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Language models

Transformers and Large Language Models

Explore language models and transformer concepts, including model families and fine-tuning techniques.

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Course topics

Language models

  • Transformer concepts
  • Model selection
  • LLM training

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For learners with basic Python and some machine-learning knowledge.

Starting point
Intermediate
Course length
7.5 total hours
Lessons
52 lectures

Course by The Fuzzy Scientist | LLM Expert

Course last updated: 01/2026

Requirements
  • Basic understanding of Python programming, some basic familiarity with machine learning concepts, and a strong eagerness to delve into LLMs and Natural Language Processing.
What is included
  • 7.5 hours on-demand video
  • 27 downloadable resources
  • 2 practice tests
  • Access on mobile
  • Certificate of completion

DoJo membership: A$250 per month per seat, excluding GST. Includes fortnightly group support and a private member community.

Explore the lessons

Course curriculum.

12 sections • 52 lectures • 7h 30m total length

1.1 Introduction: Course Overview + What You'll Learn2 lectures • 17min
  1. Introduction to Part 1 of the Course: Transformers Fundamentals09:51
  2. Introduction to Part 2 of The Course: LLMs06:44
1.2 Getting Started: How to Make the Best Use of this Course2 lectures • 7min
  1. Course Structure: How to get the Most out of this Course04:21
  2. Environment Setup: Prepare and Use the Resource of this Course Right03:03
1.3 Overview of Natural Language Processing: Bring Transformers into Perspective4 lectures • 22min
  1. Rule-Based Systems Era04:30
  2. Statistical Era05:34
  3. Machine Learning Era06:01
  4. Embeddings Era06:21
  5. Embeddings Era Quiz 2 questions
1.4 Transformers Introduction: Important Concepts and Use-cases3 lectures • 37min
  1. Encoders, Decoders and The Attention Mechanism14:03
  2. Understanding the Transformer Architecture 2 questions
  3. Pre-training & Fine-tunning12:10
  4. Tokenisation & Embeddings10:41
  5. Pre-training and Fine-tuning Transformers 1 question
1.5 Popular Transformers Models: Choose the Best Model for the Job3 lectures • 33min
  1. BERT13:05
  2. GPT10:30
  3. T509:24
  4. Exploring T5: The Text-to-Text Transfer Transformer 2 questions
1.6 Using Transformers: Building Blocks and Hidden Gems (Practical)5 lectures • 45min
  1. Building Blocks04:30
  2. Tokenizers14:24
  3. Understanding Tokenizers for Transformers 2 questions
  4. Word Embeddings11:02
  5. Masked Language Modeling (MLM)06:04
  6. Masked Language Modeling Quizz 2 questions
  7. Semantic Search Index09:07
1.7 Mastering Real-World Scenarios with Transformers and LLMs (Practical)3 lectures • 44min
  1. BERT (Encoder-model) for Extractive Question Answering18:20
  2. GPT (Decoder-model) for Instruction Following12:50
  3. Understanding GPT Instruct 2 questions
  4. T5 (Encoder-Decoder-model) for Writing Product Reviews12:52
  5. NLP Evolution and Fundamentals 30 questions
2.1 Introduction to Large Language Models6 lectures • 48min
  1. Decoding Large Language Models: An Introduction08:31
  2. Introduction to Large Language Models (LLMs) 2 questions
  3. RLHF: Teaching LLMs to Communicate Effectively08:33
  4. Understanding Input/Output in Language Models05:43
  5. Chat Templates: Hands On Overview07:10
  6. Decision Frameworks for LLM Selection07:20
  7. Generation: An Interactive Guide10:22
2.2 Preparing for LLM Training (Practical)7 lectures • 56min
  1. Comprehensive Dive into Sequence Length09:51
  2. Token Counts: Practical Intuition & Impact07:30
  3. Precision Matters: Numerical Precision in Training13:32
  4. Sequences and Tokens 6 questions
  5. Navigating GPU Selection: A Guide to Hardware Platforms06:00
  6. Practice Fundamentals: Most Basic Form of Training LLMs04:43
  7. Practice Fundamentals: Most Basic Form of Training LLMs - Part 208:35
  8. Practice Fundamentals: Most Basic Form of Training LLMs - Part 305:22
2.3 Advanced LLM Training Techniques (Practical)6 lectures • 59min
  1. Understanding Practical Limitations05:15
  2. Boosting Efficiency: PeFT and LoRa in Depth13:37
  3. Managing Data Memory: Batch Size & Sequence Length08:00
  4. Advanced Solutions: Gradient Accumulation & Checkpointing13:33
  5. Fitting Giants: Practical Introduction to LoRA for Large Models10:05
  6. Expanding LoRA: Adapter Merging and Effective Evaluations08:04
  7. Advanced Techniques 8 questions
2.4 Exploring Specialised LLM Training Techniques (Practical)5 lectures • 38min
  1. Level-Up Giants: 8-bit Training for Massive Models06:42
  2. Task-Focused Training: Aim for Better Learning - Part 109:21
  3. Task-Focused Training: Aim for Better Learning - Part 205:01
  4. Edge of Hardware Limits: Scaling Inputs with Flash Attention 208:15
  5. Edge of Hardware Limits: Reaching 4bit Training with QLoRA08:32
  6. Specialised Techniques 6 questions
2.5 Scale LLM Training with Advanced Tools (Practical)6 lectures • 45min
  1. Starting Strong: Introductory Concepts for at Scale Training05:01
  2. Understanding DeepSpeed: A Theoretical Overview06:30
  3. Implementing DeepSpeed: A Hands-On Approach08:01
  4. Scaling Training 3 questions
  5. FSDP Explained: Theoretical Insights07:31
  6. Applying FSDP: Real-World Usage and Best Practices08:16
  7. Wrapping Up: Course Conclusion, Recap, and Future Directions10:04
  8. LLM Mastery Practice Test 31 questions

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