Understanding transformer architecture is what separates using LLMs from actually understanding how and why they work — valuable for anyone building or fine-tuning language models.

What You’ll Learn

Text preprocessing and tokenization
Attention mechanisms and transformer architecture
Using and fine-tuning pretrained transformer models
Common NLP tasks: classification, NER, summarization

Curriculum

NLP Fundamentals & Tokenization
Word Embeddings
Attention Mechanisms
Transformer Architecture
Fine-Tuning Pretrained Models
Applied NLP Tasks
Capstone: NLP Application Project

Who Should Attend

ML engineers working on language-focused applications.

Prerequisites

Python and deep learning fundamentals.

Hands-on Labs

Practice what you learn.

Hands-on lab availability is shown in the course details and enrolment information.

Ask About Labs →

Related Courses

Keep going after this course.

Part Of A Larger Path

This course is one stage in the AI & Machine Learning Learning Path

View Full Path →

Ready to Start

Talk to an advisor about NLP & Transformer Models

Cohort dates, corporate training options, or how this fits your learning path.

📝 Quick Enquiry 💬 Chat with us