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Chris Fregly, Antje Barth, Shelbee Eigenbrode, and Mike Chambers

In Generative AI with Large Language Models (LLMs), you’ll learn the fundamentals of how generative AI works, and how to deploy it in real-world applications.

By taking this course, you'll learn to:

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In Generative AI with Large Language Models (LLMs), you’ll learn the fundamentals of how generative AI works, and how to deploy it in real-world applications.

By taking this course, you'll learn to:

- Deeply understand generative AI, describing the key steps in a typical LLM-based generative AI lifecycle, from data gathering and model selection, to performance evaluation and deployment

- Describe in detail the transformer architecture that powers LLMs, how they’re trained, and how fine-tuning enables LLMs to be adapted to a variety of specific use cases

- Use empirical scaling laws to optimize the model's objective function across dataset size, compute budget, and inference requirements

- Apply state-of-the art training, tuning, inference, tools, and deployment methods to maximize the performance of models within the specific constraints of your project

- Discuss the challenges and opportunities that generative AI creates for businesses after hearing stories from industry researchers and practitioners

Developers who have a good foundational understanding of how LLMs work, as well the best practices behind training and deploying them, will be able to make good decisions for their companies and more quickly build working prototypes. This course will support learners in building practical intuition about how to best utilize this exciting new technology.

This is an intermediate course, so you should have some experience coding in Python to get the most out of it. You should also be familiar with the basics of machine learning, such as supervised and unsupervised learning, loss functions, and splitting data into training, validation, and test sets. If you have taken the Machine Learning Specialization or Deep Learning Specialization from DeepLearning.AI, you’ll be ready to take this course and dive deeper into the fundamentals of generative AI.

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What's inside

Syllabus

Week 1
Generative AI use cases, project lifecycle, and model pre-training
Week 2
Fine-tuning and evaluating large language models
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Week 3
Reinforcement learning and LLM-powered applications

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Teaches the fundamentals of generative AI and how to deploy it in real-world applications
Taught by instructors who are experienced in the field of generative AI
Develops practical intuition about how to best utilize this exciting new technology
Covers the key steps in a typical LLM-based generative AI lifecycle
Examines the challenges and opportunities that generative AI creates for businesses
Suitable for developers with a good foundational understanding of how LLMs work

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Generative AI with Large Language Models with these activities:
Python programming
Refresh Python programming skills to ensure a solid foundation for this course.
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  • Review core concepts such as variables, data types, and control flow.
  • Work through practice problems and exercises.
Review the fundamentals of generative AI
Reviewing the fundamentals of generative AI will help you reinforce your understanding of the core concepts covered in the course.
Browse courses on Generative AI
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  • Read through your notes from previous courses on generative AI and machine learning.
  • Review online resources such as articles, tutorials, and videos on generative AI.
Hands-on with Transformers and Hugging Face
Deepen your understanding of how transformers work by going through interactive tutorials and experimenting with code.
Show steps
  • Set up a Python environment with the Hugging Face Transformers library
  • Follow a hands-on tutorial on fine-tuning a pre-trained LLM using Hugging Face
  • Experiment with different fine-tuning parameters and observe the impact on model performance
Seven other activities
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Show all ten activities
Study Group Discussions on LLM Architectures
Engage in group discussions to clarify your understanding of the technical aspects of LLMs and reinforce key concepts.
Show steps
  • Join or form a study group with fellow learners
  • Review the course material on LLM architectures
  • Discuss the key concepts, including transformer layers, self-attention, and scaling
  • Brainstorm ideas for novel LLM architectures
Follow tutorials on using specific generative AI tools and frameworks
Following tutorials on using specific generative AI tools and frameworks will help you develop practical skills and gain hands-on experience.
Browse courses on Generative AI Tools
Show steps
  • Identify a specific generative AI tool or framework that you want to learn.
  • Search for tutorials on using the tool or framework.
  • Follow the steps in the tutorial to build and deploy a generative AI application.
Practice building and deploying generative AI models
Practicing building and deploying generative AI models will help you develop your problem-solving skills and improve your understanding of the model development process.
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  • Start with a simple generative AI project.
  • Build and deploy the model using your chosen tools and frameworks.
  • Evaluate the performance of the model and make adjustments as needed.
Participate in generative AI competitions and hackathons
Participating in generative AI competitions and hackathons will challenge you to apply your skills and collaborate with others to solve real-world problems.
Show steps
  • Find generative AI competitions and hackathons that are relevant to your interests.
  • Form a team or work on your own.
  • Develop a solution to the competition or hackathon challenge using generative AI techniques.
Create a Generative AI Use Case Presentation
Enhance your communication and presentation skills while exploring the practical applications of generative AI.
Show steps
  • Research and identify a specific industry or business domain where generative AI can create value
  • Develop a use case scenario that demonstrates the potential benefits of generative AI
  • Create a presentation outlining the use case, its implementation, and the expected outcomes
  • Optional: Present your use case to a group or online community
Build a portfolio of generative AI projects
Building a portfolio of generative AI projects will demonstrate your skills and knowledge to potential employers and collaborators.
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Show steps
  • Identify a set of generative AI projects that you want to work on.
  • Build and deploy the projects.
  • Document your projects and showcase them on a website or portfolio platform.
Mentor other students or professionals who are interested in generative AI
Mentoring other students or professionals who are interested in generative AI will help you deepen your understanding of the subject matter and give back to the community.
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  • Identify opportunities to mentor others, such as through online forums or local meetups.
  • Share your knowledge and experience with your mentees.
  • Provide feedback and support to your mentees as they progress in their generative AI journey.

Career center

Learners who complete Generative AI with Large Language Models will develop knowledge and skills that may be useful to these careers:
Project Manager
A Project Manager plans and executes projects. This course may be useful for a Project Manager, as it covers topics like the challenges and opportunities of generative AI, which can be used to improve project management techniques.
Data Scientist
A Data Scientist uses data to solve business problems. This course may be useful for a Data Scientist, as it covers topics like empirical scaling laws and model optimization, which are used in a variety of data science projects.
Machine Learning Engineer
A Machine Learning Engineer designs and builds machine learning models. This course may be useful for a Machine Learning Engineer, as it covers topics like reinforcement learning and LLM-powered applications, which are used in a variety of machine learning projects.
Business Analyst
A Business Analyst analyzes business processes and makes recommendations for improvement. This course may be useful for a Business Analyst, as it covers topics like the challenges and opportunities of generative AI, which can impact business processes.
Software Engineer
A Software Engineer designs, builds, and tests software applications. This course can be useful for a Software Engineer, as it covers topics like training and deploying large language models, which are used in a variety of software applications.
Product Manager
A Product Manager manages the development of a product. This course may be useful for a Product Manager, as it covers topics like evaluating and deploying generative AI models, which are used in a variety of products.
Consultant
A Consultant provides advice to businesses on how to improve their operations. This course may be useful for a Consultant, as it covers topics like the fundamentals of generative AI and best practices for training and deploying models, which can be used to improve business operations.
Professor
A Professor teaches and conducts research at a university. This course may be useful for a Professor, as it covers topics like the fundamentals of generative AI and the latest advancements in the field.
Technical Writer
A Technical Writer creates documentation for technical products. This course may be useful for a Technical Writer, as it covers topics like the fundamentals of generative AI and the latest advancements in the field.
Entrepreneur
An Entrepreneur starts and runs their own business. This course may be useful for an Entrepreneur, as it covers topics like the challenges and opportunities of generative AI, which can be used to create new products and services.
Researcher
A Researcher conducts research in a variety of fields. This course may be useful for a Researcher, as it covers topics like the latest advancements in generative AI and the potential applications of these technologies.
Salesperson
A Salesperson sells products or services to clients. This course may be useful for a Salesperson, as it covers topics like the challenges and opportunities of generative AI, which can be used to improve sales techniques.
Marketer
A Marketer develops and executes marketing campaigns. This course may be useful for a Marketer, as it covers topics like the challenges and opportunities of generative AI, which can be used to improve marketing campaigns.
Freelancer
A Freelancer provides services to clients on a contract basis. This course may be useful for a Freelancer, as it covers topics like the fundamentals of generative AI and best practices for training and deploying models, which can be used to create new products and services.
Student
A Student is pursuing an education at a university or college. This course may be useful for a Student, as it covers topics like the fundamentals of generative AI and the latest advancements in the field.

Reading list

We've selected six books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Generative AI with Large Language Models.
Provides a comprehensive guide to deep learning for NLP, covering a wide range of topics relevant to LLMs.
Serves as a prerequisite for understanding the underlying principles of deep learning, which is essential for comprehending LLMs.
Covers the fundamentosl of natural language processing, which key application area for LLMs.

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