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AI, Machine Learning & Data Science2025-3 Edition100% Free Video Course

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

Advanced AI: LLMs Explained with Math (Transformers, Attention Mechanisms & More). This course explores the mathematics behind transformer models, attention mechanisms, and other advanced AI concepts. In this course, you’ll uncover the secrets behind transformers like GPT and BERT. Learn tokenization, attention mechanisms, situational encodings, and embeddings to build and innovate with advanced AI. Master machine learning and become a world-class AI expert. Transformer architecture is a fundamental model in modern AI, especially in natural language processing (NLP). What makes transformers special is that instead of reading word by word like older systems (called recurrent models), transformers examine entire sentences at once.

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(1 reviews)
5.3 Hours On-Demand
Created by Patrik Szepesi
Uploaded Sep 2026
English
Beginner to advanced
ZeroToMastery – Advanced AI: LLMs Explained with Math (Transformers, Attention Mechanisms & More)
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Course Features:
5.3 hours on-demand video
32 complete lectures
Streamable on mobile, tablet & desktop
Self-paced curriculum with progress tracking
Direct MP4 downloads & offline video access
Verified course archives hosted on cloud infrastructure.

What You'll Master in this Course

How to convert text into readable data for your model with tokenization
The inner workings of attention mechanisms in transformers
How to preserve sequence data in AI models with positional encodings
The role of matrices in language encoding and processing
Building dense word representations with multidimensional embeddings
Difference between two-way and masked language models
Practical applications of inner multiplication and vector mathematics in artificial intelligence
How Transformers process, understand, and produce human-like text

Course Curriculum32 Lectures

1 sections • 5.3 hours total length

Prefer offline learning? Download all 32 video lectures and project files for free.
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Advanced-AI-LLMs-Explained-with-Math-346K
Preview
4m
Introduction-To-Positional-Encodings-228K
Preview
4m
Positional-Encodings-Part-1-382K
5m
Positional-Encodings-Part-2- Even-and-Odd-Indices -296K
9m
Why-Use-Sine-and-Cosine-Functions-336K
6m
Understanding-the-Nature-of-Sine-and-Cosine-Functions-418K
12m
Visualizing-Positional-Encodings-in-Sine-and-Cosine-Graphs-403K
11m
Solving-the-Equations-to-Get-the-Values-for-Positional-Encodings-323K
17m
Introduction-to-Attention-Mechanism-244K
2m
Query -Key-and-Value-Matrix-235K
12m
Getting-Started-with-Our-Step-by-Step-Attention-Calculation-248K
7m
Creating-Our-Optional-Experiment-Notebook-Part-1-440K
4m
Calculating-Key-Vectors-348K
25m
Query-Matrix-Introduction-292K
10m
Calculating-Raw-Attention-Scores-294K
22m
Understanding-the-Mathematics-Behind-Dot-Products-and-Vector-Alignment-327K
14m
Visualizing-Raw-Attention-Scores-in-2D-308K
6m
Converting-Raw-Attention-Scores-to-Probability-Distributions-with-Softmax-379K
11m
Normalization-304K
4m
Understanding-the-Value-Matrix-and-Value-Vector-295K
9m
Calculating-the-Final-Context-Aware-Rich-Representation-for-the-Word- River -428K
16m
Understanding-the-Output-496K
3m
Creating-Our-Optional-Experiment-Notebook-Part-2-748K
9m
Understanding-Multi-Head-Attention-344K
13m
Multi-Head-Attention-Example-and-Subsequent-Layers-446K
16m
Masked-Language-Learning-164K
2m
Encoding-Categorical-Labels-to-Numeric-Values-452K
18m
Understanding-the-Tokenization-Vocabulary-285K
13m
Encoding-Tokens-318K
10m
Practical-Example-of-Tokenization-and-Encoding-393K
14m
DistilBert-vs.-Bert-Differences-233K
3m
Embeddings-In-A-Continuous-Vector-Space-239K
6m

Requirements

  • Familiarity with basic linear algebra strongly recommended

Description

Advanced AI: LLMs Explained with Math (Transformers, Attention Mechanisms & More). This course explores the mathematics behind transformer models, attention mechanisms, and other advanced AI concepts. In this course, you’ll uncover the secrets behind transformers like GPT and BERT. Learn tokenization, attention mechanisms, situational encodings, and embeddings to build and innovate with advanced AI. Master machine learning and become a world-class AI expert. Transformer architecture is a fundamental model in modern AI, especially in natural language processing (NLP). What makes transformers special is that instead of reading word by word like older systems (called recurrent models), transformers examine entire sentences at once.

Instructor

P

Patrik Szepesi

Specialist in AI, Machine Learning & Data Science

Passionate educator focused on real-world practical skills, modern frameworks, and production-ready engineering practices. Delivering step-by-step masterclasses accessible to learners globally on MJ Accedemy.

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