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Advanced AI: LLMs Explained with Math (Transformers, Attention Mechanisms & More)

4h 55m 29s
English
Paid
Updated September 2026

Advanced AI: LLMs Explained with Math (Transformers, Attention Mechanisms & More) is a 32-lesson 4 hours 55 minutes self-paced course by Zero To Mastery. This Zero To Mastery course goes past the high-level explanations of transformer models and works through the actual math behind architectures like GPT and BERT.

Course facts

Lessons
32
Duration
4 hours 55 minutes
Level
All levels
Language
English
Updated
2026-09-11
Instructor
Zero To Mastery
Price
Premium

This Zero To Mastery course goes past the high-level explanations of transformer models and works through the actual math behind architectures like GPT and BERT.

What's covered

  • Tokenization — how raw text gets broken into units a model can process
  • Attention mechanisms and the role they play inside a transformer
  • The core algorithms that make modern transformer models work

By the end, you should be able to explain how a transformer architecture is put together, work on implementing and tuning transformer models yourself, and read AI/ML papers and codebases with a firmer grasp of what's actually happening under the hood.

It's built for people who already have some background in machine learning or computer science and basic AI/programming concepts — not a first introduction to either math or ML.

Who teaches Advanced AI: LLMs Explained with Math (Transformers, Attention Mechanisms & More)? 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.

What lessons are included in Advanced AI: LLMs Explained with Math (Transformers, Attention Mechanisms & More)?

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#1: Advanced AI: LLMs Explained with Math
All Course Lessons (32)
#Lesson TitleDurationAccess
1
Advanced AI: LLMs Explained with Math Demo
03:01
2
Creating Our Optional Experiment Notebook - Part 1
03:22
3
Creating Our Optional Experiment Notebook - Part 2
04:02
4
Encoding Categorical Labels to Numeric Values
13:25
5
Understanding the Tokenization Vocabulary
15:06
6
Encoding Tokens
10:57
7
Practical Example of Tokenization and Encoding
12:49
8
DistilBert vs. Bert Differences
04:47
9
Embeddings In A Continuous Vector Space
07:41
10
Introduction To Positional Encodings
05:14
11
Positional Encodings - Part 1
04:15
12
Positional Encodings - Part 2 (Even and Odd Indices)
10:11
13
Why Use Sine and Cosine Functions
05:09
14
Understanding the Nature of Sine and Cosine Functions
09:53
15
Visualizing Positional Encodings in Sine and Cosine Graphs
09:25
16
Solving the Equations to Get the Values for Positional Encodings
18:08
17
Introduction to Attention Mechanism
03:03
18
Query, Key and Value Matrix
18:11
19
Getting Started with Our Step by Step Attention Calculation
06:54
20
Calculating Key Vectors
20:06
21
Query Matrix Introduction
10:21
22
Calculating Raw Attention Scores
21:25
23
Understanding the Mathematics Behind Dot Products and Vector Alignment
13:33
24
Visualizing Raw Attention Scores in 2D
05:43
25
Converting Raw Attention Scores to Probability Distributions with Softmax
09:17
26
Normalization
03:20
27
Understanding the Value Matrix and Value Vector
09:08
28
Calculating the Final Context Aware Rich Representation for the Word "River"
10:46
29
Understanding the Output
01:59
30
Understanding Multi Head Attention
11:56
31
Multi Head Attention Example and Subsequent Layers
09:52
32
Masked Language Learning
02:30
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Frequently asked questions

What prerequisites are needed before enrolling in this course?
Prospective students should have a foundational understanding of machine learning and basic linear algebra. Familiarity with AI concepts like neural networks and experience in programming, particularly in Python, will be beneficial. These prerequisites will help students grasp the mathematical foundations of transformer models, such as those discussed in this course.
What projects or hands-on exercises are included in the course?
The course includes practical examples such as tokenization and encoding text, and the creation of an experiment notebook. These exercises aim to enhance understanding of the tokenization process and its application in models like GPT and BERT. However, there are no extensive project-based assignments as the focus is primarily on understanding mathematical concepts.
Who is the target audience for this course?
This course is targeted towards individuals who have a foundational knowledge of AI and machine learning and are looking to deepen their understanding of transformer models. It is particularly suitable for those who want to explore the mathematical intricacies of models like GPT and BERT to apply this knowledge in innovative AI projects.
How does the depth of this course compare to similar courses?
This course emphasizes the mathematical foundations of transformer models, offering detailed insights into algorithms and mechanisms such as attention mechanisms and tokenization. Compared to other courses that may focus on broad application, this course provides a more analytical approach, dissecting the core algorithms and their theoretical underpinnings.
What specific tools or platforms will I learn about in this course?
Students will learn about various components intrinsic to transformer models, including tokenization techniques, attention mechanisms, and multi-head attention processes. The course also covers advanced concepts like positional encodings using sine and cosine functions, although it does not focus on specific software platforms for deployment.
What topics are explicitly not covered in this course?
While the course delves into the mathematical foundations of transformer models, it does not cover deployment strategies or real-world application scenarios in depth. The course focuses on understanding the algorithms rather than on implementation using specific software frameworks or libraries like TensorFlow or PyTorch.
What is the expected time commitment for this course?
The course comprises 32 lessons. While the total runtime is not specified, students should be prepared to spend additional time on exercises and optional notebook experiments. The commitment can vary based on individual pace and prior familiarity with the topics, but a few hours per week is a reasonable estimate.