What is Learn Ai Engineering

Learn-ai-engineering is a comprehensive collection of free resources designed to help individuals learn about artificial intelligence (AI), machine learning (ML), large language models (LLMs), and agents from scratch.

Use cases

Use cases include self-study for individuals looking to enter the AI/ML field, educational institutions incorporating the resources into their curriculum, and professionals seeking to enhance their skills in AI and ML technologies.

How to use

Users can access the resources by visiting the provided links in the README. The resources are organized into categories such as Mathematical Foundations, Python, AI & ML Fundamentals, Machine Learning Frameworks, Deep Learning, and Deep Learning Frameworks.

Key features

Key features include a wide range of free educational materials, structured learning paths for various topics in AI and ML, and practical resources for hands-on experience with popular frameworks and tools.

Where to use

Learn-ai-engineering can be utilized in various fields including education, data science, software development, and research, particularly in areas focused on AI and ML applications.

Learn AI Engineering

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

Mathematical Foundations

• Essence of Linear Algebra - 3Blue1Brown
• Probability & Statistics - Khan Academy
• Statistics Fundamentals - Josh Strarmer
• Mathematics for Machine Learning Specialization - Coursera (Andrew Ng)

Python

• AI Python for Beginners - Deeplearning.ai

AI & ML Fundamentals

• Machine Learning Crash Course - Google
• AI for Beginners – Microsoft
• Elements of AI – University of Helsinki
• Machine Learning Playlist - Josh Strarmer
• Machine Learning Specialization - Coursera

Machine Learning Frameworks

• Scikit-learn
• XGBoost
• LightGBM
• CatBoost

Deep Learning

• Deep Learning Specialization - Coursera (Andrew Ng)
• Practical Deep Learning for Coders - Fast.ai
• Mathematics for Deep Learning
• Deep Learning Playlist - Josh Starmer

Deep Learning Frameworks

• TensorFlow
• PyTorch
• Keras

Deep Learning Specializations

Computer Vision

• Deep Learning for Computer Vision - Stanford

Natural Language Processing (NLP)

• NLP Specialization - Coursera

Reinforcement Learning

• Deep RL Course - Hugging Face
• Deep RL Bootcamp - UC Berkeley

Generative AI

• The Building Blocks of Generative AI
• Generative AI for Beginners - Microsoft
• Generative AI for Everyone - Coursera

Large Language Models (LLMs)

• The Illustrated Transformer
• Large Language Models explained briefly
• Intro to LLMs
• Understanding Large Language Models
• A Visual Guide to Reasoning LLMs
• Understanding Reasoning LLMs
• Understanding Multimodal LLMs
• A Visual Guide to Mixture of Experts (MoE)
• Finetuning Large Language Models
• How Transformer LLMs Work
• Building GPT from scratch - Andrej Karpathy
• LLM Course - GitHub
• LLM Course - Hugging Face
• Awesome LLM Apps - GitHub

LLM Chatbots

• ChatGPT
• Gemini
• Claude
• Perplexity

Open Source LLMs

• Llama
• Deepseek

LLM APIs

• OpenAI
• Anthropic
• Gemini - Google
• Groq - Inference

LLM Tools & Frameworks

• LangChain
• LlamaIndex
• Ollama
• Instructor
• Outlines

LLM Based IDEs

• Cursor
• Windsurf
• GitHub Copilot

Prompt Engineering

• Google Prompting Essentials
• ChatGPT Prompt Engineering for Developers - Deeplearning.ai
• Advanced Prompting Techniques - Instructor
• Prompt Engineering Techniques - Github
• Getting Structured LLM Output - Deeplearning.ai

Retrieval-Augmented Generation (RAG)

• Introduction to RAG - Coursera
• RAG Techniques - Github

AI Agents

• A Visual Guide to LLM Agents
• Agents - Chip Huyen
• AI Agents Course - Hugging Face
• Building AI Browser Agents - Deeplearning.ai
• GenAI Agents - Github

Model Context Protocol (MCP)

• MCP - Anthropic Guide
• Building AI Apps using MCP
• MCP Course - Hugging Face
• Awesome MCP Servers - Github

MLOps & Deployment

• ML in Production - Coursera
• Full Stack Deep Learning
• ML System Design - Stanford

Tools

• Streamlit
• MLflow

Guides

• OpenAI Cookbook
• Anthropic courses

Books

• Hands-On Machine Learning
• Deep Learning - Ian Goodfellow
• Deep Learning with Python
• Why Machines Learn
• Designing Machine Learning Systems
• AI Engineering
• Build a LLM from Scratch
• Prompt Engineering for LLMs
• Natural Language Processing with Transformers

YouTube Channels

• Andrej Karpathy
• 3Blue1Brown

Other Resources

• Papers with Code
• Kaggle Competitions

Must-Read AI Papers

• Attention Is All You Need
• Generative Adversarial Networks (GANs)
• GPT: Improving Language Understanding by Generative Pre-Training
• GPT-3: Language Models are Few-Shot Learners
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• Chain-of-Thought Prompting Elicits Reasoning in LLMs