Online Course
Introduction to Hugging Face

Master working with state-of-the art Large Lanaguage Models with Hugging Face’s powerful tools and libraries.

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  • Institute of Analytics
  • The Association of Data Scientists
  • E-Learning Quality Network
  • European Agency for Higher Education and Accreditation
  • Global Association of Online Trainers and Examiners

Skill level:

Advanced

Duration:

2 hours
  • Lessons (93 hours)

CPE credits:

3
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited:

certificate

What You Learn

  • Explore the Hugging Face suite and available NLP tools.
  • Implement models for NLP tasks easily using pipelines.
  • Load and transform data using the Datasets library.
  • Work with Transformers models and fine-tune on custom data.
  • Create and share Gradio apps and integrate with Hugging Face Spaces.

Topics & tools

natural language processinglarge language model (llm)huggingfaceaipython

Your instructor

Course OVERVIEW

Description

CPE Credits: 3 Field of Study: Information Technology
Delivery Method: QAS Self Study

Hugging Face is an essential toolkit for any developer working with state-of-the-art Large Language Models (LLMs). This course will introduce you to the powerful tools and libraries Hugging Face offers, from out-of-the-box solutions, to custom model training and configurations.

Through engaging text and video lessons, interactive excersises and coding tutorials, you'll gain the practical skills needed to work confidently with Hugging Face.

The course is structures into several chapters, each building on the last, focusing on key aspects of the Hugging Face ecosystem. Throughout each chapter we’ll be looking at specific tools and code that can help us achieve our goals and improve your skills. 

Let's take a look at the course chapters in more detail:

  1. Introduction to Hugging Face
    To get started, we'll be covering exactly what Hugging Face is and all the kinds of projects it can be used for. This will give you a firm understanding of the tools available before we get stuck in with some hands-on practicals.

  2. Getting Started With Pipelines
    Pipelines allow us to work with state-of-the art models in just a few lines of code! We'll be working through some practical examples of how we can create simple pipelines using LLMs for a range of Natural Language Processing tasks.

  3. Hugging Face Models
    There are a whole range of models available to us through Hugging Face. Here I'll introduce you to the Models Hub, a fantastic tool for sourcing high quality models to work with. I show you how we can load different LLMs for use in our projects.

  4. Hugging Face Tokenizers
    In this section of the course I'll show you a number of different ways we can preprocess text data, preparing us for some more advanced topics when working with transformer models.

  5. Fine-Tuning Models
    We will work through an in-depth case study as I walk you through an example of how to fine-tune an LLM for a specific language task. We'll also be sharing our models using Hugging Face Spaces and Gradio!
  6. Advancing Your Knowledge
    Here I will introduce you to some of the more advanced topics of working with Hugging Face such as working with large data, managing hardware configurations and integrating with other frameworks.

  7. Beyond Text: Audio and Video
    The Hugging Face suite of tools makes it easy to work with transformer models across a whole range of tasks. This section of the course will the skills we have developed in earlier chapters, and adapt to work with audio and image-based data.

If you're looking to develop your skills working with Large Language Models, or AI models in general, then this course is for you. I will provide you with the knowledge and practical skills needed to become a master through Hugging Face!

Prerequisites

  • Python (version 3.8 or later), Hugging Face Transformers library, and a code editor or IDE (e.g., VS Code or Jupyter Notebook)
  • Completion of an introductory Python course is recommended.
  • Familiarity with machine learning or natural language processing concepts is helpful but not mandatory.

Curriculum

48 lessons 16 exercises 1 exam

Free preview

Course Introduction

1.1 Course Introduction

4 min

Setting up Your Environment

1.2 Setting up Your Environment

4 min

What is Hugging Face?

2.1 What is Hugging Face?

5 min

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ACCREDITED certificates

Craft a resume and LinkedIn profile you’re proud of—featuring certificates recognized by leading global institutions.

Earn CPE-accredited credentials that showcase your dedication, growth, and essential skills—the qualities employers value most.

  • Institute of Analytics
  • The Association of Data Scientists
  • E-Learning Quality Network
  • European Agency for Higher Education and Accreditation
  • Global Association of Online Trainers and Examiners

Certificates are included with the Self-Study learning plan.

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How it WORKS

  • Lessons
  • Exercises
  • Projects
  • Practice Exams
  • AI Mock Interviews

Lessons

Learn through short, simple lessons—no prior experience in AI or data science needed.

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Exercises

Reinforce your learning with mini recaps, hands-on coding, flashcards, fill-in-the-blank activities, and other engaging exercises.

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Projects

Tackle real-world AI and data science projects—just like those faced by industry professionals every day.

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Practice Exams

Track your progress and solidify your knowledge with regular practice exams.

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AI Mock Interviews

Prep for interviews with real-world tasks, popular questions, and real-time feedback.

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