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ChatGPT & Large Language Models (LLMs): A Practical Guide

58m 21s
English
Paid

Learn how ChatGPT works behind the scenes. This course gives you a clear and practical path to understanding Large Language Models. You will learn how they process text, how you can guide them with prompts, and how you can train your own models.

What ChatGPT Is

ChatGPT is a Large Language Model. It reads text and predicts what comes next. It learns these patterns from huge sets of text from the internet.

How LLMs Work

LLMs use deep learning to spot links between words. They do not think like humans. They follow patterns they learned during training.

This course explains these ideas in simple steps. You will learn what training means, how fine-tuning works, and why prompts guide model output.

Why This Course Helps You

You will see real examples of LLMs in use. These examples show you how to build prompts, shape responses, and understand model limits.

This helps you decide whether these models act like true intelligence or operate more like advanced text tools.

What You Will Learn

  • How LLMs learn from data
  • How prompts steer model behavior
  • How fine-tuning works
  • When to trust model output
  • How to train small models of your own

Careers and Future Growth

Work with LLMs is growing fast. Many teams now need people who can use and test these models. More roles appear each year.

Even if you do not plan to become an AI engineer, this knowledge helps you keep up with fast changes in tech.

Join the Community

When you enroll, you join our active online community classroom. You can learn with students, mentors, and instructors.

The community is friendly and helpful. Many students say it keeps them motivated.

Always Up to Date

This field changes fast. The course will grow with it. New lessons and resources will appear as LLMs evolve.

About the Author: Zero To Mastery

Zero To Mastery thumbnail

Zero To Mastery (ZTM) is a Toronto-based online coding academy founded by Andrei Neagoie, originally a senior developer at large Canadian tech firms before turning to teaching full-time. The academy's signature is the cohort-based bootcamp track combined with a deep self-paced course library, all aimed at career-changers and self-taught developers preparing to land software-engineering roles at top companies.

The instructor roster has grown well beyond Andrei to include other senior practitioners: Daniel Bourke (machine learning), Aleksa Tešić (DevOps), Jacinto Wong, and others. Courses cover the full software-engineering career path: web development with React and Next.js, Python, machine learning and deep learning, DevOps and cloud, system design, mobile, and the algorithm / data-structure interview prep that gates engineering jobs.

The CourseFlix listing under this source carries over 120 ZTM courses spanning that full range. Material is paid; ZTM itself runs on a monthly / annual membership model. The teaching style favours long-form, project-based courses where students build complete portfolio-quality applications rather than disconnected feature tutorials.

Watch Online 13 lessons

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#1: Why Is This Important?
All Course Lessons (13)
#Lesson TitleDurationAccess
1
Why Is This Important? Demo
03:29
2
Introduction to Prompt Design
02:50
3
ChatGPT
07:46
4
Playground
03:13
5
Example Implementation
07:33
6
Example Implementation: Hipster Friend
04:39
7
Transition to Fine-Tuning
01:41
8
Introduction to Fine-Tuning
05:41
9
Getting a Data Set
03:04
10
Fine-Tuning the Model
03:46
11
Using the Fine-Tuned Model
05:51
12
What's Next for LLMs and Career Advice
07:30
13
Thank You!
01:18
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Frequently asked questions

What are the prerequisites for this course?
There are no specific prerequisites required for this course. It is designed to provide a foundational understanding of Large Language Models and is suitable for beginners. Familiarity with basic computer science concepts might be helpful but is not necessary.
What projects will I build during the course?
The course includes practical lessons such as 'Example Implementation' and 'Example Implementation: Hipster Friend', where you will learn to build and experiment with prompts. Additionally, you will have the opportunity to train small models and use fine-tuning techniques on datasets.
Who would benefit most from taking this course?
This course is ideal for individuals interested in artificial intelligence, particularly those looking to understand how Large Language Models like ChatGPT function. It is also beneficial for professionals in tech fields who want to keep up with AI advancements, even if they do not plan to become AI engineers.
How does the depth of this course compare to others on Large Language Models?
This course provides a practical introduction to LLMs, focusing on core concepts like model training, prompt design, and fine-tuning. It is more concise with 13 lessons, making it suitable for those looking to quickly grasp fundamental principles without deep-diving into advanced technical details.
Does this course cover deployment of models?
The course does not specifically cover the deployment of models. It focuses on understanding and fine-tuning LLMs, as well as designing effective prompts. Deployment would require additional learning resources.
What is the expected time commitment for completing the course?
The course consists of 13 lessons, and while the total runtime is not specified, it is designed to be completed at your own pace. Given the practical nature of the course, additional time may be spent experimenting with prompts and fine-tuning models.
How will this course benefit my career in technology?
Understanding LLMs and ChatGPT can significantly enhance your career prospects in technology. The demand for skills in using and testing AI models is growing rapidly. This course equips you with the knowledge to keep up with technological changes and opens up opportunities in various AI-related roles.