Guide to Generative AI and LLM Architectures
Loading...
Course
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.
Self-Paced
Mentored
Intermediate
Duration
Generative AI is reshaping industries, creating opportunities for professionals who can design, train, and apply advanced AI models. Organizations increasingly seek talent with the ability to work with large language models (LLMs), natural language processing (NLP), and modern generative architectures.
In this course, youll explore the foundations of generative AI, its types, and real-world applications. Youll learn how key modelsincluding recurrent neural networks (RNNs), transformers, generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion modelsare trained and applied in practice. Youll also examine LLMs such as GPT and BERT, understanding their architectures and capabilities.
As you progress, youll gain practical experience with tokenization techniques, NLP data loaders, and essential tools like PyTorch and Hugging Face libraries. Hands-on labs will guide you through implementing tokenization and preparing data pipelines for training generative models.
The course concludes with a project where youll apply what youve learned to showcase your generative AI and LLM architecture skills. By the end, youll be equipped with both theoretical knowledge and practical expertise to advance your career in AI developmentThis course comprises three purposely designed modules that take you on a carefully defined learning journey.
It is a self-paced course, which means it is not run to a fixed schedule with regard to completing modules. It is anticipated that you will complete the course in 5 hours. However, as long as the course is completed by the end of your enrollment, you can work at your own pace. And dont worry, youre not alone! You will be encouraged to stay connected with your learning community through the course discussion space.
The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include discussion space, videos, reading material, quizzes, hands-on labs, quizzes and final assignment.
Once you have successfully completed the course, you will earn your IBM Certificate.
This course comprises three purposely designed modules that take you on a carefully defined learning journey.
It is a self-paced course, which means it is not run to a fixed schedule with regard to completing modules. It is anticipated that you will complete the course in 5 hours. However, as long as the course is completed by the end of your enrollment, you can work at your own pace. And dont worry, youre not alone! You will be encouraged to stay connected with your learning community through the course discussion space.
The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include discussion space, videos, reading material, quizzes, hands-on labs, quizzes and final assignment.
Once you have successfully completed the course, you will earn your IBM Certificate.
You will be able to:
- Differentiate between GenAI architectures like RNNs, Transformers, VAEs, GANs, and Diffusion Models.
- Apply LLMs such as GPT, BERT, BART, and T5 for language processing tasks.
- Preprocess text data using NLP libraries like NLTK, spaCy, BertTokenizer, and XLNetTokenizer.
- Create NLP data loaders in PyTorch for tokenization, numericalization, and padding>Aspiring GenAI architects and data scientists
- Aspiring GenAI architects and data scientists
- Learners with basic ML or neural network knowledge
A basic knowledge of Python and PyTorch
Course Outline
Why Learn with SkillUp Online?
We believe every learner is an individual and every course is an opportunity to build job-ready skills. Through our human-centered approach to learning, we will empower you to fulfil your professional and personal goals and enjoy career success.
Reskilling into tech? We’ll support you.
Upskilling for promotion? We’ll help you.
Cross-skilling for your career? We’ll guide you.
Personalized Mentoring & Support
1-on-1 mentoring, live classes, webinars, weekly feedback, peer discussion, and much more.
Practical Experience
Hands-on labs and projects tackling real-world challenges. Great for your resumé and LinkedIn profile.
Best-in-Class Course Content
Designed by the industry for the industry so you can build job-ready skills.
Job-Ready Skills Focus
Competency building and global certifications employers are actively looking for.
FAQs
This generative AI and LLM course helps you build job-ready skills in generative AI architecture and data science. Youll explore popular model architectures such as RNNs, GANs, VAEs, transformers, and diffusion models, while also gaining hands-on practice with large language models (LLMs) like GPT, BERT, BART, and T5.
This course is designed for aspiring AI professionals, data scientists, and developers who want to gain practical skills in generative AI and LLM. Its also a strong fit if youre looking for a generative AI Certificate to validate your knowledge and prepare for career opportunities in data science or AI engineering.
No prior programming experience is required. However, a basic understanding of Python, PyTorch, and machine learning concepts will make the hands-on labs smoother. The course explains technical concepts in a beginner-friendly way.
Youll learn the differences between major generative AI models, including RNNs, Transformers, VAEs, GANs, and Diffusion Models. The course also explains their unique training approaches and real-world applications in AI and data science.
The generative AI and LLM course introduces widely used models such as GPT, BERT, BART, and T5. Youll understand their architectures, learn how they power natural language processing tasks, and apply them to real-world use cases.
Yes. Youll implement tokenization using tools like NLTK, spaCy, BertTokenizer, and XLNetTokenizer. Youll also create NLP data loaders in PyTorch for preprocessing, numericalization, and padding of text data.
Absolutely. Youll gain practical experience with Hugging Face libraries, widely used in modern AI development, to prepare and fine-tune LLMs.
The training includes coverage of AI hallucinations and their mitigation, giving you the awareness needed to build more reliable and responsible AI solutions.
Yes. The course includes a final project where you apply your knowledge of generative AI and LLM architectures to create artifacts that showcase your generative AI architect skills.
Completing this generative AI and LLM course gives you an industry-recognized IBM Certificate that demonstrates practical, job-ready skills. It enhances your resume for AI architect, machine learning, or data science roles across industries.
Industries such as finance, healthcare, retail, and digital marketing are actively hiring professionals with Gen AI LLM skills and LLM credentials, as they look to leverage generative AI for data-driven innovation.
The course is self-paced and designed to be completed in less than a month. You can learn at your own speed while practicing in guided labs.
Yes. After completing all modules and the final project, youll earn an industry-recognized IBM Certificate that validates your skills in generative AI and LLM architectures.
Although no coding background is required, some familiarity with Python, PyTorch, and the basics of machine learning and neural networks will give you an advantage during the hands-on labs.
Course Offering
Type of certificate
IBM Certificate
About this course
05 Modules
05 Skills
Includes
Discussion space
03 Hands-on labs
02 Practice quizzes
02 Graded quizzes
Create
NLP Data Loader
Exercises to explore
Generative AI Libraries
Implementing Tokenization
This course has been created by
Newsletters & Updates
Subscribe to get the latest tech career trends, guidance, and tips in your inbox.