Master LLM: Large Language Models with Transformers

About this course

Unlock the power of modern Natural Language Processing (NLP) and elevate your skills with this comprehensive course on NLP with a focus on Transformers. This course will guide you through the essentials of Transformer models, from understanding the attention mechanism to leveraging pre-trained models. If so, then this course is for you what you need! We have divided this course into Chapters. In each chapter, you will be learning a new concept for Natural Language Processing with Transformers. These are some of the topics that we will be covering in this course:

Starting from an introduction to NLP and setting up your Python environment, you'll gain hands-on experience with text preprocessing methods, including tokenization, stemming, lemmatization, and handling special characters. You will learn how to represent text data effectively through Bag of Words, n-grams, and TF-IDF, and explore the groundbreaking Word2Vec model with practical coding exercises.

Dive deep into the workings of transformers, including self-attention, multi-head attention, and the role of position encoding. Understand the architecture of transformer encoders and decoders and learn how to train and use these powerful models for real-world applications.

The course features projects using state-of-the-art pre-trained models from Hugging Face, such as BERT for sentiment analysis and T5 for text translation. With guided coding exercises and step-by-step project walkthroughs, you’ll solidify your understanding and build your confidence in applying these models to complex NLP tasks.

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