---
title: "learn-ai-engineering"
type: "tool"
slug: "ashishps1-learn-ai-engineering"
canonical_url: "https://www.graphcanon.com/tools/ashishps1-learn-ai-engineering"
github_url: "https://github.com/ashishps1/learn-ai-engineering"
homepage_url: null
stars: 5803
forks: 1414
primary_language: null
license: "GPL-3.0"
categories: ["llm-frameworks", "developer-tools"]
tags: ["deep-learning", "agents", "llm", "ai", "machine-learning", "large-language-models", "generative-ai", "agentic-ai"]
updated_at: "2026-07-07T18:39:12.212399+00:00"
---

# learn-ai-engineering

> Learn AI and LLMs from scratch using free resources

A collection of free educational materials to learn about AI, machine learning, large language models, and agents. It includes foundational mathematics, Python programming, ML frameworks like Scikit-learn and XGBoost, DL frameworks such as TensorFlow and PyTorch, and specializations in deep learning from top universities.

## Facts

- Repository: https://github.com/ashishps1/learn-ai-engineering
- Stars: 5,803 · Forks: 1,414 · Open issues: 8 · Watchers: 68
- License: GPL-3.0
- Last pushed: 2026-02-05T02:34:40+00:00

## Categories

- [LLM Frameworks](/categories/llm-frameworks.md)
- [Developer Tools](/categories/developer-tools.md)

## Tags

deep-learning, agents, llm, ai, machine-learning, large language models, generative-ai, agentic-ai

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## README (excerpt)

```text
# Learn AI Engineering

A comprehensive collection of free resources to learn everything about AI/ML, LLMs and Agents.

## Mathematical Foundations
- [Mathematics Roadmap for Machine Learning](https://thepalindrome.org/p/the-roadmap-of-mathematics-for-machine-learning)
- [Essence of Linear Algebra - 3Blue1Brown](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)
- [Probability & Statistics - Khan Academy](https://www.khanacademy.org/math/statistics-probability)
- [Statistics Fundamentals - Josh Strarmer](https://www.youtube.com/playlist?list=PLblh5JKOoLUK0FLuzwntyYI10UQFUhsY9)
- [Mathematics for Machine Learning Specialization - Coursera (Andrew Ng)](https://www.coursera.org/specializations/mathematics-machine-learning)

## Python
- [AI Python for Beginners - Deeplearning.ai](https://www.deeplearning.ai/short-courses/ai-python-for-beginners/)

## AI & ML Fundamentals
- [Machine Learning Crash Course - Google](https://developers.google.com/machine-learning/crash-course)
- [AI for Beginners – Microsoft](https://microsoft.github.io/AI-For-Beginners/)
- [Elements of AI – University of Helsinki](https://course.elementsofai.com/)
- [Machine Learning Playlist - Josh Strarmer](https://www.youtube.com/playlist?list=PLblh5JKOoLUICTaGLRoHQDuF_7q2GfuJF)
- [Machine Learning Specialization - Coursera](https://www.coursera.org/specializations/machine-learning-introduction)

### Machine Learning Frameworks
- [Scikit-learn](https://scikit-learn.org/stable/)
- [XGBoost](https://xgboost.ai/)
- [LightGBM](https://lightgbm.readthedocs.io/en/stable/)
- [CatBoost](https://catboost.ai/)

## Deep Learning
- [Deep Learning Specialization - Coursera (Andrew Ng)](https://www.coursera.org/specializations/deep-learning)
- [Practical Deep Learning for Coders - Fast.ai](https://course.fast.ai/)
- [Mathematics for Deep Learning](https://d2l.ai/chapter_appendix-mathematics-for-deep-learning/)
- [Deep Learning Playlist - Josh Starmer](https://www.youtube.com/playlist?list=PLblh5JKOoLUIxGDQs4LFFD--41Vzf-ME1)

### Deep Learning Frameworks
- [TensorFlow](https://www.tensorflow.org/)
- [PyTorch](https://pytorch.org/)
- [Keras](https://keras.io/)

## Deep Learning Specializations
### Computer Vision
- [Deep Learning for Computer Vision - Stanford](https://cs231n.stanford.edu/)
### Natural Language Processing (NLP)
- [NLP Specialization - Coursera](https://www.coursera.org/specializations/natural-language-processing)
### Reinforcement Learning
- [Deep RL Course - Hugging Face](https://huggingface.co/learn/deep-rl-course/unit0/introduction)
- [Deep RL Bootcamp - UC Berkeley](https://sites.google.com/view/deep-rl-bootcamp/lectures)

## Generative AI
- [The Building Blocks of Generative AI](https://shriftman.substack.com/p/the-building-blocks-of-generative)
- [Generative AI for Beginners - Microsoft](https://github.com/microsoft/generative-ai-for-beginners)
- [Generative AI for Everyone - Coursera](https://www.coursera.org/learn/generative-ai-for-everyone)

## Large Language Models (LLMs)
- [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/)
- [Large Language Models explained briefly](https://www.youtube.com/watch?v=LPZh9BOjkQs)
- [Intro to LLMs](https://www.youtube.com/watch?v=zjkBMFhNj_g&pp=ygUDbGxt)
- [Understanding Large Language Models](https://magazine.sebastianraschka.com/p/understanding-large-language-models)
- [A Visual Guide to Reasoning LLMs](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-reasoning-llms)
- [Understanding Reasoning LLMs](https://magazine.sebastianraschka.com/p/understanding-reasoning-llms)
- [Understanding Multimodal LLMs](https://magazine.sebastianraschka.com/p/understanding-multimodal-llms)
- [A Visual Guide to Mixture of Experts (MoE)](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-mixture-of-experts)
- [Finetuning Large Language Models](https://magazine.sebastianraschka.com/p/finetuning-large-language-models)
- [How Transformer LLMs Work](https://www.deep
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/ashishps1-learn-ai-engineering`](/api/graphcanon/tools/ashishps1-learn-ai-engineering)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_