Mastering Generative AI: LLM Architecture & Data Preparation
Build job-ready generative AI architecture and data science skills focusing on large language model development and implementation in just two weeks.
Build job-ready generative AI architecture and data science skills focusing on large language model development and implementation in just two weeks.
This intermediate-level course from IBM equips you with in-demand skills in generative AI architecture and data preparation that employers are actively seeking. With generative AI forecast to grow over 46% annually by 2030, this course positions you at the forefront of this rapidly expanding field. You'll explore real-world applications of generative AI and gain comprehensive insights into various architectures and models, including recurrent neural networks (RNNs), transformers, generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models. The curriculum covers large language models like GPT, BERT, BART, and T5, giving you a solid understanding of advanced language processing techniques. You'll develop practical skills in tokenization processes using NLP libraries such as NLTK, spaCy, BertTokenizer, and XLNetTokenizer. Through hands-on labs, you'll implement tokenization, create NLP data loaders using PyTorch, and work with generative AI libraries in Hugging Face. This course provides both theoretical knowledge and practical implementation experience, culminating in an industry-recognized credential that enhances your resume with skills that today's employers need.
Instructors:
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What you'll learn
Learn about real-world applications of generative AI and their significance in various industries
Understand different generative AI architectures including RNNs, transformers, GANs, VAEs, and diffusion models
Gain insights into large language models like GPT, BERT, BART, and T5 used in advanced language processing
Master the tokenization process using NLP libraries such as NLTK, spaCy, BertTokenizer, and XLNetTokenizer
Implement tokenization techniques to effectively preprocess raw textual data
Create and utilize NLP data loaders with PyTorch for tokenization, numericalization, and padding
Skills you'll gain
This course includes:
PreRecorded video
Graded quizzes,2 labs,Practice quizzes
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
Fee Structure
Payment options
Financial Aid
Instructor
Pioneering Data Scientist Bridging AI Research and Education
Dr. Joseph Santarcangelo, a Data Scientist at IBM, brings a unique blend of academic excellence and practical expertise to the field of data science and artificial intelligence. With a Ph.D. in Electrical Engineering, his groundbreaking research focused on the intersection of machine learning, signal processing, and computer vision to understand how video content influences human cognitive processes. At IBM, he has established himself as a prominent educator and course developer, creating comprehensive learning materials that have reached hundreds of thousands of students worldwide. His teaching portfolio encompasses a wide range of technical subjects, from foundational Python programming to advanced topics in artificial intelligence, machine learning, and computer vision. Santarcangelo's ability to translate complex technical concepts into accessible learning experiences has made him an influential figure in data science education, maintaining consistently high ratings from learners while continuing to push the boundaries of applied machine learning and artificial intelligence research.
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Frequently asked questions
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