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Cancellation Policy: If you cancel your registration two weeks or more before the course is scheduled to begin, you are entitled to receive your choice of either a credit for a future seminar (which can be applied toward any of our courses) or a refund of the registration fee (minus a processing fee of $50). 

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 3-Day Livestream Seminar Taught by Hudson Golino, Ph.D.

This seminar is part of our AI-Enabled Data Analytics Certification Program, a series of 4 expert-led courses designed to build practical AI skills for research and data analysis. Contact us to learn how to complete your certification and access special pricing.

This seminar introduces basic techniques for converting unstructured text data into structured data in R. As a necessary precursor to working with large language transformer models (LLMs), you’ll learn about LLM embeddings, their use, and gain hands-on experience implementing them.

In this course, we’ll cover zero-shot classification, which uses LLMs for text classification without the need for labeled data. You’ll learn how to implement these methods in R using Hugging Face Transformers. In addition, we’ll explore how retrieval-augmented generation can be used to discover and understand topics in text and support automatic zero-shot text classification with pre-trained transformer models. Finally, we’ll cover text classification using LLM embeddings and network models.

The overall goal of this course is to provide you with a comprehensive applied understanding of LLMs for research applications. By the end of the course, you will have the necessary skills to apply these techniques to analyze and extract insights from unstructured text data in your research work.

Why are large language transformer models (LLMs) so popular nowadays?

Large language transformer models (such as GPT-5, GPT-oss-120b, and GPT-oss-20b) have gained popularity for several reasons:

1. State-of-the-art performance: These models have achieved state-of-the-art performance on a wide range of natural language processing tasks, including language translation, text summarization, question answering, and language generation.
2. Zero-shot learning: LLMs can perform tasks for which they have not been explicitly trained, a property known as zero-shot learning. This is because they have been trained on a vast amount of diverse text data, allowing them to understand the underlying patterns and relationships in natural language.
3. Scalability: LLMs are highly scalable and can be fine-tuned for specific tasks with relatively small amounts of task-specific data.
4. General-purpose: LLMs are designed to be general-purpose, meaning they can be used for a wide variety of natural language processing tasks without the need for specialized models for each task.
5. Ease of use: Many LLMs are available as pre-trained models, allowing developers and researchers to use them without the need for extensive training or expertise in natural language processing..

Overall, the combination of state-of-the-art performance, zero-shot learning, scalability, general-purpose design, and ease of use makes large language transformer models highly attractive for a wide range of natural language processing applications.

This course is designed as a first introduction to natural language processing and large language models for research applications, covering some basic concepts and applications of transformer models in R.