 6: Speech & Audio (GitHub) github
  • Phase 7: Transformers Deep Dive (GitHub) github

Reinforcement Learning

Learning by acting: rewards, policies, value functions.

• Deep Reinforcement Learning Course - Hugging Face, Thomas Simonini, Omar Sanseviero, Sayak Paul. course

MLOps

Taking models to production: data, deployment, monitoring, iteration.

• CMU 17-445/645 — Machine Learning in Production - Carnegie Mellon University, Christian Kästner, Bogdan Vasilescu. course
• CS 329S: Machine Learning Systems Design - Stanford, Chip Huyen. course
• Machine Learning in Production - DeepLearning.AI, Andrew Ng. course · intermediate · ~11 h
• Made With ML — MLOps - Made With ML, Goku Mohandas. course
• MLOps Zoomcamp - DataTalks.Club, Alexey Grigorev. course
• AI Engineering - O'Reilly, Chip Huyen. book
• Designing Machine Learning Systems - O'Reilly, Chip Huyen. book
• Machine Learning Systems - Harvard, Vijay Janapa Reddi. book
• The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction - Google, Eric Breck, Shanqing Cai, Eric Nielsen, Michael Salib, D. Sculley. paper
• MLOps: Continuous delivery and automation pipelines in machine learning - Google Cloud. article

ML Systems

Making ML fast and scalable: GPUs, kernels, parallelism, training and serving systems.

• AMD AI Academy - AMD. course
• CMU 17-445/645 — Machine Learning in Production - Carnegie Mellon University, Christian Kästner, Bogdan Vasilescu. course
• CS149: Parallel Computing - Stanford, Kayvon Fatahalian, Kunle Olukotun. course
• CS336: Language Modeling from Scratch - Stanford, Tatsunori Hashimoto, Percy Liang. course
• Deep Learning Systems: Algorithms and Implementation - CMU, Tim Dettmers, Tianqi Chen. course
• Large Language Model Systems - CMU, Lei Li. course
• MIT 6.5940 — TinyML and Efficient Deep Learning Computing - MIT, Song Han. course
• StaffML - Harvard University, Vijay Janapa Reddi. course
• Introduction to Machine Learning Interviews Book - Chip Huyen. book
• Machine Learning Systems - Harvard, Vijay Janapa Reddi. book
• Hidden Technical Debt in Machine Learning Systems - Google, D. Sculley et al. paper
• CUDA C++ Best Practices Guide - NVIDIA. docs
• ROCm Documentation - AMD. docs
• GPU MODE Lectures - GPU MODE. video

Math

Linear algebra, calculus, probability and statistics.

• 18.01SC Single Variable Calculus - MIT OpenCourseWare, David Jerison. course
• 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning - MIT OpenCourseWare, Gilbert Strang. course
• 18.06SC Linear Algebra - MIT OpenCourseWare, Gilbert Strang. course
• CS 228 - Probabilistic Graphical Models - Stanford, Stefano Ermon. course
• CS229: Machine Learning - Stanford, Jehangir Amjad, Anand Avati. course
• Differential Calculus - Khan Academy. course · 6 units
• Linear algebra - Khan Academy. course · 3 units
• Mathematics for Machine Learning and Data Science - DeepLearning.AI, Luis Serrano. course · beginner · ~94 h
• MIT 6.041SC — Probabilistic Systems Analysis and Applied Probability - MIT OpenCourseWare, John Tsitsiklis. course
• Multivariable calculus - Khan Academy. course · 5 units
• Precalculus - Khan Academy. course · 10 units
• Statistics and probability - Khan Academy. course · 16 units

Programming

Writing software well: languages, tools, and the craft of building programs.

• 6.006 Introduction to Algorithms - MIT OpenCourseWare, Erik Demaine, Jason Ku, Justin Solomon. course
• 6.100L Introduction to CS and Programming using Python - MIT OpenCourseWare, Ana Bell. course
• AI Dev Tools Zoomcamp - DataTalks.Club, Alexey Grigorev, Bhavani Ravi, Moein Foroughi. course
• Algorithms, Part I - Princeton University, Kevin Wayne, Robert Sedgewick. course · ~50 h
• CS50's Introduction to Computer Science - Harvard University, David J. Malan. course · beginner
• CS50's Introduction to Programming with Python - Harvard University, David J. Malan. course
• Free Computer Vision Courses - OpenCV.org. course · 5 units
• Full stack open - University of Helsinki, Matti Luukkainen. course
• Kaggle Learn: Data Visualization - Kaggle, Alexis Cook. course · ~4 h
• Kaggle Learn: Pandas - Kaggle, Aleksey Bilogur. course · ~4 h
• Kaggle Learn: Python - Kaggle, Colin Morris. course · ~5 h
• Python for Everybody - Charles R. Severance. course
• Python Programming for Economics and Finance - QuantEcon, Thomas J. Sargent, John Stachurski. course

Computer Systems

How computers, networks and distributed systems actually work.

• 15-213/15-513/14-513 Introduction to Computer Systems - CMU, Phillip Gibbons, Seth Goldstein. course
• 6.5840: Distributed Systems - MIT, Robert Morris, Frans Kaashoek. course · advanced
• CS 144: Introduction to Computer Networking - Stanford, Keith Winstein. course
• CS149: Parallel Computing - Stanford, Kayvon Fatahalian, Kunle Olukotun. course

Databases

Storing and querying data: SQL, indexes, database internals.

• CMU 15-445/645 :: Intro to Database Systems - CMU, Jignesh Patel. course
• CS50's Introduction to Databases with SQL - Harvard University, Carter Zenke, David J. Malan. course · beginner
• Use The Index, Luke! - Markus Winand. book
• Develop with Redis - Redis. docs
• PostgreSQL Documentation - PostgreSQL, PostgreSQL Global Development Group. docs

Backend

Building services: HTTP, architecture, caching, observability.

• Full stack open - University of Helsinki, Matti Luukkainen. course
• Develop with Redis - Redis. docs
• HTTP: Hypertext Transfer Protocol - MDN, Mozilla. docs
• OpenTelemetry Documentation - OpenTelemetry, OpenTelemetry Authors. docs
• A pattern language for microservices - microservices.io, Chris Richardson. article
• The Twelve-Factor App - 12factor.net, Adam Wiggins. article

DevOps

Containers, orchestration and infrastructure as code.

• Build and share a containerized application - Docker. docs · 15 min
• Get started with Docker - Docker, Docker Inc. docs
• Kubernetes Documentation - kubernetes.io, The Kubernetes Authors. docs
• Learn Kubernetes Basics - Kubernetes, The Kubernetes Authors. docs
• Terraform Documentation - HashiCorp. docs
• Terraform Tutorials - HashiCorp. docs
• The Twelve-Factor App - 12factor.net, Adam Wiggins. article

Data Engineering

Pipelines that move and shape data at scale.

• Data Engineering Zoomcamp - DataTalks.Club, Alexey Grigorev. course · beginner

Technical Writing

Writing clear technical documents.

• Google Technical Writing One - Google. course · beginner · ~2 h
• Google Technical Writing Two - Google. course · intermediate · ~1.2 h

Staying Current

Ongoing sources to follow. They never finish, so they are not courses — read an issue, keep what matters.

• StatQuest with Josh Starmer - StatQuest, Josh Starmer. video
• Ahead of AI - Ahead of AI, Sebastian Raschka. article
• Daily Papers - Hugging Face. article
• Interconnects - Interconnects, Nathan Lambert. article
• Learn Computer Vision - Roboflow. article
• One Useful Thing - One Useful Thing, Ethan Mollick. article

Contributing

Found something that meets the criteria? See CONTRIBUTING.md. Open a pull request that adds one JSON file under resources/ — this README is generated from those files.

Acknowledgements

The selection criteria were inspired by The No-Hype AI Learning Guide by h9-tec.

Question sources & licenses

The interview questions in questions/ are original work except for one borrowed input: 375 of the technical question prompts come from ai-engineering-interview-questions-company-wise by pallavi-shekhar, which is licensed under Apache-2.0. Only the question prompts were taken. The key points and answer levels are written independently, and no answers were copied. The upstream repository has no NOTICE file. Behavioral questions cite their own sources in each file.

License

This list is licensed under CC BY 4.0. Reuse it freely — just credit AI Learn World and link back to this repository.