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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.

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.

Generative AI and LLMs: Architecture and Data Preparation

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Generative AI and LLMs: Architecture and Data Preparation

This course is part of multiple programs.

Instructors: Joseph Santarcangelo +1 more

57,996 already enrolled

Included with

2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

6 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

6 hours to complete

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: Data Pipelines
Category: Data Preprocessing
Category: Recurrent Neural Networks (RNNs)
Category: LLM Application
Category: Large Language Modeling
Category: Generative Model Architectures
Category: Model Training
Category: Natural Language Processing

Tools you'll learn

Category: Generative Adversarial Networks (GANs)
Category: PyTorch (Machine Learning Library)
Category: Hugging Face
Category: Generative AI

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

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.

What's included

5 videos3 readings2 assignments1 app item3 plugins

5 videosTotal 28 minutes

Overview of AI Engineering with LLMs6 minutes
Course Introduction3 minutes
Significance of Generative AI 6 minutes
Generative AI Architectures and Models 6 minutes
Generative AI for NLP7 minutes

3 readingsTotal 15 minutes

IBM Product Spotlight: watsonx.governance2 minutes
Course Overview10 minutes
Summary and Highlights3 minutes

2 assignmentsTotal 25 minutes

Practice Quiz: Generative AI Overview and Architecture10 minutes
Graded Quiz: Generative AI Architecture15 minutes

1 app itemTotal 60 minutes

Lab: Exploring Generative AI Libraries60 minutes

3 pluginsTotal 32 minutes

Helpful Tips for Course Completion2 minutes
Reading: Basics of AI Hallucinations10 minutes
Reading: Overview of Libraries and Tools20 minutes

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 videos6 readings2 assignments2 app items2 plugins

2 videosTotal 14 minutes

Tokenization7 minutes
Overview of Data Loaders7 minutes

6 readingsTotal 14 minutes

Data Quality and Diversity for Effective LLM Training 5 minutes
Summary and Highlights2 minutes
What's Next: Explore IBM watsonx.governance1 minute
Course Conclusion3 minutes
Congratulations and Next Steps2 minutes
Team and Acknowledgments1 minute

2 assignmentsTotal 25 minutes

Practice Quiz: Preparing Data10 minutes
Graded Quiz: Data Preparation for LLMs15 minutes

2 app itemsTotal 120 minutes

Lab: Implementing Tokenization60 minutes
Lab: Creating an NLP Data Loader60 minutes

2 pluginsTotal 9 minutes

Cheat Sheet: Generative AI and LLMs: Architecture and Data Preparation5 minutes
Course Glossary: Generative AI and LLMs: Architecture and Data Preparation4 minutes

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Instructors

Joseph Santarcangelo

IBM

37 Courses2,523,361 learners

Offered by

IBM

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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.

To access course materials, assignments, and earn a Certificate, you'll need to purchase the Certificate experience when you enroll in a course. Eligible learners may also have the option to start with a Free Trial. Some courses may also offer a Full Course, No Certificate option. This lets you access course materials, submit required assessments, and receive a final grade, but you won't be able to earn or purchase a Certificate.