Are you looking to dive into the exciting world of generative AI?
As you undoubtedly know, generative AI is the biggest thing going right now, and has been stretching back about a year to the launch of ChatGPT. Generative AI has taken not just tech but the entire world by storm ever since. The skills are in high demand, but given that the specialization is young and constantly changing, staying up on the latest developments is especially important for the nascent field.
Whether you’re a beginner in the wider 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’s a list of five such courses that can kickstart or elevate your journey in generative AI.
1. Generative AI for Beginners
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. You’ll cover topics like understanding generative AI and Large Language Models, prompt engineering, building various applications, and designing user experiences for AI applications.
Course link: Generative AI for Beginners
2. Generative AI Fundamentals
This Databricks course provides foundational knowledge of generative AI, including LLMs, 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: Generative AI Fundamentals
3. Introduction to Generative AI Learning Path
This introductory level micro-learning course by Google Cloud Skills Boost 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, Generative AI Fundamentals, and Responsible AI: Applying AI Principles with Google Cloud. Earn badges upon completion.
Course link: Introduction to Generative AI Learning Path
4. Generative AI with Large Language Models
This AWS course offers a comprehensive understanding of generative AI, focusing on LLM-based AI lifecycle, transformer architecture, model optimization, and practical deployment methods. It is designed for developers with foundational knowledge in LLMs, providing insights into best practices for training and deploying these models effectively. Prior experience in Python and basic machine learning concepts are prerequisites, making it an intermediate-level course.
Course link: Generative AI with Large Language Models
5. Generative AI for Everyone
Generative AI for Everyone, brought to you 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 functions, its capabilities and limitations, and includes practical exercises in prompt engineering and advanced AI applications. Participants will explore real-world applications, engage in generative AI projects, and understand its impact on business and society. It aims to equip learners with knowledge about the lifecycle of AI projects, potential opportunities, and risks associated with generative AI technologies.
Course link: Generative AI for Everyone
Wrapping Up
These courses provide a series of great starting points for anyone interested in learning about, and mastering, generative AI. They offer practical insights, foundational 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 building a portfolio that showcases your skills and creativity in this rapidly evolving field.
Best of luck with your studies!
Matthew Mayo (@mattmayo13) holds a Master's degree in computer science and a graduate 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 a mission to democratize knowledge in the data science community. Matthew has been coding since he was 6 years old.
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