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awesome-llm-courses
A curated list of awesome online courses about Large Langage Models (LLMs)
https://github.com/wikit-ai/awesome-llm-courses
Last synced: 5 days ago
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- 07 Generative AI Explorer - Vertex AI
- Learn Embeddings and Vector Databases
- Learn LangChain.js
- Introduction to Natural Language Processing
- Text Classification with Natural Language Processing
- Pretraining LLMs – Upstage
- Prompt Compression and Query Optimization – MongoDB
- 08 Explore and Evaluate Models using Model Garden
- 09 Prompt Design using PaLM
- CodeSignal
- Understanding LLMs and Basic Prompting Techniques
- LLM University by Cohere
- Become an AI Developer – DataCamp
- Introduction to Large Language Models with GPT & LangChain
- Prompt Engineering with GPT & LangChain
- Building Multimodal AI Applications with LangChain & the OpenAI API
- Semantic Search with Pinecone
- Retrieval Augmented Generation with OpenAI API & Pinecone
- Building Chatbots with the OpenAI API and Pinecone
- Using Open Source AI Models with Hugging Face
- Building NLP Applications with Hugging Face
- Image Classification with Hugging Face
- Introduction to Large Language Models – Google Cloud
- Encoder-Decoder Architecture – Google Cloud
- Scrimba Courses Library – Artificial Intelligence
- The AI Engineer Path
- Intro to AI Engineering
- Learn Embeddings and Vector Databases
- Learn AI Agents
- Prompt Engineering for Web Developers
- Learn to code with AI
- Build AI Apps with ChatGPT, DALL-E and GPT-4
- The Official LangChain.js Course
- W&B AI Academy
- LLM Engineering: Structured Outputs
- Building LLM-Powered Apps
- Training and Fine-tuning Large Language Models (LLMs)
- Effective MLOps: Model Development
- CI/CD for Machine Learning (GitOps)
- Data Validation in Production ML Pipelines
- Machine Learning for Business Decision Optimization
- W&B 101
- W&B 201: Model Registry
- Introduction to Generative AI Learning Path
- 01 Introduction to Generative AI
- 02 Introduction to Large Language Models
- 03 Introduction to Responsible AI
- 05 Responsible AI: Applying AI Principles with Google Cloud
- Generative AI for Developers Learning Path
- 01 Introduction to Image Generation
- 02 Attention Mechanism
- 03 Encoder-Decoder Architecture
- 04 Transformer Models and BERT Model
- 05 Create Image Captioning Models
- 06 Introduction to Generative AI Studio
- Activeloop
- LangChain & Vector Databases in Production
- Retrieval Augmented Generation for Production with LangChain & LlamaIndex - level video content – 25 hours of learning content
- Training & Fine-Tuning LLMs for Production - level video content – 40 hours of learning content
- Full Stack LLM Bootcamp (Spring 2023)
- Learn LangChain.js - Build LLM apps with JavaScript and OpenAI
- LLMOps: Building Real-World Applications With Large Language Models
- Prompt Engineering Course
- CSC 6201/CIE 6021 Large Language Models
- Augmenting LLMs using Retrieval Augmented Generation - Beginner
- Building RAG Agents for LLMs - Intermediate
- Community Computer Vision Course - vision-course/unit4/multimodal-models/pre-intro))
- LLMOps: Building Real-World Applications With Large Language Models
- Introduction to Large Language Models with Google Cloud
- Carbon Aware Computing for GenAI Developers – Google Cloud
- Function-Calling and Data Extraction with LLMs – Nexusflow
- Building Your Own Database Agent – Microsoft
- Introduction to On-Device AI – Qualcomm
- Building Multimodal Search and RAG – Weaviate
- Red Teaming LLM Applications – Giskard
- JavaScript RAG Web Apps with LlamaIndex
- Efficiently Serving LLMs – Predibase
- Serverless LLM apps with Amazon Bedrock – AWS
- ChatGPT Prompt Engineering for Developers – OpenAI
- Building Systems with the ChatGPT API – OpenAI
- LangChain for LLM Application Development – LangChain
- LangChain: Chat with Your Data – LangChain
- Finetuning Large Language Models – Lamini
- Large Language Models with Semantic Search – Cohere
- Evaluating and Debugging Generative AI Models Using Weights and Biases – W&B
- How Diffusion Models Work
- Building Applications with Vector Databases – Pinecone
- Automated Testing for LLMOps – circleci
- LLMOps – Google Cloud
- Build LLM Apps with LangChain.js – LangChain
- Advanced Retrieval for AI with Chroma – Chroma
- Reinforcement Learning from Human Feedback – Google Cloud
- Building and Evaluating Advanced RAG Applications – LlamaIndex
- Quality and Safety for LLM Applications – Whylabs
- Vector Databases: from Embeddings to Applications – Weaviate
- Functions, Tools and Agents with LangChain – LangChain
- Pair Programming with a Large Language Model – Google
- Understanding and Applying Text Embeddings – Google Cloud
- 👷 The LLM Engineer
- PY_101V: Your own vectors with Weaviate - based
- PY_101M: Multimodal data with Weaviate - based
- PY_220: Flexible data representation: Named vectors - based
- PY_275: Text tokenization
- PY_280: Multi-tenancy
- TS_100: Intro to Weaviate with TypeScript (or JavaScript) - based
- Weaviate Academy
- PY_230: Vector indexes
- PY_250: Vector compression for improved efficiency
- PY_101T: Text data with Weaviate - based
- Introduction to Gen AI Studio with Google Cloud
- Introduction to Gemini for Google Workspace
- Introduction to Image Generation with Google Cloud
- Generative AI Fundamentals with Google Cloud
- Gemini in Gmail
- Gemini in Google Docs
- Gemini in Google Meet
- Gemini in Google Sheets
- Gemini in Google Slides
- Gemini API by Google
- Transformer Models and BERT Model with Google Cloud
- Knowledge Graphs for RAG – Neo4j
- Building Generative AI Applications with Gradio – HuggingFace
- How Business Thinkers Can Start Building AI Plugins With Semantic Kernel – Microsoft
- Introduction to LangGraph
- IBM: Introduction to Generative AI
- IBM: Introduction to Prompt Engineering - 3 hours per week
- IBM: Models and Platforms for Generative AI - 3 hours per week
- IBM: Developing Generative AI Applications with Python
- Build a Chat Application using the PaLM 2 API on Cloud Run – Google Cloud
- Generative AI with Large Language Models – AWS
- NVIDIA – Self-Paced Courses
- Web Security Academy
- Web LLM attacks
- Multimodal RAG: Chat with Videos – Intel
- AI Python for Beginners - 5 Hours – Beginner
- Large Multimodal Model Prompting with Gemini – Google Cloud
- Building AI Applications with Haystack
- Improving Accuracy of LLM Applications – Lamini and Meta
- Embedding Models: From Architecture to Implementation – Vectara
- Federated Learning – Flower
- Scrimba Courses Library – Artificial Intelligence
- Deploy AI apps with Cloudflare
- Intro to Mistral AI
- Learn OpenAI's Assistants API
- RAG++ : From POC to Production
- Developer's guide to LLM prompting
- Enterprise Model Management - to-end model lifecycle. Include LLM Case Study – 25 lessons – 2.5 hours of video content
- Neo4j Generative AI Courses
- Neo4j & LLM Fundamentals
- Introduction to Vector Indexes and Unstructured Data
- Build a Neo4j-backed Chatbot using Python - Feat. Langchain and Streamlit
- Build a Neo4j-backed Chatbot with TypeScript - Feat. Langchain and Next.js
- Building Knowledge Graphs with LLMs
- 04 Generative AI Fundamentals
- 07 Generative AI Explorer - Vertex AI
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