Generative AI Foundations (Coursera)

By the end of this course, you will have acquired the knowledge and skills to:

- Grasp the foundational concepts and technical intricacies of Generative AI, including its advantages and limitations.

- Apply Generative AI for code generation, enhancing your programming efficiency and creativity in Python and other languages.

- Master the art of prompt engineering to optimize interactions with AI models like ChatGPT, leading to improved outcomes in code generation and beyond.

- Utilize ChatGPT for learning and mastering Python, data science, and software development practices, thereby broadening your technical skill set.

- Explore the revolutionary fields of Autoencoders and Generative Adversarial Networks (GANs), understanding their architecture, operation, and applications.

- Dive into the world of language models and transformer-based generative models, gaining insights into their mechanisms, applications, and impact on the future of AI.

This course is meticulously crafted to cater to a broad audience, including software developers, data scientists, AI enthusiasts, and professionals seeking to leverage Generative AI technologies for innovative solutions.

While prior knowledge of Generative AI Fundamentals or Python Coding is helpful, but it is not a prerequisite to complete the course.

Whether you're looking to enhance your existing skills or embark on a new career path in the field of AI, this course will provide you with the knowledge, practical skills, and confidence to succeed. Join us on this exciting journey into the world of Generative AI!

This course is part of the Learn Generative AI with LLMs Specialization.

What you'll learn

Master Generative AI concepts, apply them in code generation and gain expertise in advanced models like Autoencoders and GANs.

Syllabus

Gen AI Foundations

This module is designed to equip learners with a solid understanding of Generative AI principles, models, and applications, setting the stage for more advanced exploration. Through engaging lessons that include videos on the overview of Generative AI, its principles, understanding its models, and the advantages and disadvantages, along with practical applications like code generation and prompt engineering, participants will gain valuable insights. This module also emphasizes ethical considerations and includes practice assignments and discussion prompts to encourage active learning and application of concepts. Whether you're new to AI or looking to enhance your understanding of Generative AI's capabilities, this module provides the essential knowledge base to start your journey.

Autoencoders and GANs

This module is crafted to provide an in-depth understanding of how these models function, their architectural nuances, and their wide array of applications in the tech industry. Starting with the basics of Autoencoders, learners will explore the workings and variations of these networks, including Variational Autoencoders (VAEs), and understand their significance in data compression and generative tasks. The journey continues with an exploration of GANs, from their foundational architecture to the nuances of training and the exploration of their diverse variants. Through practical assignments, engaging video content, and focused readings, participants will gain hands-on experience working with these models, culminating in a deeper comprehension of their capabilities and limitations.

Language Models and Transformer-based Generative Models

This module provides an in-depth exploration of Language Models and Transformer-based Generative Models, foundational elements in natural language processing and artificial intelligence. Starting with an overview of language models, it progresses to cover the revolutionary transformer architecture, detailing its attention mechanism and various advanced models. The module then shifts focus to groundbreaking models such as GPT and BERT, examining their development, capabilities, and the wide array of applications they enable in the AI domain. Concluding with comprehensive assessments, including practice and graded assignments on cutting-edge topics like VAEs and GANs, the module offers a holistic understanding of how these technologies drive innovation in AI research and applications.

Course Wrap-up and Assessment

This final module is designed to consolidate the knowledge and skills learners have acquired throughout the course. It starts with a Practice Project, encouraging learners to apply their understanding in a hands-on manner, thus bridging the gap between theoretical knowledge and practical application. Following this, the module offers a Graded Assignment on Gen AI Fundamentals, aimed at rigorously evaluating the learners' grasp of the key concepts, techniques, and applications explored in the course.