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A curated list of awesome online courses about Large Langage Models (LLMs)
https://github.com/wikit-ai/awesome-llm-courses
List: awesome-llm-courses
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A curated list of awesome online courses about Large Langage Models (LLMs)
Host: GitHub
URL: https://github.com/wikit-ai/awesome-llm-courses
Owner: wikit-ai
License: cc0-1.0
Created: 2024-02-05T10:25:38.000Z (11 months ago)
Default Branch: main
Last Pushed: 2024-07-18T05:49:20.000Z (5 months ago)
Last Synced: 2024-07-29T13:10:29.654Z (5 months ago)
Topics: awesome, awesome-list, courses, large-language-models, llm, llms, natural-language-processing, nlp, online-courses
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Stars: 62
Watchers: 2
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project

awesome-machine-learning-interpretability - Awesome LLM Courses - ai/awesome-llm-courses?style=social) (Education Resources / Open-ish Classes)
ultimate-awesome - awesome-llm-courses - A curated list of awesome online courses about Large Langage Models (LLMs). (Other Lists / Monkey C Lists)
README

        
# Awesome LLM Courses [![Awesome](https://awesome.re/badge-flat.svg)](https://awesome.re)

> A curated list of awesome online courses about Large Langage Models (LLMs).

We try to monitor free available online courses about LLMs. Please open a PR or an issue if you want to suggest a list update 🤓

- 🤗 Hugging Face

	- [NLP Course](https://huggingface.co/learn/nlp-course/)

	- [Community Computer Vision Course](https://huggingface.co/learn/computer-vision-course/) (cf. [Unit 4 about Multimodal & Vision Language Models 🌐📚👁️](https://huggingface.co/learn/computer-vision-course/unit4/multimodal-models/pre-intro))

- [CodeSignal](https://learn.codesignal.com/course-paths) – Selected LLM/NLP course paths with Cosmo, the AI tutor 🐶✨

	- [Understanding LLMs and Basic Prompting Techniques](https://learn.codesignal.com/preview/course-paths/16) — 5 lessons — 15 practices — Intermediate

	- [Introduction to Natural Language Processing](https://learn.codesignal.com/preview/course-paths/42) – 4 courses – 78 practices – Intermediate

	- [Text Classification with Natural Language Processing](https://learn.codesignal.com/preview/course-paths/24) – 4 courses – 110 practices – Advanced

- [🗣️ Large Language Model Course – Maxime Labonne](https://github.com/mlabonne/llm-course)

	- [🧩 LLM Fundamentals](https://github.com/mlabonne/llm-course#-llm-fundamentals)

	- [🧑‍🔬 The LLM Scientist](https://github.com/mlabonne/llm-course#-the-llm-scientist)

	- [👷 The LLM Engineer](https://github.com/mlabonne/llm-course#-the-llm-engineer)

- Udacity

	- [Introduction to Large Language Models with Google Cloud](https://www.udacity.com/course/introduction-large-language-models-google-cloud--cd12959) – 45 Minutes – Beginner

	- [Introduction to Gen AI Studio with Google Cloud](https://www.udacity.com/course/introduction-to-generative-ai-studio-with-google-cloud--cd13292) – 20 Hours — Beginner

	- [Introduction to Gemini for Google Workspace](https://www.udacity.com/course/introduction-to-duet-AI-in-google-workspace--cd13517) – 1 Day – Beginner

	- [Introduction to Image Generation with Google Cloud](https://www.udacity.com/course/introduction-image-generation-google-cloud--cd12982) – 1 Day – Intermediate

	- [Generative AI Fundamentals with Google Cloud](https://www.udacity.com/course/generative-ai-fundamentals-for-google-cloud--cd13291) – 4 Days – Beginner

	- [Gemini in Gmail](https://www.udacity.com/course/duet-AI-in-gmail--cd13518) – 1 Day – Beginner

	- [Gemini in Google Docs](https://www.udacity.com/course/gemini-in-google-docs--cd13677) – 1 Day – Beginner

	- [Gemini in Google Meet](https://www.udacity.com/course/duet-AI-in-google-meet--cd13540) – 1 Day – Beginner

	- [Gemini in Google Sheets](https://www.udacity.com/course/duet-AI-in-google-sheets--cd13542) – 1 Day – Beginner

	- [Gemini in Google Slides](https://www.udacity.com/course/duet-AI-in-google-slides--cd13543) – 1 Day – Beginner

	- [Gemini API by Google](https://www.udacity.com/course/gemini-API-by-google--cd13416) – 3 Days – Intermediate

	- [LLMOps: Building Real-World Applications With Large Language Models](https://www.udacity.com/course/building-real-world-applications-with-large-language-models--cd13455) – 11 Hours – Intermediate

	- [Transformer Models and BERT Model with Google Cloud](https://www.udacity.com/course/transformer-models-bert-model-google-cloud--cd12969) – 1 Day – Beginner

- [DeepLearning.AI – Short Courses](https://www.deeplearning.ai/short-courses/)

	- [Multimodal RAG: Chat with Videos – Intel](https://www.deeplearning.ai/short-courses/multimodal-rag-chat-with-videos/) – 1 Hour – Intermediate

	- [AI Python for Beginners](https://www.deeplearning.ai/short-courses/ai-python-for-beginners/) – 4-5 Hours – Beginner

	- [Large Multimodal Model Prompting with Gemini – Google Cloud](https://www.deeplearning.ai/short-courses/large-multimodal-model-prompting-with-gemini/) – 2 Hours – Beginner

	- [Building AI Applications with Haystack](https://www.deeplearning.ai/short-courses/building-ai-applications-with-haystack/) – 1 Hour – Intermediate

	- [Improving Accuracy of LLM Applications – Lamini and Meta](https://www.deeplearning.ai/short-courses/improving-accuracy-of-llm-applications/) – 1x Hour – Intermediate

	- [Embedding Models: From Architecture to Implementation – Vectara](https://www.deeplearning.ai/short-courses/embedding-models-from-architecture-to-implementation/) – 1 Hour – Beginner

	- [Federated Learning – Flower](https://www.deeplearning.ai/short-courses/intro-to-federated-learning/) – 2 Hours – Beginner to Intermediate

	- [Pretraining LLMs – Upstage](https://www.deeplearning.ai/short-courses/pretraining-llms/) – 1 Hour – Beginner

	- [Prompt Compression and Query Optimization – MongoDB](https://www.deeplearning.ai/short-courses/prompt-compression-and-query-optimization/) – 1 Hour – Intermediate

	- [Carbon Aware Computing for GenAI Developers – Google Cloud](https://www.deeplearning.ai/short-courses/carbon-aware-computing-for-genai-developers) – 1 Hour – Beginner

	- [Function-Calling and Data Extraction with LLMs – Nexusflow](https://www.deeplearning.ai/short-courses/function-calling-and-data-extraction-with-llms) – 1 Hour – Intermediate

	- [Building Your Own Database Agent – Microsoft](https://www.deeplearning.ai/short-courses/building-your-own-database-agent) – 1 Hour – Beginner

	- [AI Agents in LangGraph – LangChain, Tavily](https://www.deeplearning.ai/short-courses/ai-agents-in-langgraph) – 1 Hour – Intermediate

	- [AI Agentic Design Patterns with AutoGen – Microsoft, Penn State University](https://www.deeplearning.ai/short-courses/ai-agentic-design-patterns-with-autogen) – 1 Hour – Beginner

	- [Introduction to On-Device AI – Qualcomm](https://www.deeplearning.ai/short-courses/introduction-to-on-device-ai) – 1 Hour – Beginner

	- [Multi AI Agent Systems with crewAI – crewAI](https://www.deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai) – 1 Hour – Beginner

	- [Building Multimodal Search and RAG – Weaviate](https://www.deeplearning.ai/short-courses/building-multimodal-search-and-rag) – 1 Hour – Intermediate

	- [Building Agentic RAG with LlamaIndex – LlamaIndex](https://www.deeplearning.ai/short-courses/building-agentic-rag-with-llamaindex) – 1 Hour – Beginner

	- [Quantization in Depth – Hugging Face](https://www.deeplearning.ai/short-courses/quantization-in-depth) – 1 Hour – Intermediate

	- [Prompt Engineering for Vision Models – Comet](https://www.deeplearning.ai/short-courses/prompt-engineering-for-vision-models) – 1 Hour – Beginner

	- [Getting Started With Mistral – Mistral AI](https://www.deeplearning.ai/short-courses/getting-started-with-mistral) – 1 Hour – Beginner

	- [Quantization Fundamentals with Hugging Face – Hugging Face](https://www.deeplearning.ai/short-courses/quantization-fundamentals-with-hugging-face) – 1 Hour – Beginner

	- [Preprocessing Unstructured Data for LLM Applications – Unstructured](https://www.deeplearning.ai/short-courses/preprocessing-unstructured-data-for-llm-applications) – 1 Hour – Beginner

	- [Open Source Models with Hugging Face – Hugging Face](https://www.deeplearning.ai/short-courses/open-source-models-hugging-face) – 1 Hour – Beginner

	- [Prompt Engineering with Llama 2 & 3 – Meta](https://www.deeplearning.ai/short-courses/prompt-engineering-with-llama-2) – 1 Hour – Beginner

	- [Red Teaming LLM Applications – Giskard](https://www.deeplearning.ai/short-courses/red-teaming-llm-applications/) – 1 hour – Beginner

	- [JavaScript RAG Web Apps with LlamaIndex](https://www.deeplearning.ai/short-courses/javascript-rag-web-apps-with-llamaindex/) – 1 hour – Beginner

	- [Efficiently Serving LLMs – Predibase](https://www.deeplearning.ai/short-courses/efficiently-serving-llms/) – 1 hour – Intermediate

	- [Knowledge Graphs for RAG – Neo4j](https://www.deeplearning.ai/short-courses/knowledge-graphs-rag/) – 1 hour – Intermediate

	- [Serverless LLM apps with Amazon Bedrock – AWS](https://www.deeplearning.ai/short-courses/serverless-llm-apps-amazon-bedrock/) – 1 hour – Intermediate

	- [ChatGPT Prompt Engineering for Developers – OpenAI](https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/) – 1 hour – Beginner to Advanced

	- [Building Systems with the ChatGPT API – OpenAI](https://www.deeplearning.ai/short-courses/building-systems-with-chatgpt/) – 1 hour – Beginner to Advanced

	- [LangChain for LLM Application Development – LangChain](https://www.deeplearning.ai/short-courses/langchain-for-llm-application-development/) – 1 hour – Beginner

	- [LangChain: Chat with Your Data – LangChain](https://www.deeplearning.ai/short-courses/langchain-chat-with-your-data/) – 1 hour – Beginner

	- [Finetuning Large Language Models – Lamini](https://www.deeplearning.ai/short-courses/finetuning-large-language-models/) – 1 hour – Intermediate

	- [Large Language Models with Semantic Search – Cohere](https://www.deeplearning.ai/short-courses/large-language-models-semantic-search/) – 1 hour – Beginner

	- [Building Generative AI Applications with Gradio – HuggingFace](https://www.deeplearning.ai/short-courses/building-generative-ai-applications-with-gradio/) – 1 hour – Beginner

	- [Evaluating and Debugging Generative AI Models Using Weights and Biases – W&B](https://www.deeplearning.ai/short-courses/evaluating-debugging-generative-ai/) – 1 hour – Intermediate

	- [How Diffusion Models Work](https://www.deeplearning.ai/short-courses/how-diffusion-models-work/) – 1 hour – Intermediate

	- [Building Applications with Vector Databases – Pinecone](https://www.deeplearning.ai/short-courses/building-applications-vector-databases/) – 1 hour – Beginner

	- [Automated Testing for LLMOps – circleci](https://www.deeplearning.ai/short-courses/automated-testing-llmops/) – 1 hour – Intermediate

	- [LLMOps – Google Cloud](https://www.deeplearning.ai/short-courses/llmops/) – 1 hour – Beginner

	- [Build LLM Apps with LangChain.js – LangChain](https://www.deeplearning.ai/short-courses/build-llm-apps-with-langchain-js/) – 1 hour – Intermediate

	- [Advanced Retrieval for AI with Chroma – Chroma](https://www.deeplearning.ai/short-courses/advanced-retrieval-for-ai/) – 1 hour – Intermediate

	- [Reinforcement Learning from Human Feedback – Google Cloud](https://www.deeplearning.ai/short-courses/reinforcement-learning-from-human-feedback/) – 1 hour – Intermediate

	- [Building and Evaluating Advanced RAG Applications – LlamaIndex](https://www.deeplearning.ai/short-courses/building-evaluating-advanced-rag/) – 1 hour – Beginner

	- [Quality and Safety for LLM Applications – Whylabs](https://www.deeplearning.ai/short-courses/quality-safety-llm-applications/) – 1 hour – Beginner

	- [Vector Databases: from Embeddings to Applications – Weaviate](https://www.deeplearning.ai/short-courses/vector-databases-embeddings-applications/) – 1 hour – Intermediate

	- [Functions, Tools and Agents with LangChain – LangChain](https://www.deeplearning.ai/short-courses/functions-tools-agents-langchain/) – 1 hour – Intermediate

	- [Pair Programming with a Large Language Model – Google](https://www.deeplearning.ai/short-courses/pair-programming-llm/) – 1 hour – Beginner

	- [Understanding and Applying Text Embeddings – Google Cloud](https://www.deeplearning.ai/short-courses/google-cloud-vertex-ai/) – 1 hour – Beginner

	- [How Business Thinkers Can Start Building AI Plugins With Semantic Kernel – Microsoft](https://www.deeplearning.ai/short-courses/microsoft-semantic-kernel/) – 1 hour – Beginner

- 🦜🔗 LangChain Academy

	- [Introduction to LangGraph](https://academy.langchain.com/courses/intro-to-langgraph) – 40 lessons – 4 hours of video content

- Cohere

	- [LLM University by Cohere](https://cohere.com/llmu)

- [Become an AI Developer – DataCamp](https://www.datacamp.com/ai-code-alongs)

	- [Introduction to Large Language Models with GPT & LangChain](https://www.datacamp.com/code-along/introduction-to-large-language-models-gpt-langchain)

	- [Prompt Engineering with GPT & LangChain](https://www.datacamp.com/code-along/prompt-engineering-gpt-langchain)

	- [Building Multimodal AI Applications with LangChain & the OpenAI API](https://www.datacamp.com/code-along/multimodal-ai-applications-langchain-openai-api)

	- [Semantic Search with Pinecone](https://www.datacamp.com/code-along/semantic-search-pinecone)

	- [Retrieval Augmented Generation with OpenAI API & Pinecone](https://www.datacamp.com/code-along/retrieval-augmented-generation-openai-api-pinecone)

	- [Building Chatbots with the OpenAI API and Pinecone](https://www.datacamp.com/code-along/building-chatbots-openai-api-pinecone)

	- [Using Open Source AI Models with Hugging Face](https://www.datacamp.com/code-along/using-open-source-models-hugging-face)

	- [Building NLP Applications with Hugging Face](https://www.datacamp.com/code-along/building-nlp-applications-hugging-face)

	- [Image Classification with Hugging Face](https://www.datacamp.com/code-along/image-classification-hugging-face)

- EdX

	- [Databricks: Large Language Models: Application through Production](https://www.edx.org/learn/computer-science/databricks-large-language-models-application-through-production) – 6 weeks – 4-10 hours per week

	- [Databricks: Large Language Models: Foundation Models from the Ground Up](https://www.edx.org/learn/computer-science/databricks-large-language-models-foundation-models-from-the-ground-up) – 4 weeks – 4-8 hours per week

	- [IBM: Introduction to Generative AI](https://www.edx.org/learn/computer-science/ibm-introduction-to-generative-ai)

	- [IBM: Introduction to Prompt Engineering](https://www.edx.org/learn/artificial-intelligence/ibm-introduction-to-prompt-engineering) – 3 weeks – 1-3 hours per week

	- [IBM: Models and Platforms for Generative AI](https://www.edx.org/learn/artificial-intelligence/ibm-generative-ai-models-and-platforms) – 3 weeks – 1-3 hours per week

	- [IBM: Developing Generative AI Applications with Python](https://www.edx.org/learn/artificial-intelligence/ibm-developing-generative-ai-applications-with-python) – 6 weeks – 1–2 hours per week

- Coursera

	- [Introduction to Large Language Models – Google Cloud](https://www.coursera.org/learn/introduction-to-large-language-models) – Approx. 1 hour – Beginner

	- [Encoder-Decoder Architecture – Google Cloud](https://www.coursera.org/learn/encoder-decoder-architecture) – Approx. 1 hour – Advanced

	- [Build a Chat Application using the PaLM 2 API on Cloud Run – Google Cloud](https://www.coursera.org/projects/googlecloud-build-a-chat-application-using-the-palm-2-api-on-cloud-run-g4pjb) – Project – 90 minutes – Intermediate

	- [Generative AI with Large Language Models – AWS](https://www.coursera.org/learn/generative-ai-with-llms) – Approx. 16 hours – Intermediate

- [Scrimba Courses Library – Artificial Intelligence](https://v2.scrimba.com/t0ai)

	- [Build AI Apps with ChatGPT, DALL-E and GPT-4](https://v2.scrimba.com/build-ai-apps-with-chatgpt-dall-e-and-gpt-4-c01) – 4.6 Hours – Intermediate

	- [Deploy AI apps with Cloudflare](https://v2.scrimba.com/deploy-ai-apps-with-cloudflare-c037) – 50 Minutes – Intermediate

	- [Intro to AI Engineering](https://v2.scrimba.com/intro-to-ai-engineering-c032) – 90 Minutes – Intermediate

	- [Intro to AI Engineering](https://v2.scrimba.com/intro-to-ai-engineering-c032) – 90 Minutes – Intermediate

	- [Intro to Mistral AI](https://v2.scrimba.com/intro-to-mistral-ai-c035) – 84 Minutes – Intermediate

	- [Learn LangChain.js](https://v2.scrimba.com/learn-langchainjs-c02t) – 94 Minutes – Intermediate

	- [Learn OpenAI's Assistants API](https://v2.scrimba.com/learn-openais-assistants-api-c030) – 30 Minutes – Intermediate

	- [Learn to code with AI](https://v2.scrimba.com/learn-to-code-with-ai-c02m) – 4.5 Hours – Beginner

	- [Prompt Engineering for Web Developers](https://v2.scrimba.com/prompt-engineering-for-web-developers-c02o) – 3.1 Hours – Intermediate

- [W&B AI Academy](https://www.wandb.courses/pages/w-b-courses)

	- [RAG++ : From POC to Production](https://www.wandb.courses/courses/rag-in-production) – 75 lessons – 2 hours of video content

	- [Developer's guide to LLM prompting](https://www.wandb.courses/courses/prompting) – 25 lessons – 1 hour of video content

	- [LLM Engineering: Structured Outputs](https://www.wandb.courses/courses/steering-language-models) – 34 lessons — 1 hour of video content

	- [Building LLM-Powered Apps](https://www.wandb.courses/courses/building-llm-powered-apps) – 31 lessons – 2 hours of video content

	- [Training and Fine-tuning Large Language Models (LLMs)](https://www.wandb.courses/courses/training-fine-tuning-LLMs) – 37 lessons – 4 hours of video content

	- [Enterprise Model Management](https://www.wandb.courses/courses/enterprise-model-management) – Cover end-to-end model lifecycle. Include LLM Case Study – 25 lessons – 2.5 hours of video content

- Google Cloud Skills Boost

    - [Introduction to Generative AI Learning Path](https://www.cloudskillsboost.google/paths/118)

		- [01 Introduction to Generative AI](https://www.cloudskillsboost.google/paths/118/course_templates/536) – Introductory

        - [02 Introduction to Large Language Models](https://www.cloudskillsboost.google/paths/118/course_templates/539?locale=en) – 8 hours – Introductory

        - [03 Introduction to Responsible AI](https://www.cloudskillsboost.google/paths/118/course_templates/554?locale=en) – 8 hours – Introductory

        - [04 Generative AI Fundamentals](https://www.cloudskillsboost.google/paths/118/course_templates/556) – 8 hours – Introductory

        - [05 Responsible AI: Applying AI Principles with Google Cloud](https://www.cloudskillsboost.google/paths/118/course_templates/388) – 8 hours – Introductory

	- [Generative AI for Developers Learning Path](https://www.cloudskillsboost.google/paths/183)

		- [01 Introduction to Image Generation](https://www.cloudskillsboost.google/paths/183/course_templates/541) – 8 hours – Introductory

		- [02 Attention Mechanism](https://www.cloudskillsboost.google/paths/183/course_templates/537?locale=en) – 8 hours – Intermediate

		- [03 Encoder-Decoder Architecture](https://www.cloudskillsboost.google/paths/183/course_templates/543) – 8 hours – Intermediate

		- [04 Transformer Models and BERT Model](https://www.cloudskillsboost.google/paths/183/course_templates/538) – 8 hours – Introductory

		- [05 Create Image Captioning Models](https://www.cloudskillsboost.google/paths/183/course_templates/542) – 8 hours – Intermediate

		- [06 Introduction to Generative AI Studio](https://www.cloudskillsboost.google/paths/183/course_templates/552) – 8 hours – Introductory

		- [07 Generative AI Explorer - Vertex AI](https://www.cloudskillsboost.google/paths/183/course_templates/723) – 4 hours 15 minutes – Introductory

		- [08 Explore and Evaluate Models using Model Garden](https://www.cloudskillsboost.google/focuses/71938?parent=catalog&path=183) – 1 hour – Intermediate

		- [09 Prompt Design using PaLM](https://www.cloudskillsboost.google/focuses/71937?parent=catalog&path=183) – 1 hour 30 minutes – Introductory

- [Activeloop](https://learn.activeloop.ai)

	- [LangChain & Vector Databases in Production](https://learn.activeloop.ai/courses/langchain) – 40 hours of learning content

	- [Retrieval Augmented Generation for Production with LangChain & LlamaIndex](https://learn.activeloop.ai/courses/rag) – 1 hour of high-level video content – 25 hours of learning content

	- [Training & Fine-Tuning LLMs for Production](https://learn.activeloop.ai/courses/llms) – 1.5 hrs of high-level video content – 40 hours of learning content

- [Full Stack LLM Bootcamp (Spring 2023)](https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/)

	1. [Learn to Spell: Prompt Engineering](https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/prompt-engineering/)

	2. [LLMOps](https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/llmops/)

	3. [UX for Language User Interfaces](https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/ux-for-luis/)

	4. [Augmented Language Models](https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/augmented-language-models/)

	5. [Launch an LLM App in One Hour](https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/launch-an-llm-app-in-one-hour/)

	6. [LLM Foundations](https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/llm-foundations/)

	7. [Project Walkthrough: askFSDL](https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/askfsdl-walkthrough/)

	8. [What's Next?](https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/whats-next/)

	9. [Reza Shabani: How To Train Your Own LLM](https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/shabani-train-your-own/)

	10. [Harrison Chase: Agents](https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/chase-agents/)

	11. [Fireside Chat with Peter Welinder](https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/welinder-fireside-chat/)

- Freecodecamp

	- [Learn LangChain.js - Build LLM apps with JavaScript and OpenAI](https://www.freecodecamp.org/news/learn-langchain-to-link-llms-with-external-data/) [YouTube](https://www.youtube.com/watch?v=HSZ_uaif57o) – Approx. 1 hour 30 minutes

- DAIR.AI

	- [LLMOps: Building Real-World Applications With Large Language Models](https://www.comet.com/site/llm-course/) – Intermediate

	- [Prompt Engineering Course](https://www.promptingguide.ai/)

- The Chinese University of HongKong, Shenzhen

	- [CSC 6201/CIE 6021 Large Language Models](https://llm-course.github.io/) – Slides from 10 lectures

- [NVIDIA – Self-Paced Courses](https://learn.nvidia.com/en-us/training/self-paced-courses)

	- [Generative AI Explained](https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-07+V1) – 2 Hours – Technical - Beginner

	- [Augmenting LLMs using Retrieval Augmented Generation](https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-16+V1) – 1 Hour – Technical - Beginner

	- [Building RAG Agents for LLMs](https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-15+V1) – 8 Hours – Technical - Intermediate

- [Weaviate Academy](https://weaviate.io/developers/academy)

	- [PY_101T: Text data with Weaviate](https://weaviate.io/developers/academy/py/starter_text_data) – Python – Project-based

	- [PY_101V: Your own vectors with Weaviate](https://weaviate.io/developers/academy/py/starter_custom_vectors) – Python – Project-based

	- [PY_101M: Multimodal data with Weaviate](https://weaviate.io/developers/academy/py/starter_multimodal_data) – Python – Project-based

	- [PY_220: Flexible data representation: Named vectors](https://weaviate.io/developers/academy/py/named_vectors) – Python – Project-based

	- [PY_230: Vector indexes](https://weaviate.io/developers/academy/py/vector_index) – Python

	- [PY_250: Vector compression for improved efficiency](https://weaviate.io/developers/academy/py/compression) – Python

	- [PY_275: Text tokenization](https://weaviate.io/developers/academy/py/tokenization) – Python

	- [PY_280: Multi-tenancy](https://weaviate.io/developers/academy/py/multitenancy) – Python

	- [TS_100: Intro to Weaviate with TypeScript (or JavaScript)](https://weaviate.io/developers/academy/js/intro_weaviate_typescript) – TypeScript – Project-based

- [Web Security Academy](https://portswigger.net/web-security) by Portswigger (the creators of Burp Suit)

	- [Web LLM attacks](https://portswigger.net/web-security/llm-attacks) – Short course + 4 labs

- [Neo4j Generative AI Courses](https://graphacademy.neo4j.com/categories/generative-ai/)

	- [Neo4j & LLM Fundamentals](https://graphacademy.neo4j.com/courses/llm-fundamentals/) – 4 Hours

	- [Introduction to Vector Indexes and Unstructured Data](https://graphacademy.neo4j.com/courses/llm-vectors-unstructured/) – 2 Hours

	- [Build a Neo4j-backed Chatbot using Python](https://graphacademy.neo4j.com/courses/llm-chatbot-python/) – 2 Hours - Feat. Langchain and Streamlit

	- [Build a Neo4j-backed Chatbot with TypeScript](https://graphacademy.neo4j.com/courses/llm-chatbot-typescript/) – 6 Hours - Feat. Langchain and Next.js

	- [Building Knowledge Graphs with LLMs](https://graphacademy.neo4j.com/courses/llm-knowledge-graph-construction/) – 2 Hours