> ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. --- title: "Learning Paths, Courses & Books" url: https://daily.dev/agentic-ai-hub/learning-paths-courses-books/ description: "Organized beginner β†’ advanced. Pick a lane and go deep. Breadth comes with time." lastUpdated: "2026-07-22" --- Organized beginner β†’ advanced. Pick a lane and go deep. Breadth comes with time. ## Roadmaps (start here to orient) - **roadmap.sh (AI Engineer)**: [roadmap.sh/ai-engineer](https://roadmap.sh/ai-engineer). Visual, community-maintained path from fundamentals to building AI products. A good map of the territory. πŸ”„ - **roadmap.sh (AI/Data Scientist)**: [roadmap.sh/ai-data-scientist](https://roadmap.sh/ai-data-scientist). For the modeling and research side rather than the app side. - **roadmap.sh (AI Agents)**: [roadmap.sh/ai-agents](https://roadmap.sh/ai-agents). Design, build, and ship agents; the newest and most on-trend of the three roadmaps. πŸ”„ ## Beginner (build intuition + first models) - **Andrew Ng, Machine Learning Specialization**: [coursera.org/specializations/machine-learning-introduction](https://www.coursera.org/specializations/machine-learning-introduction). The classic on-ramp to ML fundamentals. - **DeepLearning.AI Short Courses**: [deeplearning.ai/short-courses](https://www.deeplearning.ai/short-courses/). Free 1-to-2-hour project courses on RAG, agents, and fine-tuning, built with the actual tool vendors. High ROI for practitioners. πŸ”„ - **3Blue1Brown, Neural Networks series**: [youtube.com/@3blue1brown](https://www.youtube.com/@3blue1brown). Watch before or alongside any course. The visual intuition sticks. - **Book: *AI Engineering*, Chip Huyen** (2025): [oreilly.com](https://www.oreilly.com/library/view/ai-engineering/9781098166298/). Intro to building applications on foundation models. A starting point for the app-builder path. - **Book: *Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow*, AurΓ©lien GΓ©ron** (3rd ed., 2022): [oreilly.com](https://www.oreilly.com/library/view/hands-on-machine-learning/9781098125967/). The best-selling practical ML reference; code-first and thorough. ## Intermediate (build real things, understand internals) - **Andrej Karpathy, Neural Networks: Zero to Hero**: [karpathy.ai/zero-to-hero.html](https://karpathy.ai/zero-to-hero.html). Build a GPT from scratch, line by line. A free deep-learning course. - **fast.ai, Practical Deep Learning for Coders**: [course.fast.ai](https://course.fast.ai/). Top-down and code-first. Get models working, then learn why. - **Hugging Face Courses**: [huggingface.co/learn](https://huggingface.co/learn). Free NLP, LLM, agents, and RL courses using the HF stack; very hands-on. πŸ”„ - **Book: *Build a Large Language Model (From Scratch)*, Sebastian Raschka** (2024): [manning.com](https://www.manning.com/books/build-a-large-language-model-from-scratch). Implement a full LLM yourself. The paper-to-code bridge. - **Book: *Hands-On Large Language Models*, Jay Alammar & Maarten Grootendorst** (2024): [oreilly.com](https://www.oreilly.com/library/view/hands-on-large-language/9781098150952/). Visual, practical guide to using and understanding LLMs. - **Book: *Natural Language Processing with Transformers*, Tunstall, von Werra & Wolf** (rev. ed., 2022): [oreilly.com](https://www.oreilly.com/library/view/natural-language-processing/9781098136789/). The canonical transformers/Hugging Face book, by HF's own authors. Pairs with the HF courses above. - **Course: *Generative AI with Large Language Models* (DeepLearning.AI + AWS)**: [coursera.org/learn/generative-ai-with-llms](https://www.coursera.org/learn/generative-ai-with-llms). The de facto standard applied-LLM course: prompting, fine-tuning, and RLHF end to end. ## Advanced (research depth + production scale) - **Stanford CS224N (NLP with Deep Learning)**: [web.stanford.edu/class/cs224n](https://web.stanford.edu/class/cs224n/). Lectures on YouTube; the academic backbone for NLP and LLMs. - **Stanford CS336 (Language Modeling from Scratch)**: [stanford-cs336.github.io](https://stanford-cs336.github.io/). Build a full LLM training stack. About as close to how frontier labs work as a course gets. πŸ”„ - **Book: *Designing Machine Learning Systems*, Chip Huyen** (2022): [oreilly.com](https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/). A production MLOps reference for shipping ML at scale. - **Book: *Deep Learning*, Goodfellow, Bengio, Courville** (2016): [deeplearningbook.org](https://www.deeplearningbook.org/). The foundational theory text, free online. Dense but canonical. - **Read the papers**: [Key Papers & Reading List](/agentic-ai-hub/key-papers-reading-list/) is itself the advanced curriculum. Reproduce one from scratch. Nothing teaches faster. > > **A pragmatic path for a working developer (2026):** roadmap.sh/ai-engineer to orient β†’ Karpathy Zero-to-Hero for internals β†’ a couple of DeepLearning.AI short courses on RAG + agents β†’ *AI Engineering* (Huyen) β†’ then ship something real and let [People to Follow (X/Twitter)](/agentic-ai-hub/people-to-follow/)/[Communities](/agentic-ai-hub/communities/) keep you current. The half-life of specific tools is short. The fundamentals and the *habit of following the frontier* are what compound.