Jump Start in Artificial Intelligence, Machine Learning and Deep Learning
About This Course
Unlock the transformative potential of Artificial Intelligence (AI) with this hands-on course designed specifically for non-technical professionals and business executives. Gain a foundational understanding of AI, Machine Learning (ML), and Deep Learning (DL) concepts, and learn how these technologies can be applied to industries like finance, healthcare, education, and consulting to create efficiencies, drive innovation, and unlock value.
Participants will explore core AI techniques like Machine Learning, Deep Learning, Generative AI (Gen AI) and Reinforcement Learning; experiencing first-hand the power of AI models through engaging case studies and real-world anecdotes. They will also be exposed to the most recent developments in AI through hands-on activities with Large Language Model (LLMs) and learn about their use cases in industry. Using no-code AI platforms from providers like Google Cloud, Amazon Web Services (AWS), and BigML, learners will participate in practical activities to apply these techniques to solve industry-specific problems. Live demonstrations and hands-on labs will bring key concepts to life, while interactive visualisations will demystify how AI algorithms operate.
This course requires no prior programming experience and aims to empower professionals with the knowledge and tools to make informed decisions about AI adoption and its applications in their organisations.
What You'll Learn
▪ Understand the capabilities and limitations of Artificial Intelligence (AI) and Machine Learning technologies, including their applications in domains like healthcare, finance, public policy and education
▪ Communicate AI and Machine Learning insights effectively to support decision-making in non-technical and business contexts
▪ Develop a conceptual understanding of Data Science, Artificial Intelligence (AI), and Machine Learning
▪ Learn key AI and Machine Learning techniques like clustering, regression, classification, and reinforcement learning through a practical, case-based approach