CF

AI Engineering: Fine-Tuning LLMs

1h 35m 46s
English
Paid

If you're interested in AI that actually works and not just sounds impressive, this compact course is for you.

Fine-tuning the GPT model is not just about changing parameters. It's a process that allows you to "reprogram" the model for your specific tasks and goals.

Whether it's creating intelligent chatbots, personalized learning systems, or content generators, this course provides you with all the tools to implement your ideas using your own custom AI model. You'll learn to prepare data, set up the training process, and evaluate results effectively, like a true professional.

There is no abstract theory here - only practical, modern AI engineering skills that can be applied immediately.

About the Author: Zero To Mastery

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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.

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#1: Introduction
All Course Lessons (14)
#Lesson TitleDurationAccess
1
Introduction Demo
04:23
2
Why Fine-Tuning?
09:20
3
Fine-Tuning Setup
04:16
4
Preparing Data for Fine-Tuning
05:09
5
Fine-Tuning Step by Step
14:42
6
Generating the Prompt
03:46
7
Function to Structure Data
05:39
8
Creating JSONL Files
04:56
9
Upload Data to OpenAI API
02:50
10
Fine-Tuning a GPT Model
04:39
11
Debugging the Fine-Tuning Job
06:20
12
Evaluating Fine-Tuned Models
11:20
13
Testing the Fine-Tuned Model
10:15
14
What's Next?
08:11
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Frequently asked questions

What prerequisites are needed before enrolling in this course?
This course does not explicitly list prerequisites, but a basic understanding of AI models, particularly GPT, is beneficial. Familiarity with JSON file formatting and API interactions will also help, as these topics are covered in lessons like 'Creating JSONL Files' and 'Upload Data to OpenAI API'.
What projects will I work on during the course?
During the course, you will work on practical projects such as creating intelligent chatbots and personalized learning systems. These projects involve preparing data, setting up the training process, and evaluating fine-tuned models, as detailed in lessons like 'Preparing Data for Fine-Tuning' and 'Testing the Fine-Tuned Model'.
Who is the target audience for this course?
The course is designed for individuals interested in applying AI engineering skills practically. It's suitable for those who want to create custom AI models for tasks like content generation and personalized systems, making it ideal for developers and data scientists looking to specialize in AI fine-tuning.
How does this course compare in depth to other AI courses?
This course focuses specifically on the practical aspects of fine-tuning GPT models, providing hands-on experience without delving into abstract theory. Compared to broader AI courses, it offers detailed insights into the step-by-step process of fine-tuning, data preparation, and model evaluation.
What specific tools or platforms will I use in this course?
The course uses tools and platforms related to GPT model fine-tuning, such as the OpenAI API for uploading data. Lessons like 'Upload Data to OpenAI API' and 'Fine-Tuning a GPT Model' guide you through using these tools effectively.
What topics are not covered in this course?
The course does not cover general AI theory or the development of AI models from scratch. It focuses on the practical application of fine-tuning pre-existing GPT models, leaving out broader AI topics and foundational AI engineering concepts.
What is the expected time commitment to complete this course?
While the total runtime is not provided, the course consists of 14 lessons. Given the focus on practical skills, expect to spend additional time on exercises and projects outside of lesson viewing, particularly during data preparation and model evaluation phases.