AI Courseware
Artificial intelligence (AI) is transforming industries by empowering machines to learn, adapt, and perform tasks traditionally requiring human intelligence. Offering AI training is essential for organizations aiming to equip developers, data scientists, and technical teams with the skills needed to innovate and solve complex challenges.
Our AI courseware provides training companies and educators with a comprehensive curriculum designed to teach practical AI skills. Covering topics such as generative AI, document intelligence, natural language processing, and AI-assisted coding, our materials ensure learners gain hands-on experience with cutting-edge tools and techniques. The content includes foundational AI concepts, advanced applications, and real-world use cases to prepare learners for today’s AI-driven development landscape.
License our AI courseware to deliver impactful training that empowers your clients to harness AI technologies, enhance productivity, and stay competitive in the evolving tech industry.
AI-Assisted Coding: Boosting Developer Productivity and Efficiency (AIAC101)
3 days
AI-Assisted Coding: Boosting Developer Productivity and Efficiency is a hands-on course for developers and technical teams who want to integrate AI tools like ChatGPT into their everyday coding workflows. Participants learn how to use large language models to write, debug, optimize, and explain code more efficiently while understanding the capabilities, limitations, and responsible use of AI in software development.
Through practical, guided exercises, learners apply AI-assisted techniques across HTML, CSS, JavaScript, SQL, XML, and Python, focusing on real-world tasks such as refactoring, data handling, automation, and problem solving. This course is ideal for organizations and individuals seeking to improve developer …
AI Prompting-Copilot (TOS-PRCO)
1 day
This Copilot training course delivers a technically grounded introduction to GPT-based AI systems designed for enterprise and professional environments, with emphasis on model architecture concepts, generative AI capabilities, and real-world workflow integration. Participants explore how large language models process and generate text, examine the functional scope and constraints of Copilot Chat, and apply structured prompt-engineering frameworks to optimize output quality, relevance, and consistency. The curriculum extends beyond basic usage to address AI-assisted business communication, knowledge management, and customer service models, highlighting human-in-the-loop collaboration, quality control mechanisms, and decision-support use cases. A dedicated focus on AI ethics and bias mitigation equips …
Foundations of AI for Creatives (GENAI200)
1 day
Foundations of AI for Creatives is designed for artists, designers, writers, and other creative professionals seeking to understand and apply artificial intelligence in their work. This practical, project-based course introduces the core concepts of AI and generative tools, emphasizing real-world applications in creative workflows. Participants will explore a range of technologies—including text, image, audio, and video generation—while gaining hands-on experience with prompt engineering, content refinement, and multimodal storytelling. The course also examines ethical considerations, accessibility, and the evolving relationship between human creativity and machine intelligence, empowering creatives to navigate this new landscape with clarity, confidence, and intention.
AI in Software Testing (AIST)
2 days
Master the future of quality assurance with AI-powered testing.
This hands-on course introduces software testers, QA professionals, and developers to the practical use of artificial intelligence in modern testing workflows. You’ll learn how to harness large language models (LLMs), such as ChatGPT and GitHub Copilot, to generate, analyze, and maintain test cases with greater speed and precision.
Through a progressive series of labs, you’ll explore real-world techniques for AI-assisted test creation, legacy code analysis, code coverage improvement, exploratory testing, synthetic data generation, and much more. You’ll also tackle the unique challenges of testing AI systems themselves, manage flaky tests, and …
