According to @DeepLearningAI on Twitter, a new course titled 'Design, Develop, and Deploy Multi-Agent Systems with CrewAI' is now available on Coursera, taught by @joaomdmoura, Co-Founder and CEO of CrewAIInc. This course provides AI professionals with practical training to build collaborative AI agents equipped with advanced features such as tool integration, memory management, and guardrails, enabling scalable handling of real-world workflows. The course addresses the growing demand for AI solutions that automate and optimize complex business processes, highlighting immediate business applications and workflow automation opportunities with multi-agent systems (Source: @DeepLearningAI, Nov 18, 2025).

According to @AndrewYNg, a new course titled 'Design, Develop, and Deploy Multi-Agent Systems with CrewAI' is now available, taught by @joaomdmoura, Co-founder and CEO of CrewAIInc (source: Andrew Ng on Twitter). This course provides hands-on training in building practical AI multi-agent systems that can automate complex workflows by mimicking human team collaboration. Participants will learn to use CrewAI’s open-source framework to create, coordinate, and deploy AI teams, focusing on agent design, task assignment, and system deployment. The curriculum covers building reliable AI agents with tools and guardrails, developing agent teams for planning and coordination, and deploying production-ready AI systems with monitoring and evaluation. This offering addresses growing industry demand for scalable AI workflow automation and equips professionals with in-demand skills for enterprise AI integration (source: deeplearning.ai course page).

According to DeepLearning.AI on Twitter, their Retrieval Augmented Generation course offers a comprehensive overview of how large language models (LLMs) generate tokens, the root causes of model hallucinations, and the factuality improvements achieved through retrieval-based grounding. The course also analyzes practical tradeoffs such as prompt length, compute costs, and context window limitations, using Together AI’s production-ready tools as case studies. This curriculum addresses real-world enterprise needs for accurate, cost-effective generative AI, providing valuable insights for businesses seeking to deploy advanced retrieval-augmented solutions and optimize AI-driven workflows (source: DeepLearning.AI Twitter, August 28, 2025).

According to Andrew Ng on Twitter, a new short course led by @BanghuaZ, Assistant Professor at the University of Washington and co-founder of Nexusflow, teaches AI professionals how to post-train and customize large language models (LLMs). The course focuses on practical methods for fine-tuning LLMs to follow specific instructions and answer domain-specific questions, a critical step for deploying AI solutions tailored to industry needs. This hands-on approach addresses the increasing demand for customized AI models in sectors like enterprise software, customer service automation, and healthcare, highlighting significant business opportunities for companies that invest in post-training expertise (Source: Andrew Ng on Twitter, July 9, 2025).

According to Andrew Ng (@AndrewYNg), Meta has unveiled a new short course, 'Building with Llama 4,' in partnership with @AIatMeta and taught by @asangani7, Director of Partner Engineering for Meta’s AI team. The course highlights the capabilities of Llama 4, which introduces three new models and incorporates the Mixture-of-Experts (MoE) architecture. This marks a significant advancement in open-source large language models, offering practical guidance for developers and businesses aiming to leverage Llama 4's improved efficiency and scalability. The initiative presents new opportunities for AI-powered product development, customization, and enterprise adoption, especially for organizations seeking robust, cost-effective language model solutions (Source: Andrew Ng, Twitter, June 18, 2025).