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Build in-demand, job-ready generative AI architecture and data science skills in two weeks. A basic knowledge of Python and PyTorch and an awareness of machine learning and neural networks would be an advantage, though not strictly required.
The demand for gen AI is forecast to grow over 46% annually by 2030 (Source: Statista). AI engineers and developers, data scientists, machine learning engineers, and other AI professionals with gen AI skills are highly sought-after. This course builds in-demand skills in large language model (LLM) architecture and data preparation employers are looking for.
During the course, you’ll learn about real-world applications using generative AI. You’ll gain insights into gen AI architectures and models, such as recurrent neural networks (RNNs), transformers, generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models. You’ll use different training approaches for each model. Plus, you’ll explore LLMs such as generative pre-trained transformers (GPT) and bidirectional encoder representations from transformers (BERT).
Additionally, you’ll gain a detailed understanding of the tokenization process, tokenization methods, and the use of tokenizers for word-based, character-based, and subword-based tokenization. You’ll get hands-on experience using data loaders for training generative AI models, using PyTorch libraries, and generative AI libraries in Hugging Face. Plus, you’ll implement tokenization and create an NLP data loader.
If you’re looking to master gen AI LLM architecture and data preparation, ENROLL TODAY and get ready to power up your resume with skills employers need!
Prerequisites: To enroll 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.
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.
Module 1: Generative AI Architecture
Module 2: Data Preparation for LLMs
How do AI specialists use skills in LLM architecture and data preparation in their work?
AI specialists use skills in LLM architecture and data preparation to design, train, and fine-tune language models that power applications like chatbots, virtual assistants, and automated content generation tools. These skills help them prepare and structure data effectively, ensuring that models are accurate, relevant, and efficient in understanding and generating human-like text for real-world applications.
Why do employers value skills in LLM architecture and data preparation?
Employers increasingly value skills in LLM architecture and data preparation because these are foundational for creating and optimizing AI applications that automate and personalize interactions, analyze vast amounts of data, and enhance customer engagement. These capabilities are essential for businesses aiming to remain competitive in fields like customer service, content generation, and personalized marketing.
What are some of the reasons employers specifically look for LLM and data preparation expertise?
Employers look for LLM and data preparation expertise to stay at the forefront of AI-driven innovation, optimize operational efficiency, and harness the power of predictive insights. Skilled professionals in these areas can build scalable, responsive models that allow companies to deliver more relevant customer experiences, streamline content creation, and extract actionable insights from large datasets—key advantages in today’s data-driven economy.
Who can take this course?
Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.
Who can take this course?
Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.
