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Natural Language Processing: NLP With Transformers in Python
Learn next-generation NLP with transformers for sentiment analysis, Q&A, similarity search, NER, and more
This Course Includes
udemy
3.5 (2.3K reviews )
11h 30m 
english
Online - Self Paced
professional certificate
Udemy
About Natural Language Processing: NLP With Transformers in Python
Transformer models are the de-facto standard in modern NLP. They have proven themselves as the most expressive, powerful models for language by a large margin, beating all major language-based benchmarks time and time again. In this course, we cover everything you need to get started with building cutting-edge performance NLP applications using transformer models like Google AI's
BERT
, or Facebook AI's
DPR
. We cover several key NLP frameworks including:
HuggingFace's Transformers
TensorFlow 2
PyTorch
spaCy
NLTK
Flair And learn how to apply transformers to some of the most popular NLP use-cases:
Language classification/sentiment analysis
Named entity recognition (NER)
Question and Answering
Similarity/comparative learning Throughout each of these use-cases we work through a variety of examples to ensure that what, how, and why transformers are so important. Alongside these sections we also work through
two full-size NLP projects
, one for sentiment analysis of financial Reddit data, and another covering a fully-fledged open domain question-answering application. All of this is supported by several other sections that encourage us to learn how to better design, implement, and measure the performance of our models, such as:
History of NLP and where transformers come from
Common preprocessing techniques for NLP
The theory behind transformers
How to fine-tune transformers We cover all this and more, I look forward to seeing you in the course!
What You Will Learn?
- Industry standard NLP using transformer models .
- Build full-stack question-answering transformer models .
- Perform sentiment analysis with transformers models in PyTorch and TensorFlow .
- Advanced search technologies like Elasticsearch and Facebook AI Similarity Search (FAISS) .
- Create fine-tuned transformers models for specialized use-cases .
- Measure performance of language models using advanced metrics like ROUGE .
- Vector building techniques like BM25 or dense passage retrievers (DPR) .
- An overview of recent developments in NLP .
- Understand attention and other key components of transformers .
- Learn about key transformers models such as BERT .
- Preprocess text data for NLP .
- Named entity recognition (NER) using spaCy and transformers .
- Fine-tune language classification models Show moreShow less.