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Learn Large Language Models online: university courses, full YouTube courses, and courses with free certificates, from the IITs, MIT, Harvard, Google, Microsoft and more.
DeepLearning.AI
Become an AI power user in this new course taught by Andrew Ng. From finding information to building apps, you'll develop the prompting skills that get real, useful results from today's most powerful AI models.
DeepLearning.AI
Learn how to use generative AI's capabilities & limitations. Get an overview of real-world examples, and impact on business & society for effective strategies.
Udacity
This course introduces the fundamentals of LangChain, including LangChain Expression Language (LCEL) and building a basic chatbot program. From there, you'll learn to leverage LangChain’s prompt templates and utilize output parsers to generate high-quality, structured outputs from large language mo…
DeepLearning.AI
Learn Python programming with AI assistance. Gain skills writing, testing, and debugging code efficiently, and create real-world AI applications.
Anthropic
Build AI apps that access tools, data, and prompts using the Model Context Protocol.
DeepLearning.AI
Knowledge graphs are used in development to structure complex data relationships, drive intelligent search functionality, and build powerful AI applications that can reason over different data types. Knowledge graphs can connect data from both structured and unstructured sources (databases, documen…
Anthropic
Explore, build, and refine codebases with Claude Code.
Anthropic
Learn how an AI Assistant is built to use and accomplish tasks on computers.
DeepLearning.AI
Go beyond the chat box. Use API access to leverage LLMs into your own applications, and learn to build a custom chatbot. In ChatGPT Prompt Engineering for Developers, you will learn how to use a large language model (LLM) to quickly build new and powerful applications. Using the OpenAI API, you’ll…
Microsoft
Learn about the history of natural language processing (NLP), including how the Transformer architecture revolutionized the field and helped us create large language models (LLMs). Work with LLMs in Azure Machine Learning through the foundation models in the model catalog.
Google
Build and train a 20M-parameter LLM from scratch using JAX, the open-source library behind Google's Gemini, and learn the core techniques powering modern AI development.
Vanderbilt University
Unlock the full potential of generative AI and become a master of prompt engineering. Dive deeper into how you can use In-context Learning to build better and more reliable prompts. See how Retrieval Augmented Generation (RAG) works and what can go wrong that you can counteract with fact-checkable…
OpenAI
Learn to use OpenAI Canvas to write, code, and create more effectively in collaboration with AI.
Udacity
Understand and apply LLMOps principles to large language models. Master the ability to fine-tune, deploy, and monitor applications while implementing AI safety measures across multiple production projects.
Taught by professor Christopher Potts, this professional Stanford Online course draws on theoretical concepts from linguistics, natural language processing, and machine learning. Topics include domain adaptation for supervised sentiment, retrieval augmented in-context learning, advanced behavioral…
Vanderbilt University
In the era of Generative AI, the demand for personalized and specialized Generative AI assistants is skyrocketing. Large language models like GPTs have demonstrated their remarkable capabilities, but what if you could harness their power to create custom AI assistants tailored to your specific need…
OpenAI
Learn the fundamentals of prompt engineering for ChatGPT. Learn effective prompting, and how to use LLMs for summarizing, inferring, transforming, and expanding.
Microsoft
Implement advanced retrieval-augmented generation (RAG) pipelines for production AI agents using Azure AI Search and Microsoft Foundry. Design hybrid search architectures, implement reranking strategies, configure dynamic knowledge source routing, and optimize chunking and embedding strategies.
