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There are 2 modules in this course
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.
Designed for data scientists, ML engineers, and AI enthusiasts, you’ll learn to differentiate between various generative AI architectures and models, such as recurrent neural networks (RNNs), transformers, generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models. You’ll also discover how LLMs, such as generative pretrained transformers (GPT) and bidirectional encoder representations from transformers (BERT), power real-world language tasks.
Get hands-on with tokenization techniques using NLTK, spaCy, and Hugging Face, and build efficient data pipelines with PyTorch data loaders to prepare models for training.
A basic understanding of Python, PyTorch, and familiarity with machine learning and neural networks are helpful but not mandatory. Enroll today and get ready to launch your journey into generative AI!
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.
IBM Product Spotlight: watsonx.governance•2 minutes
Course Overview•10 minutes
Summary and Highlights•3 minutes
2 assignments•Total 25 minutes
Graded Quiz: Generative AI Architecture•15 minutes
Practice Quiz: Generative AI Overview and Architecture•10 minutes
1 app item•Total 60 minutes
Lab: Exploring Generative AI Libraries•60 minutes
3 plugins•Total 32 minutes
Helpful Tips for Course Completion•2 minutes
Reading: Basics of AI Hallucinations•10 minutes
Reading: Overview of Libraries and Tools•20 minutes
Data Preparation for LLMs
Module 2•3 hours to complete
Module details
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.
At IBM, we know how rapidly tech evolves and recognize the crucial need for businesses and professionals to build job-ready, hands-on skills quickly. As a market-leading tech innovator, we’re committed to helping you thrive in this dynamic landscape. Through IBM Skills Network, our expertly designed training programs in AI, software development, cybersecurity, data science, business management, and more, provide the essential skills you need to secure your first job, advance your career, or drive business success. Whether you’re upskilling yourself or your team, our courses, Specializations, and Professional Certificates build the technical expertise that ensures you, and your organization, excel in a competitive world.
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Learner reviews
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4·
Reviewed on Mar 24, 2025
Too fast reading of the slides without much of explanations.
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SH
4·
Reviewed on Jul 22, 2025
his course is sufficient to introduce the different architectures of LLMs and enable you to prepare data for training models.
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4·
Reviewed on Nov 11, 2025
Labs could have been made a little more lucid and comprehensive with comments for unusual syntaxes and appropriate visuals for the subject matter. Great course, regardless.
How long does it take to complete the Specialization?
It will take only two weeks to complete this course if you spend two hours of study time per week.
Do I need any background knowledge to complete this course successfully?
It will be good if you have a basic knowledge of Python and PyTorch and a familiarity with machine learning and neural network concepts.
Which roles can I perform after completing this course?
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.
Do I need any specific software or tools to complete the course successfully?
Only a modern web browser is required to complete this course and all hands-on labs.
You will be provided access to cloud-based environments to complete the labs at no charge.
You will sign up for platforms such as Hugging Face and use functionalities that are not charged.
When will I have access to the lectures and assignments?
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Certificate?
When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.