Skip to main content
HomeArtificial IntelligenceLarge Language Models (LLMs) Concepts

Large Language Models (LLMs) Concepts

57 reviews

Discover the full potential of LLMs with our conceptual course covering LLM applications, training methodologies, ethical considerations, and latest research.

Start Course for Free
2 Hours15 Videos50 Exercises
19,130 LearnersTrophyStatement of Accomplishment

Create Your Free Account



By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.
GroupTraining 2 or more people?Try DataCamp For Business

Loved by learners at thousands of companies

Course Description

Discover Large Language Models

In this course, you’ll journey through the world of Large Language Models (LLMs) and discover how they are reshaping the AI landscape. You’ll explore the factors fueling the LLM boom, such as the deep learning revolution, data availability, and computing power.

This conceptual course will dig into LLMs and how they revolutionize businesses and everyday life with real-world examples, from finance to content creation.

Unlock the Secrets of LLMs and Training Methodologies

You’ll learn about the building blocks of LLMs, including natural language processing techniques, fine-tuning strategies, and learning techniques like zero-shot, few-shot, and multi-shot learning. As you progress, you’ll gain insights into the state-of-the-art training methodologies that drive LLMs, including next-word prediction, masked language modeling, and attention mechanisms.

Explore LLMs Concerns and Considerations

You’ll also tackle the critical ethical and environmental considerations in building and training LLMs, such as training data and privacy concerns.

As you wrap up the course, you’ll discover how to stay ahead of the curve as you delve into the latest research in the LLM field. You’ll explore future developments focusing on model explainability, unsupervised bias handling, computational efficiency, and enhanced creativity.

By the end of this course, you'll have a comprehensive understanding of LLMs, their capabilities, applications, and intriguing challenges.
For Business

GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more
Try DataCamp for BusinessFor a bespoke solution book a demo.
  1. 1

    Introduction to Large Language Models (LLM)


    The AI landscape is evolving rapidly, and Large Language Models (LLMs) are at the forefront of this evolution. This chapter examines how LLMs are advancing the development of human-like artificial intelligence and transforming industries through their numerous applications. You will explore the challenges and complexity associated with language modeling.

    Play Chapter Now
    The rise of LLMs in the AI landscape
    50 xp
    Definition of an LLM
    50 xp
    LLMs in the AI landscape
    100 xp
    AI vs. LLM applications
    100 xp
    Real-world applications
    50 xp
    Business applications
    50 xp
    Multimodal applications
    100 xp
    Automate data-driven tasks
    50 xp
    Challenges of language modeling
    50 xp
    What can a language model do?
    50 xp
    Single vs. multi-task learning
    100 xp
  2. 2

    Building Blocks of LLMs

    This chapter emphasizes the novelty of LLMs and their emergent capabilities while outlining various NLP techniques for data preparation. You will learn the challenges of training LLMs and how fine-tuning can effectively address them. You will also understand how N-shot learning techniques enable efficient adaptation of pre-trained models when faced with limited labeled data.

    Play Chapter Now
  3. 3

    Training Methodology and Techniques

    In this chapter, you will learn about the fundamental building blocks of training an LLM, such as pre-training techniques. You'll also gain an intuitive understanding of complex concepts like transformer architecture, including the attention mechanism. The chapter discusses an advanced fine-tuning technique and summarizes the training process to complete an LLM.

    Play Chapter Now
  4. 4

    Concerns and Considerations

    In this chapter, we delve into the key considerations when training LLMs, such as large data availability, data quality, accurate labeling, and the implications of biased data. You will also examine various LLM risks like data privacy, ethical concerns, and environmental impact. Lastly, the chapter concludes by discussing emerging research areas and the evolving landscape of LLMs.

    Play Chapter Now

In the following tracks

AI Fundamentals


Collaborator's avatar
Jasmin Ludolf
Collaborator's avatar
Amy Peterson
Collaborator's avatar
James Chapman
Vidhi Chugh HeadshotVidhi Chugh

AI Strategist and Ethicist

Vidhi is an AI Strategist and Ethicist working at the intersection of data science, product, and engineering to build scalable machine learning systems. Listed as one of the "Top 200 Business and Technology Innovators" in the world, Vidhi is on a mission to democratize machine learning and break the jargon for everyone to be a part of this transformation.
See More

Don’t just take our word for it

from 57 reviews
Sort by
  • Benny L.
    about 2 months

    This is a fantastic course you would never miss!

  • Stephen C.
    6 months

    Great course

  • Heather C.
    7 months

    Excellent professor.

  • Julio R.
    7 months

    It provides both an excellent overview of the key elements of LLMs and details on their structure and how do they work. The instructor follows a good pace and provide plenty of examples.

  • Pedro R.
    9 months

    The course gives a good understanding on how the model work, and gives a good overview on how is the process into making one as well the difficuties and care that should be done while doing it.

"This is a fantastic course you would never miss!"

Benny L.

"Great course"

Stephen C.

"Excellent professor."

Heather C.


Join over 13 million learners and start Large Language Models (LLMs) Concepts today!

Create Your Free Account



By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.