Virtual Classroom
Craft effective prompts for Microsoft Copilot for Microsoft 365 (MS-4005)
Craft Effective Prompts for Microsoft Copilot for Microsoft 365 (MS-4005) is a specialized course designed to help users maximize the capabilities of Microsoft Copilot by creating clear, concise, and effective prompts
Virtual Classroom
Copilot for Microsoft 365 User Enablement Specialist (MS-4007)
This course focuses on empowering professionals to leverage Microsoft 365 Copilot to enhance user adoption and enablement. Learn how to utilize AI-driven tools within Microsoft 365 to streamline workflows, improve productivity, and assist users in navigating and maximizing their use of the platform. Ideal for specialists looking to optimize user experiences and accelerate digital transformation within organizations.
Blended Learning
Designing and Implementing a Microsoft Azure AI Solution (AI-102T00)
This course is designed for AI professionals and developers who want to enhance their skills in building AI solutions on the Azure platform. Participants will learn advanced techniques for designing and deploying AI models, leveraging Azure Cognitive Services, Azure Machine Learning, and other AI technologies.
Virtual Classroom
Design a dream destination with AI (AI-3024)
Learn the art of AI-Powered Destination Designing with our AI-3024 Course. Use AI to visualize, plan, and brand unique destinations with our expert-led, interactive training.
Virtual Classroom
Copilot Foundations (AI-3018)
Copilot Foundations introduces participants to the basics of generative AI and language models, focusing on creating, testing, and managing intelligent AI copilots. Through hands-on labs using Microsoft Copilot and Azure AI Studio, learners will explore effective prompting techniques, develop custom copilot solutions using Retrieval Augmented Generation (RAG) with their own data, and gain practical experience in managing AI projects and resources.
On Demand
From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase
Naive Bayes, K-nearest neighbours, Support Vector Machines, Artificial Neural Networks, K-means, Hierarchical clustering, Principal Components Analysis, Linear regression, Logistics regression, Random variables, Bayes theorem, Bias-variance tradeoff