Free Download Generative AI Explained From Math Basics to LLMs
Published 4/2026
Created by Omar Koryakin
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 34 Lectures ( 4h 55m ) | Size: 1.65 GB
Master Vectors, Neural Networks, Transformers & Attention and The Concepts Behind ChatGPT, Gemini & Claude
What you'll learn
✓ Explain how machines represent, preprocess, and normalize data before it enters any AI or machine learning model
✓ Interpret vectors, dot products, cosine similarity, and Euclidean distance which is the math behind LLM embeddings
✓ Describe how model training works: loss functions, gradient descent, and what it truly means for a machine to learn
✓ Compare supervised, unsupervised, self-supervised, and reinforcement learning and how LLMs combine all three
✓ Break down the transformer architecture: tokenization, positional encoding, attention, and multi-head attention
✓ Distinguish between encoder-only, decoder-only, and encoder-decoder models and their real-world applications
✓ Identify key hyperparameters like learning rate, temperature, top-k, and top-p and understand why they matter
✓ Build a conceptual mental model of how ChatGPT, Claude, Gemini, and other LLMs generate text step by step
Requirements
● No programming or coding experience required as this is a fully conceptual course with zero code
● Basic arithmetic skills (addition, multiplication, fractions) are helpful but even these are reviewed in the course
● A computer or tablet with internet access to stream video lectures so no special software downloads needed
● Curiosity about how AI works under the hood so no prior machine learning or data science background necessary
Description
Ever wondered what really happens inside ChatGPT, Claude, or Gemini when you type a prompt? Most people use these tools every day without understanding the engine underneath. Most learning resources either drown you in equations and code or barely scratch the surface. This course gives you the middle ground a clear, structured, concept-first journey through the foundations of Generative AI.
No coding. No advanced math prerequisites. Just genuine understanding built step by step.
You will start with how machines see and prepare data the types of data AI works with, numerical challenges like underflow and overflow, and why normalization is essential before any model can learn. From there, you will build up the mathematical intuition behind AI: vectors, dot products, cosine similarity, and the one equation that serves as the backbone of all machine learning.
Then you will explore how machines actually learn. You will understand loss functions, gradient descent, learning rate, and what "training a model" truly means not as a buzzword, but as a concrete mathematical process. You will compare supervised, unsupervised, self-supervised, and reinforcement learning, and discover that modern LLMs combine three of these approaches.
Finally, you will arrive at the transformer architecture the breakthrough that powers every major Generative AI model today. You will learn tokenization, positional encoding, the attention mechanism (including query, key, and value matrices), multi-head attention, and how encoder-only, decoder-only, and encoder-decoder models serve different purposes across the industry.
By the end of this course, you will be able to
• Explain how data flows through an AI model from raw input to generated output
• Read and understand AI research summaries, technical blog posts, and architecture diagrams
• Have informed conversations about AI strategy with technical teams and leadership
• Evaluate AI tools and vendor claims with real conceptual understanding
• Build a strong foundation for further study in prompt engineering, fine-tuning, or AI development
What makes this course different?
• Concept-first approach: Every idea is explained visually on a drawing board before any formula appears
• Progressive learning path: Each lesson builds directly on the previous one no knowledge gaps
• Real-world connections: Every mathematical concept is tied back to its actual role in Generative AI
• Efficient and concise: Lessons are focused and to the point no filler content or unnecessary tangents
• Globally accessible: Clear, professional English with diverse, inclusive examples throughout
This course is designed for professionals and learners who want to understand AI at a conceptual level whether you are evaluating AI tools for your organization, preparing for a career transition, or simply want to be AI-literate in a world that increasingly demands it.
The concepts covered in this course are the same ones behind ChatGPT (GPT), Google Gemini, Anthropic Claude, Meta LLaMA, and every other large language model. Understanding them will give you a lasting advantage regardless of which specific model or tool leads the market tomorrow.
Enroll now and build the understanding that separates AI users from AI-literate professionals.
Who this course is for
■ Professionals who use AI tools daily (ChatGPT, Copilot, Claude) and want to understand how they actually work
■ Technical and non-technical managers who need to make informed decisions about AI adoption in their organizations
■ Data analysts, business analysts, and BI professionals looking to build foundational knowledge of AI and ML concepts
■ Software developers and engineers who want a clear conceptual foundation before diving into hands-on AI implementation
■ Career changers and students exploring AI, data science, or machine learning as a new professional direction
■ Anyone who tried learning AI before but was overwhelmed by heavy math or code-first approaches
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