Ready to explore the exciting world of generative AI and large language models (LLMs)? This IBM course, part of the Generative AI Engineering Essentials with LLMs Professional Certificate, gives you practical skills to harness AI to transform industries.
Generative AI and LLMs: Architecture and Data Preparation
Early bird sale! Unlock 10,000+ courses from Google, Microsoft, and more for £160/year. Save now.
Generative AI and LLMs: Architecture and Data Preparation
This course is part of multiple programs.
Instructors: Joseph Santarcangelo +1 more
21,534 already enrolled
Included with 
Gain insight into a topic and learn the fundamentals.
4.7
(217 reviews)
Intermediate level
Recommended experience
5 hours to complete
3 weeks at 1 hour a week
Flexible schedule
Learn at your own pace
What you'll learn
Differentiate between generative AI architectures and models, such as RNNs, transformers, VAEs, GANs, and diffusion models
Describe how LLMs, such as GPT, BERT, BART, and T5, are applied in natural language processing tasks
Implement tokenization to preprocess raw text using NLP libraries like NLTK, spaCy, BertTokenizer, and XLNetTokenizer
Create an NLP data loader in PyTorch that handles tokenization, numericalization, and padding for text datasets
Skills you'll gain
- Category: Artificial Neural Networks
- Category: Artificial Intelligence and Machine Learning (AI/ML)
- Category: Generative AI
- Category: Natural Language Processing
- Category: Deep Learning
- Category: Data Processing
- Category: Large Language Modeling
- Category: PyTorch (Machine Learning Library)
- Category: Text Mining
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
4 assignments
Taught in English
Build your subject-matter expertise
This course is available as part of
When you enroll in this course, you'll also be asked to select a specific program.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
There are 2 modules in this course
In this module, you will learn about the significance of generative AI and how it is transforming various fields through content generation, code creation, and image synthesis. You will explore key generative AI architectures, such as generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, and transformers, and understand the differences in their training approaches. You’ll also examine how large language models (LLMs) like generative pretrained transformers (GPT) and bidirectional encoder representations from transformers (BERT) are applied in building NLP-based applications. Finally, through a hands-on lab, you will create a simple chatbot using the Hugging Face transformers library and get introduced to essential tools and libraries used in generative AI development.
What's included
5 videos2 readings2 assignments1 app item3 plugins
In this module, you will learn how to prepare data for training large language models (LLMs) by implementing tokenization and building data loaders. You will explore different tokenization methods and understand how tokenizers convert raw text into model-ready input. You will implement tokenization using libraries such as NLTK, spaCy, BertTokenizer, and XLNetTokenizer. Additionally, you will learn the role of data loaders in the training pipeline and use the DataLoader class in PyTorch to create a data loader with a custom collate function that processes batches of text. These practical skills are essential for building efficient NLP pipelines for LLM training. In addition, supporting materials, such as a cheat sheet and glossary, will reinforce your learning.
What's included
2 videos5 readings2 assignments2 app items2 plugins
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Instructors
Instructor ratings
4.5 (46 ratings)Joseph Santarcangelo
IBM
35 Courses1,982,745 learners
Offered by
IBM
Why people choose Coursera for their career
Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."
Learner reviews
4.7
217 reviews
- 5 stars
77.47%
- 4 stars
15.31%
- 3 stars
4.95%
- 2 stars
0.90%
- 1 star
1.35%
Showing 3 of 217
JR
5
Reviewed on Mar 1, 2025
Was waiting for a course like this for a long time. Very happy with it. Library installation on labs seems a bit slow
BB
4
Reviewed on Mar 25, 2025
Too fast reading of the slides without much of explanations.
MA
5
Reviewed on Jan 3, 2025
It was very informative and I enjoyed the journey I learned the patterns from the deep.
Frequently asked questions
It will take only two weeks to complete this course if you spend two hours of study time per week.
It will be good if you have a basic knowledge of Python and PyTorch and a familiarity with machine learning and neural network concepts.
This course is part of a specialization. When you complete the specialization, you will prepare yourself with the skills and confidence to take on jobs such as AI Engineer, NLP Engineer, Machine Learning Engineer, Deep Learning Engineer, and Data Scientist.