NLP Engineer – Transformers Specialist

Welcome to LSET’s Natural Language Processing (NLP) with Transformers Course, designed to equip you with skills in text analysis and AI. Explore tokenization, embeddings, attention, and fine-tuning BERT and GPT models. Ideal for data, finance, or healthcare professionals, this course offers hands-on experience with Hugging Face Transformers for sentiment analysis, question answering, and summarization. Join us to excel in NLP!

LSET’s Comprehensive Natural Language Processing (NLP) with Transformers Course offers an in-depth exploration of essential NLP principles and practices. Structured to provide both theoretical knowledge and practical skills, this course prepares you to analyse and process text data effectively. You will learn about tokenization, embeddings, attention mechanisms, and fine-tuning state-of-the-art models, equipping you with the expertise to tackle real-world applications in data, finance, or healthcare.

Technologies Covered in This Course

Hugging Face Transformers is a leading open-source library for implementing and deploying advanced NLP models. It provides pre-trained models like BERT and GPT for tasks such as text classification, question answering, and summarization.

BERT (Bidirectional Encoder Representations from Transformers) is a powerful pre-trained model for understanding context in text. It excels in tasks like sentiment analysis, named entity recognition, and question answering.

GPT (Generative Pre-trained Transformer) is a state-of-the-art model for generating human-like text and performing tasks like text completion, summarization, and conversational AI.

Tokenizers are essential tools for breaking down text into tokens for processing by NLP models. Popular libraries like Hugging Face’s Tokenizer and spaCy enable efficient text preprocessing.

Word Embeddings (e.g., Word2Vec, GloVe) capture semantic meanings of words in vector form, enabling models to understand relationships and context in text data.

Attention Mechanisms power Transformer models by focusing on relevant parts of input data, improving performance in tasks like machine translation and text summarization.

spaCy is an open-source library for advanced NLP tasks, offering efficient pipelines for tokenization, part-of-speech tagging, and dependency parsing.

PyTorch/TensorFlow are deep learning frameworks used for building, training, and fine-tuning Transformer models, providing flexibility for custom NLP solutions.

NLTK (Natural Language Toolkit) is a comprehensive library for text processing, offering tools for tokenization, stemming, lemmatization, and more, ideal for foundational NLP tasks.