What you'll learn

Industry standard NLP using transformer models

Build full-stack question-answering transformer models

Perform sennt 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

They have proven themselves as the most expressive, powerful models for language by a large ma, beating all major language-based benchmarks and 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/sennt 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 sennt 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 eeers interested in NLP

Practitioners looking to upgrade their skills

Developers looking to implement NLP solutions

Data scientist

Machine Learning Eeer

Python Developers




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