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Are you looking to immerse yourself in the exciting world of generative ai?
As you no doubt know, generative ai is the big thing right now and it dates back about a year to the launch of ChatGPT. Since then, generative ai has taken not only technology by storm but the entire world. The skills are in high demand, but since the specialization is young and constantly changing, keeping up with the latest advances is especially important for this nascent field.
Whether you’re a beginner in the broader field of artificial intelligence or looking to enhance your existing skills, there are numerous free courses available to help you master this cutting-edge technology. Here is a list of five such courses that can boost or enhance your journey towards generative ai.
This comprehensive 12-lesson course from Microsoft teaches the fundamentals of building generative ai applications. Each lesson includes a video introduction, written material, Jupyter Notebooks with code examples, challenges, and additional resources. It will cover topics such as understanding generative ai and large language models, rapid engineering, building various applications, and designing user experiences for ai applications.
Course link: ai-for-beginners” rel=”noopener” target=”_blank”>Generative ai for beginners
This Databricks course provides foundational knowledge of generative ai, including LLM, through four videos. It covers various aspects of generative ai, such as applications, success strategies, and potential risks and challenges. After completing the course and passing a knowledge test, you can earn a badge to share on your LinkedIn profile or resume.
Course link: ai-fundamentals” rel=”noopener” target=”_blank”>Fundamentals of Generative ai
This Google Cloud Skills Boost introductory-level microlearning course provides an overview of generative ai concepts, exploring large language models, responsible ai principles, and Google tools for developing your own Gen ai applications. Courses include Introduction to Generative ai, Introduction to Large Language Models, Introduction to Responsible ai, Fundamentals of Generative ai, and Responsible ai: Applying ai Principles with Google Cloud. Earn badges upon completion.
Course link: Introduction to the Generative ai Learning Path
This AWS course offers a comprehensive understanding of generative ai, focusing on the LLM-based ai lifecycle, transformer architecture, model optimization, and practical implementation methods. It is designed for developers with basic LLM knowledge and provides information on best practices for training and deploying these models effectively. Previous experience in Python and machine learning basics are prerequisites, making this an intermediate-level course.
Course link: ai-with-llms” rel=”noopener” target=”_blank”>Generative ai with large language models
Generative ai for Everyone, presented by Deeplearning.ai and taught by ai expert Andrew Ng, focuses on understanding and applying generative ai in various contexts. The course covers the fundamentals of how generative ai works, its capabilities and limitations, and includes practical exercises in rapid engineering and advanced ai applications. Participants will explore real-world applications, participate in generative ai projects, and understand its impact on business and society. It aims to equip students with knowledge about the life cycle of ai projects, potential opportunities and risks associated with generative ai technologies.
Course link: ai-for-everyone” rel=”noopener” target=”_blank”>Generative ai for everyone
These courses provide a number of great starting points for anyone interested in learning and mastering generative ai. They offer practical insights, fundamental knowledge, and hands-on experience in developing and deploying ai applications. As you progress, remember to apply your newly acquired knowledge by working on projects and creating a portfolio that showcases your skills and creativity in this rapidly evolving field.
Good luck with your studies!
Matthew May (@mattmayo13) has a master’s degree in computer science and a postgraduate diploma in data mining. As Editor-in-Chief of KDnuggets, Matthew aims to make complex data science concepts accessible. His professional interests include natural language processing, machine learning algorithms, and exploring emerging ai. He is driven by the mission to democratize knowledge in the data science community. Matthew has been coding since he was 6 years old.
