Top 7 Generative AI Courses by Andrew Ng
These courses are free and can typically be completed within one to two hours.
When it comes to courses that actually add value to your career, Andrew Ng, founder and CEO of DeepLearning.AI, is the first and most credible name that pops into our mind. His courses are comprehensive, covering an extensive range of topics in generative AI, including diffusion models, generative adversarial networks (GANs), and variational autoencoders (VAEs). What sets his offerings apart is the collaborative effort with industry giants such as AWS and OpenAI, which further reinforces their credibility.
Remarkably, these courses are provided completely free of cost and can typically be completed within one to two hours, making them easily accessible and time-efficient.
Understanding the importance of generative AI at this hour, we have curated a concise list of 7 courses provided by him in this space.
Generative AI with Large Language Models: Enrol in this course to gain a comprehensive understanding of generative AI’s lifecycle using LLMs and the underlying transformer architecture. Learn effective utilisation of LLMs for diverse tasks, model selection, and appropriate training techniques. Instructors include Andrew Ng, Antje Barth (principal developer advocate at AWS), Chris Fregly (principal solutions architect at AWS), Shelbee Eigenbrode (principal solutions architect at AWS), and Mike Chambers (developer advocate at AWS).
LangChain: Chat With Your Data!: In the latest addition to Ng’s courses and instructed by Harrison Chase, co-founder and CEO of LangChain, the course focuses on Retrieval Augmented Generation (RAG) and advanced chatbot building. Topics that will be covered include document loading, splitting, vector stores, retrieval techniques, question answering, and chatbot development. Python developers keen on leveraging these technologies will find the course valuable.
LangChain for LLM Application Development: This is another course with Chase that will teach you how to apply LLMs to data, build personalised assistants and chatbots, and utilise agents, chained calls, and memories for enhanced LLM utility. The course covers models, prompts, parsers, memory implementation, chain construction, and leveraging LLMs for question answering over documents. It also explores LLMs as reasoning agents. It is a one-hour beginner-friendly program that requires basic Python knowledge.
How Diffusion Models Work: This intermediate-level course taught by Sharon Zhou, CEO and co-founder of Lamini explores how to build and optimise diffusion models. In the course, you will learn about the diffusion process, how to build neural networks for noise prediction, and how to enhance image generation with contextual information. You will also gain practical coding skills and hands-on experience with creating personalised diffusion models. By the end of the course, you will have a solid foundation in diffusion models and be able to explore them for your own applications. Prior knowledge of Python, TensorFlow, or PyTorch is recommended.
ChatGPT Prompt Engineering for Developers: DeepLearning.AI collaborated with generative AI leader OpenAI for this course where Isabella Fulford, a technical staff member at the ladder, will take you through how to use LLMs effectively to build powerful applications. The course covers prompt engineering, LLM API usage for summarisation, inference, text transformation, and expansion. It also emphasises crafting successful prompts, systematic prompt development, and creating personalised chatbots.
Building Systems with the ChatGPT API: Again in partnership with OpenAI and co-led by Fulford and Ng, this program teaches the automation of complex workflows using chain calls to a powerful language model. Topics covered include interactive prompts, Python code utilisation, and customer service chatbot development. Practical applications encompass query classification, safety evaluation, and multi-step reasoning. The course offers hands-on examples, Jupyter notebooks, and follows beginner-friendly practices for maximising LLM model performance responsibly. Suitable for those with basic Python knowledge and ML engineers seeking prompt engineering skills for LLMs.
Mathematics for Machine Learning and Data Science Specialisation: Even before generative AI became a buzz word and understanding the importance of mathematics in this sector, Ng introduced this course to give people an intuitive understanding of AI’s most crucial maths concepts like linear algebra, calculus, and probability. Led by Luis Serrano and co-created by Ng alongside Anshuman Singh, Magdalena Bouza, and Elena Sanina.
Besides these short courses, Ng also provides specialised courses on more specific topics like AI for medicine, TensorFlow practices, ethical AI and more. You can find more information about them here.