on creating new content like text, images, and code using advanced machine learning models. Courses range from beginner-friendly introductions for all professionals to advanced programs for seasoned developers.
Here are five key aspects of a generative AI course:
- Fundamental concepts and core models: The curriculum starts by explaining what generative AI is and how it differs from traditional, discriminative AI. You will cover core model architectures, such as:
- Generative Adversarial Networks (GANs): Two neural networks that compete to create realistic images.
- Variational Autoencoders (VAEs): Models that learn a compressed representation of data to generate new examples.
- Transformers: Powerful models that use an "attention mechanism" to understand relationships in data, enabling tasks like natural language processing (NLP) and code generation.
- Practical skills in prompt engineering: A key component is learning how to craft effective inputs (prompts) to get the best results from Large Language Models (LLMs) and other generative tools. Courses teach techniques like few-shot, zero-shot, and persona-based prompting to control model behavior and refine outputs.
- Real-world application development: Courses emphasize hands-on experience through coding labs and projects, with a focus on building functional applications. Examples of projects include creating a conversational chatbot using Retrieval-Augmented Generation (RAG) to connect an LLM to external data, or developing a custom image generator.
- Advanced techniques and frameworks: For those with a technical background, courses dive into sophisticated topics for customizing AI models. These may include:
- Fine-tuning LLMs: Adapting a pre-trained model for a specific task using a smaller, curated dataset.
- AI Agentic Frameworks: Building systems with autonomous AI agents that can perform complex, multi-step tasks.
- Integration with platforms and tools: Working with practical tools and platforms like LangChain, Hugging Face, and cloud services from AWS or Google.
- Ethical considerations and responsible AI practices: A course addresses the responsible development and deployment of generative AI. It covers important topics such as data privacy, bias in AI outputs, security risks, and the need for transparency. These lessons ensure learners understand the societal impact of the technology and how to mitigate potential risks.