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Natural Language Processing: NLP With Transformers in Python

In partnership With
Udemy

Topic:

Learn next-generation NLP with transformers for sentiment analysis, Q&A, similarity search, NER, and more

 

What you'll 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

 

Requirements:

  • Knowledge of Python

  • Experience in data science a plus

  • Experience in NLP a plus

 

Description:

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!

 

Who this course is for:

  • Aspiring data scientists and ML engineers interested in NLP

  • Practitioners looking to upgrade their skills

  • Developers looking to implement NLP solutions

  • Data scientist

  • Machine Learning Engineer

  • Python Developers

 

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Learn cutting-edge natural language processing techniques to process speech and analyze text. Build probabilistic and deep learning models, such as hidden Markov models and recurrent neural networks, to teach the computer to do tasks such as speech recognition, machine translation, and more!

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