
Learn how generative AI learns patterns from data to create new text, images, and content, and explore applications in content creation, art, healthcare, gaming, personalization, and data augmentation.
Demonstrate popular AI tools—Dall-E, Midjourney, and ChatGPT—showing text-to-image prompts and Python code generation for data from Wikipedia.
Explore foundational concepts of artificial intelligence, machine learning, and deep learning, including labeled data and neural networks. Compare discriminative and generative models, with examples of image synthesis and text generation.
Define generative AI and describe its key characteristics, with examples like GPT and Delhi, and explore tasks from text and image generation to audio and video creation.
Define large language models as AI systems that understand and generate human language, trained on massive text data, and highlight examples such as GPT, Bert, Llama, Claude, Gemini, and Mistral.
Assess the ethical implications of generative ai, including misinformation, bias and discrimination, privacy, and job displacement, and learn responsible practices to protect users and data.
Explore how to integrate generative ai with existing systems, manage data quality and security, and address workforce reskilling while guiding adoption across teams for a smooth, productive rollout.
Identify key generative ai use cases, such as content creation, customer support, and personalized marketing, and select tools and teams to measure roi with time saved and satisfaction.
Rag blends generation from llms with retrieval from an external knowledge base, using retrieved passages as context for more accurate, up-to-date answers.
Explore how graph rag extends retrieval augmented generation by leveraging Neo4j's graph database and relationships for context-rich knowledge retrieval.
Examine the high level architecture of generative models, including GANs, VAEs, and GPT, and how generators, discriminators, encoders, and decoders learn via backpropagation on large, diverse data.
Unlock the potential of Generative AI with this comprehensive course, designed to provide leaders and professionals with a solid foundation in AI technology. This course breaks down essential concepts in Generative AI, covering the basics of Machine Learning (ML) and Deep Learning (DL) that underpin AI models. You’ll gain insights into the architecture behind GenAI, with a focus on Large Language Models (LLMs) and how they’re designed to understand and generate human-like language.
We explore powerful tools like ChatGPT, DALL-E, and Midjourney, delving into how they work and how they can be applied to real-world problems. Additionally, the course addresses the Retrieval-Augmented Generation (RAG) architecture, showing how it enhances AI responses by connecting LLMs to external data sources for contextually accurate results.
Beyond technical knowledge, this course also emphasizes the ethical implications of GenAI, such as handling bias, privacy concerns, and potential misinformation. By the end of this course, you’ll understand the opportunities and challenges of deploying Generative AI responsibly.
Whether you're new to AI or an experienced professional, this course equips you with practical insights and the knowledge needed to leverage Generative AI effectively in your organization. Join us to get started with GenAI and gain a competitive edge in today’s AI-driven landscape!