Natural Language Processing: NLP With Transformers in Python

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

4.25 (2269 reviews)

Udemy

platform

English

language

Data Science

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

Loading charts...

Comidoc Review

Our Verdict

This natural language processing course excels in providing hands-on experience with Transformers in Python for various NLP tasks. While it covers essential aspects of attention mechanisms and recent developments, some users struggle with understanding the theory behind certain topics. Inconsistencies in chapter alignment and outdated code present areas for improvement, making this an advanced-level course for those with a solid NLP background.

What We Liked

The course provides a thorough exploration of NLP with Transformers in Python, covering industry-standard NLP using transformer models and full-stack question-answering transformer models.
Excellent hands-on approach through real-world applications, including sentiment analysis, named entity recognition (NER), and advanced search technologies like Elasticsearch and FAISS.
Clear explanations of attention mechanisms and key components of Transformers, as well as an overview of recent developments in NLP.

Potential Drawbacks

Some users find the theory behind certain topics like encoders, decoders, and attention mechanism challenging to grasp despite the engaging teaching style.
The course appears to be derived from a more comprehensive one, leading to misaligned chapters and unexplained concepts. Outdated code and environment setup issues also detract from the learning experience.