# ashishps1/learn-ai-engineering

Learn AI and LLMs from scratch using free resources

Repository: https://github.com/ashishps1/learn-ai-engineering
Canonical: https://ross.abutalabs.com/products/learn-ai-engineering
License: GPL-3.0
License Family: copyleft
Topics: agentic-ai, agents, ai, deep-learning, generative-ai, large-language-models, llm, machine-learning, mcp, ml, prompt-engineering, rag
Last push: 2026-02-05T02:34:40+00:00

## Health v2 (maintenance only)
Score: 49/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 65, release rhythm 35, longevity 36
- inputs: {"age_days": 509, "days_push": 210, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5961, forks 1431 (observed 2026-08-28T04:09:33.751988+00:00)

## What it is
A curated collection of free resources for learning AI engineering from scratch, covering math foundations, Python, machine learning, deep learning, generative AI, LLMs, RAG, and AI agents. It is a learning roadmap repository linking to courses, videos, and documentation rather than shipping software.

## Use cases
- learn ai engineering from scratch
- find free courses on large language models
- roadmap for becoming an ai engineer
- learn machine learning fundamentals for free
- study rag and agentic ai concepts
- find deep learning tutorials and specializations

## When to choose
- you want a structured, free self-study path into AI/ML and LLMs
- you need curated links to courses, videos, and roadmaps in one place
- you are a beginner looking for math, Python, and ML foundations before LLM topics

## When to avoid
- you need runnable code, libraries, or production tooling
- you want an interactive course with exercises and certification rather than a link collection
- you need up-to-date primary documentation for a specific framework

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, llm-inference, rag, agent-framework, prompt-engineering, mcp
- domain: artificial-intelligence, machine-learning, deep-learning, large-language-models, tutorials, awesome-lists
- platform: cross-platform
- tags: curated-resources, free-courses, roadmap, generative-ai, self-learning, ai-agents, retrieval-augmented-generation

## Member repositories
- ashishps1/learn-ai-engineering (main) score 49

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:33.751988+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:50:24.366041+00:00, confidence not recorded.
  - readme: https://github.com/ashishps1/learn-ai-engineering (fetched 2026-08-28T04:09:33.751988+00:00, sha 2ce3db71e8c3)
- Data as of 2026-08-30T08:39:29.467469+00:00.