Name: Exploring The Technologies Behind GPT-5, o3, BERT & AI Agent
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Created byJustin Coleman, Tim Reynolds

Last updated 1/2026

English

What you'll learn

Identify and select the most suitable transformer-based model for specific NLP tasks.
Comprehend how transformers process text and generate predictions.
Fine-tune transformer-based models with custom datasets.
Develop and implement actionable pipelines using fine-tuned models.
Deploy fine-tuned models for production use.
Perform effective prompt engineering for optimal outputs from GPT-o1 and ChatGPT.
Understand the concepts of attention mechanisms and their application in NLP.
Grasp the principles of transfer learning and its role in NLP.
Utilize BERT for natural language understanding tasks.
Conduct pre-training and fine-tuning of BERT models.
Apply hands-on experience with BERT for various NLP tasks.
Explore natural language generation using GPT models.
Gain practical experience with GPT models for text generation tasks.
Integrate BERT and GPT models for advanced NLP applications.
Understand the fundamentals and applications of the T5 model.
Engage in hands-on projects with T5 for different NLP tasks.
Deploy transformer models in real-world scenarios.
Utilize massively large language models effectively.
Apply best practices and strategies for using ChatGPT and other LLMs in various applications.

Course content

7 sections • 84 lectures • 9h 23m total length

Introduction to the Course2:59
Explore the technologies behind massive language models like GPT-4 and beyond, compare parameters from hundreds of millions to hundreds of billions, and learn to interact via playgrounds and APIs.
Welcome Message0:59

Understanding Large Language Models6:34
The Evolution of Natural Language Processing Techniques7:22
Understanding Attention Mechanisms in Deep Learning7:46
Fundamentals of Encoder-Decoder Architectures6:52
Introduction to Transformer Models6:54
Detailed Exploration of Transformer Mechanics2:21
Deep Dive into Scaled Dot Product Attention - Part 113:30
Understanding Transformers and Attention in NLP (Updated)1:21
Deep Dive into Scaled Dot Product Attention - Part 24:53
Deep Dive into Scaled Dot Product Attention - Part 310:12
Comprehensive Study of Multi-Headed Attention - Part 17:49
Comprehensive Study of Multi-Headed Attention - Part 28:50
Exploring Transfer Learning Techniques - Part 18:30
Exploring Transfer Learning Techniques - Part 25:58
Getting Started with PyTorch for NLP10:02
Streamlining Transformer Models with PyTorch6:35
Introduction to BERT: The New NLP Paradigm - Part 110:02
Explore BERT, the bi-directional encoder representation from transformers, and how its encoder stack creates context-rich token embeddings via CLS and SEP tokens, tokenization, embeddings, and multi-headed self-attention learned through pre-training.
Introduction to BERT: The New NLP Paradigm - Part 211:36
Techniques for Effective Wordpiece Tokenization - Part 110:16
Techniques for Effective Wordpiece Tokenization - Part 213:55
Understanding BERT's Embedding Capabilities - Part 16:00
Understanding BERT's Embedding Capabilities - Part 29:02
Advanced Language Modeling with BERT - Part 13:49
Advanced Language Modeling with BERT - Part 26:25
Next Sentence Prediction Techniques with BERT - Part 13:43
Next Sentence Prediction Techniques with BERT - Part 26:45
Explore how BertForNextSentencePrediction uses a CLS-based representation and an NSP head to predict sentence order, tokenize two sentences, and analyze logits and loss for accurate two-sentence relationships.
Advanced Applications of BERT in NLP Tasks - Part 15:14
Advanced Applications of BERT in NLP Tasks - Part 29:30
An Overview of BERT Variants7:27
BERT Variants: Advanced Concepts5:20
Basics of Sequence Classification with BERT4:54
Advanced Sequence Classification Techniques with BERT10:44
Practical Applications of BERT in Sequence Classification22:17
Foundations of Token Classification with BERT8:03
Advanced Token Classification Strategies Using BERT9:25
Core Concepts of Question Answering with BERT10:07
In-Depth Analysis of BERT for Question Answering11:41
GPT Model Architecture: An Introduction7:25
Innovative Applications of the GPT Architecture10:32
Techniques in Masked Multi-Head Attention9:22
Advanced Uses of Masked Multi-Head Attention7:40
Practical Implementations of Multi-Head Attention10:26
Strategies for Efficient GPT Pre-Training5:41
Optimizing GPT for Enhanced Performance3:09
Introduction to Few-Shot Learning with GPT3:26
Advanced Few-Shot Learning Techniques9:50
Basics of Stylistic Completion with GPT7:27
Advanced Stylistic Completion Techniques with GPT7:51
Implementing Efficient Code Dictation with GPT7:23
Mastering GPT for Code Dictation: Deep Dive8:16
GPT for Code Dictation: Advanced Implementation and Strategies9:28
Exploring Siamese BERT Networks for Semantic Analysis7:11
Deep Dive into Siamese BERT Networks for Semantic Analysis8:04
Advanced Siamese BERT Networks for Semantic Interpretation8:23
Siamese BERT Networks: Semantic Interpretation Case Studies11:43
Multitasking with GPT: An Introductory Approach6:00
Advancing Multi-Task Performance in GPT6:41
Practical Applications of Multitasking with GPT6:11
Introduction to T5's Encoder-Decoder Architecture4:31
Principles and Techniques of Pre-training the T5 Model2:44
Understanding Cross-Attention Mechanisms in T55:28
Exploring Pre-Trained T5 Models: Fundamentals5:48
Advanced Applications of Pre-Trained T5 Models8:11
Implementing Abstractive Summarization with T5: Core Techniques8:43
Explore implementing abstractive summarization with t5, fine-tuning pre-trained language models for sequence-to-sequence tasks, using a 5,000-example reviews dataset with a summarize prefix and encoder-decoder training.
Enhancing Techniques in Abstractive Summarization with T56:58

Exploring RLHF's Role in Advancing ChatGPT2:20
Fundamental Principles of Reinforcement Learning4:44
Mastering OpenAI Software for Reinforcement Learning1:35
A Comprehensive Introduction to Reinforcement Learning2:26
Advanced Concepts and Strategies in Reinforcement Learning3:21
Understanding OpenAI Frameworks for Developers4:42
Practical Exploration of OpenAI Environments0:39
Theoretical and Applied Aspects of Markov Decision Processes1:48
Solving Complex Challenges in Reinforcement Learning5:29

Requirements

Basic Python programming knowledge is helpful but not required
Internet Access

Description

Unlock the Next Generation of AI: Master GPT-5, o3, and the Agentic Revolution!

(Freshly Updated for 2026! Covers the latest "Reasoning" models and Autonomous Agents)

Are you ready to move beyond basic chatbots and master the intelligence engines that are reshaping the world? Welcome to "LLM Mastery 2026"—your definitive launchpad into the advanced world of Large Language Models (LLMs) and Generative AI.

The AI landscape has shifted. It is no longer just about generating text; it is about Reasoning and Agency. This course takes you from the foundations of BERT and Transformers all the way to building autonomous AI Agents powered by GPT-5 and OpenAI’s o3 (Reasoning) series.

Why This Course is Your Career Accelerator in 2026: In today's market, "knowing how to use ChatGPT" is not enough. Employers and clients demand engineers who can build systems—applications that can think, plan, and execute tasks autonomously.

This course is meticulously crafted for:

Aspiring AI Engineers: Build your first "Agentic" workflow.
Data Scientists: Fine-tune open-source models (Llama 3, Mistral) on your own data.
Software Developers: Integrate "Reasoning" capabilities (o3/o1) into your apps.
Researchers & Students: Deeply understand the math behind Attention Mechanisms and Transformers.

What Awaits You Inside? We don't just teach theory; we build. You will code alongside us using PyTorch and Jupyter Notebooks.

Decode the Foundations Move beyond the hype. Understand the architecture of BERT, T5, and GPT. Learn how "Attention" changed everything and how tokens become intelligence.

Master Reasoning Models (o3 & o1) Learn the secrets behind OpenAI’s "Reasoning" models. Understand Chain-of-Thought (CoT) prompting and how to optimize for models that "think" before they speak.

Build Autonomous AI Agents The future is Agentic. Learn to build AI systems that can browse the web, write code, and use tools to solve complex problems autonomously using frameworks like LangGraph or custom Python logic.

Advanced Fine-Tuning Stop relying on generic APIs. Learn to Fine-Tune LLMs on your custom datasets to create specialized models for medical, legal, or coding tasks.

Multimodal AI & Vision Explore the frontier of Vision Transformers (ViT) and Multimodal models that can see, hear, and speak—powering the next wave of robotics and real-world AI.

Production-Grade RAG Build "Retrieval Augmented Generation" systems that don't hallucinate. Connect your LLM to live data sources, PDFs, and databases to build truly knowledgeable assistants.

Ignite Your Career & Become an AI Powerhouse:

Portfolio-Ready Projects: Build a Sentiment Analyzer, a Medical Summarization Bot, and a fully functional Autonomous Research Agent.
Interview Dominance: We cover the technical questions top tech companies ask—from "What is a Key/Query/Value vector?" to "How do you reduce hallucination?"
Future-Proof Skills: Master the tools of 2026, ensuring your skills stay relevant for years to come.

Join the Vanguard of the AI Revolution. The difference between watching the future and building it is this course. Don't let the AI revolution outpace you.

Enroll NOW to master GPT-5, o3, and the future of Agentic AI!

Who this course is for:

Anyone interested in ChatGPT
Anyone interested in Reinforcement Learning from Human Feedback (RLHF)
Aspiring data scientists, machine learning engineers, and NLP practitioners
Anyone who want to gain a solid understanding of transformer models and their applications in modern natural language processing tasks.
Software developers seeking to enhance their productivity and leverage the power of NLP models for innovative applications in their work.

What you'll learn

Explore related topics

Course content

Welcome2 lectures • 4min

Getting Started with Large Language Models65 lectures • 8hr 26min

Course materials - Notebooks & Data1 lecture • 1min

Introduction to Reinforcement Learning9 lectures • 27min

Solving Reinforcement Learning Problems with OpenAI/ChatGPT4 lectures • 12min

Deep Reinforcement Learning Applications2 lectures • 13min

Thank You1 lecture • 1min

Requirements

Description

Who this course is for: