Artificial Intelligence

Sharon ZhouDeepLearning.AI

Join our new short course, Finetuning Large Language Models! Learn from Sharon Zhou, Co-Founder and CEO of Lamini, and instructor for the GANs Specialization and How Diffusion Models Work. When you complete this course, you will be able to understand when to apply finetuning on LLMs, prepare your data for finetuning, and train and evaluate an LLM on your data. Finetuning allows the model to learn style, form, and can update the model with new knowledge to improve results.

Krish NaikUdemy

This course provides a detailed exploration of generative AI, focusing on the integration of Langchain and Huggingface models. Participants will learn to create advanced AI applications, understand architecture and design patterns, and gain hands-on experience in deploying models across various environments. The course covers deployment strategies, RAG pipelines, and model optimization, ensuring learners can build scalable and reliable AI systems.

Elie SchoppikDeepLearning.AI

In this course, you will explore the Model Context Protocol (MCP) and its role in standardizing AI application development. Learn to build and deploy MCP servers, create MCP-compatible applications, and connect AI applications to external systems. By the end, you'll be equipped to develop AI applications with rich context and minimal integration work.

Isa FulfordDeepLearning.AI

In Building Systems with the ChatGPT API, you will learn how to automate complex workflows using chain calls to a large language model. You will build chains of prompts that interact with the completions of prior prompts, systems where Python code interacts with both completions and new prompts, and a customer service chatbot using all the techniques from this course. This course is beginner-friendly and includes hands-on examples with built-in Jupyter notebooks for seamless experimentation.

Dr. Greg LoughnaneMaven

In this comprehensive bootcamp, you will learn to prototype LLM applications using best practices and the four primary patterns of Generative AI. The course covers prompt engineering, retrieval augmented generation, fine-tuning, and agent development. You'll build applications from quick prototypes to scalable production systems, using open-source LLMs and embedding models. The program also includes visibility and monitoring tools, ensuring you can evaluate and optimize your applications effectively.

Heidi CulbertsonMaven

This course provides a deep understanding of the longevity landscape and older adult demographic. You will learn foundational knowledge of the age 50+ demographic, how to approach product usability for specific audiences, and a system for ideation to production. The course also covers an age-inclusive framework, AI-enabled tasks for persona and product personalization, and best practices for conversational AI design.

Amit SanganiDeepLearning.AI

In this course, you will learn best practices for prompting and selecting among the Llama 2 & 3 models by using them as a personal assistant to help you complete day-to-day tasks. You will experiment with advanced prompt engineering techniques, like few-shot prompting to get Llama 2 to classify the sentiment of text messages, and chain-of-thought prompting to solve logic problems. You will also treat Code Llama as a pair programming partner to both learn to write and improve code. Additionally, you will promote safe and responsible use of LLMs by having Llama Guard check user prompts and model responses for harmful content.

Manisha AroraMaven

This course offers a dynamic blend of technical expertise and real-world business challenges. Through a series of interactive sessions, discussions, and hands-on projects, you will learn how to scope machine learning projects effectively, lead discussions with stakeholders, navigate the entire data science workflow from data exploration to model deployment, and communicate project insights and business impact to stakeholders.