Udemy

LLMs Mastery: Complete Guide to Transformers & Generative AI

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  • 34,406 Students
  • Updated 1/2026
4.4
(7,904 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
7 Hour(s) 30 Minute(s)
Language
English
Taught by
The Fuzzy Scientist | LLM Expert
Rating
4.4
(7,904 Ratings)

Course Overview

LLMs Mastery: Complete Guide to Transformers & Generative AI

Generative AI, r1, LLMs, ChatGPT, GPT4, o1, Llama3, Decoders, T5, BERT, LoRA, FSDP, 4bit, Machine Learning, Data Science

Welcome to "LLMs Mastery: Complete Guide to Generative AI & Transformers"!

This practical course is designed to equip you with the knowledge and skills to build efficient, production-ready Large Language Models using cutting-edge technologies.


Key Topics Covered:

  • Generative AI: Understand the principles and applications of Generative AI in creating new data instances.

  • ChatGPT & GPT4: Dive into the workings of advanced AI models like ChatGPT and GPT4.

  • LLMs: Start with the basics of LLMs, learning how they decode, process inputs and outputs, and how they are taught to communicate effectively.

  • Encoder-Decoders: Master the concept of encoder-decoder models in the context of Transformers.

  • T5, GPT2, BERT: Get hands-on experience with popular Transformer models such as T5, GPT2, and BERT.

  • Machine Learning & Data: Understand the role of machine learning and data in training robust AI models.

  • Advanced Techniques: Sophisticated training strategies like PeFT, LoRa, managing data memory and merging adapters.

  • Specialised Skills:  Cutting-edge training techniques, including 8-bit, 4-bit training and Flash-Attention.

  • Scalable Solutions: Master the use of advanced tools like DeepSpeed and FSDP to efficiently scale model training.


Course Benefits:

Career Enhancement: Position yourself as a valuable asset in tech teams, capable of tackling significant AI challenges and projects.

Practical Application: Learn by doing—build projects that demonstrate your ability to apply advanced LLM techniques in real-world situations.

Innovative Approach: Stay at the forefront of AI technology by mastering techniques that are shaping the future of machine learning.



What You Will Learn:

Natural Language Processing Basics

Journey Through NLP Evolution: From rule-based systems to advanced embeddings.

Foundation in NLP: Set the stage for advanced learning in natural language processing.


Introduction to Transformers

Transformer Architecture: Learn about encoders, decoders, and attention mechanisms.

Model Strategies: Understand pre-training, fine-tuning, tokenization, and embeddings.


Popular Transformer Models

Explore Key Models: Dive into BERT, GPT, and T5 and their unique capabilities.

Deepen Model Insights: Uncover the potential and versatility of Transformer technology.


Using Transformers (Practical)

Hands-On Experience: Apply Transformers in real-world scenarios.

Advanced Techniques: Master tokenization, embeddings, and MLMs.

Project Implementation: Build a Semantic Search Index.


NLP Tasks and Applications (Practical)

Real-World Applications: Use BERT for question answering, GPT for personal assistants, and T5 for writing reviews.

Practical NLP Skills: Experience the direct application of NLP tasks.


Foundations of Large Language Models

Introduction to LLMs: Understand basic architecture and functionalities.

Communication Techniques: Enhance model responsiveness with RLHF.

Input/Output Processes: Explore how LLMs handle data for AI interactions.


Advanced Configuration and Optimization

Chat Template Design: Practical experience in structuring LLM interactions.

Model Selection Frameworks: Strategic decision-making for choosing LLMs.

Generation Techniques: Tailor LLM outputs through interactive learning.


Specialized Training Techniques

Advanced Model Training: Focus on sequence length, token counts, and numerical precision.

Efficiency Methods: Learn 8-bit and 4-bit training to adapt models to constraints.

Scaling Tools: Implement DeepSpeed and FSDP for efficient model scaling.


Practical Applications of LLMs

Application in Contexts: Apply LLM skills in simulated real-world projects.

Task-Specific Training: Optimize models for specific tasks like memory management and efficiency.




Who This Course Is For:

  • Tech Professionals: Enhance your skills and knowledge in cutting-edge AI technologies.

  • Aspiring AI Practitioners: Get a comprehensive education in LLMs from basic principles to advanced applications.

  • Researchers and Students: Gain a deep understanding of the latest developments and how they can be applied to solve complex problems.

Ready to dive into the world of Generative AI and Transformers?

Enroll today and start your journey to mastery!


Course Content

  • 10 section(s)
  • 52 lecture(s)
  • Section 1 1.1 Introduction: Course Overview + What You'll Learn
  • Section 2 1.2 Getting Started: How to Make the Best Use of this Course
  • Section 3 1.3 Overview of Natural Language Processing: Bring Transformers into Perspective
  • Section 4 1.4 Transformers Introduction: Important Concepts and Use-cases
  • Section 5 1.5 Popular Transformers Models: Choose the Best Model for the Job
  • Section 6 1.6 Using Transformers: Building Blocks and Hidden Gems (Practical)
  • Section 7 1.7 Mastering Real-World Scenarios with Transformers and LLMs (Practical)
  • Section 8 2.1 Introduction to Large Language Models
  • Section 9 2.2 Preparing for LLM Training (Practical)
  • Section 10 2.3 Advanced LLM Training Techniques (Practical)

What You’ll Learn

  • Grasp NLP Fundamentals: Understand the evolution from rule-based systems to advanced LLMs like Llama3, Gemma2, Phi3, and Mistral.
  • Master Transformers & LLMs: Learn the architecture and application of Transformers in depth. Including tokenization, embeddings, pre-training & fine-tunning.
  • Understand Generative AI Principles: Develop skills in building and fine-tuning generative models for real-world applications using RLHF and Chat-Templates
  • Use Transformer Models: Overview LLMs and Encoder-Decoder models like BERT, GPT, T5, Llama and more in many different NLP tasks: Personal assistant, Reviews, QA
  • Specialised Techniques: Implement 8-bit and 4-bit training, and use tools like DeepSpeed and FSDP, along with PeFT, LoRA, FlashAttention and more.


Reviews

  • B
    Brian Goad
    5.0

    Excellent level of detail and broad coverage of the AI ecosystem. Very useful!

  • S
    SHAILENDRA BALKRISHNA Kelkar
    3.0

    The axolotl git is not available on git as per the link. Please update that. The training of models becomes Incomprehensible because of it. Initial modules are good. But later on, it gets complicated.

  • P
    PEKKA HIPPELÄINEN
    4.5

    Good compact course. I had bit background in neural networks as being researcher in neural networks lab. Why I gave 4,5 stars, I missed bit more detailed explanation of embeddings forming and attention mechanism. However, I agree that having these included would make the content less accessible for its level.

  • F
    Faisal Alkheraiji
    3.5

    Great course overall. The course explain the theory part of LLMs nicely and in easy way to understand. My 3.5 stars are for the following: 1. The instructor didn't share the course slides although many requested in Q&A 2. The instructor is not active in Q&A section. 3. Some course materials are outdated. Many colab notebooks provided are not working due to the packages updated. 4. The instructor don't show how to follow the advanced lessons tutorials. He just said you need to rent GPUs and point to few resources. I was looking for how to connect these rented GPUs to my local environment in VScode for example

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