Offered By: IBMSkillsNetwork
Guide to Generative AI and LLM Architectures
Build in-demand, job-ready generative AI architecture and data science skills in less than a month. No programming experience is required.
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Course
Artificial Intelligence
At a Glance
Build in-demand, job-ready generative AI architecture and data science skills in less than a month. No programming experience is required.
This course teaches you the skills you need for your first job as a generative AI architect or data scientist.
You will learn:
- Job-ready generative AI architecture and data science skills in less than a month, plus practical experience and an industry-recognized credential that employers value.
- To difference between generative AI architectures and models, such as RNNs, transformers, VAEs, GANs, and diffusion models.
- The use of LLMs, such as GPT, BERT, BART, and T5 in language processing.
- The implementation of tokenization to preprocess raw textual data using NLP libraries such as NLTK, spaCy, BertTokenizer, and XLNetTokenizer.
- The creation of an NLP data loader using PyTorch to perform tokenization, numericalization, and padding of text data.
Course Overview
The demand for generative AI is expected to grow at 46.47% annually, resulting in a market volume of US$356 billion by 2030 (Source: Statista, Feb 2024). The expansion of generative AI across industries emphasizes its potential for aspiring data scientists, machine learning engineers, and AI developers.
This course focuses on using large language models (LLMs), natural language processing (NLP), and general artificial intelligence (AI) skills that organizations desire.
In this course, you will learn about the types of generative AI and its real-world applications. You will gain knowledge of various generative AI architectures and models, such as Recurrent Neural Networks (RNNs), Transformers, Generative Adversarial Networks (GANs), Variational AutoEncoders (VAEs), and Diffusion Models. You will learn the differences in the training approaches used for each model. You will be able to explain the use of LLMs, such as Generative Pre-Trained Transformers (GPT) and Bidirectional Encoder Representations from Transformers (BERT).
Furthermore, you will learn about the tokenization process, tokenization methods, and the use of tokenizers for word-based, character-based, and subword-based tokenization. You will gain knowledge to use data loaders for training generative AI models, list the PyTorch libraries for preparing and handling data within data loaders, and use the generative AI libraries in Hugging Face. The course will also prepare you to implement tokenization and create an NLP data loader.
Throughout this short self-paced course, you will be presented with instructional guidance through videos followed by hands-on labs to practice what you learn. You will also complete a final project to showcase your generative AI or LLM architecture skills.
If you’re looking to build a rewarding career in generative AI architecture or data science domain, this IBM Guide to Generative AI and LLM Architectures course will get you job-ready and give you the skills you need for rewarding career opportunities.
Course Syllabus
Module 0: Welcome
- Overview of AI Engineering with LLMs
- Video: Course Introduction
- Reading: Specialization Overview
- Reading: General Information
- Reading: Learning Objectives and Syllabus
- Reading: Helpful Tips for Course Completion
- Reading: Grading Scheme
Module 1: Generative AI Architecture
- Reading: Module Introduction and Learning Objectives
- Video: Significance of Generative AI
- Video: Generative AI Architectures and Models
- Video: Generative AI for NLP
- Reading: Basics of AI Hallucinations
- Reading: Overview of Libraries and Tools
- Lab: Exploring Generative AI Libraries
- Reading: Summary and Highlights
- Practice Quiz: Generative AI Overview and Architecture
- Graded Quiz: Generative AI Architecture
Module 2: Data Preparation for LLMs
- Reading: Module Introduction and Learning Objectives
- Video: Tokenization
- Lab: Implementing Tokenization
- Video: Overview of Data Loaders
- Lab: Creating an NLP Data Loader
- Reading: Summary and Highlights
- Practice Quiz: Preparing Data
- Graded Quiz: Data Preparation for LLMs
Module 3: Course Cheat Sheet, Glossary and Wrap-up
- Reading: Cheat Sheet: Guide to Generative AI and LLM Architectures
- Reading: Course Glossary: Guide to Generative AI and LLM Architectures
Course Wrap-Up
- Reading: Course Conclusion
- Reading: Congratulations and Next Steps
- Reading: Team and Acknowledgements
- Reading: Copyrights and Trademarks
Recommended Skills Prior to Taking this Course
For this course, a basic knowledge of Python and PyTorch and an awareness of machine learning and neural networks would be an advantage, though not strictly required.
Estimated Effort
5 Hours
Level
Intermediate
Skills You Will Learn
Hugging Face Libraries, Large Language Models, NLP Data Loader, PyTorch, Tokenization
Language
English
Course Code
AI0208EN