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Natural Language Processing with Transformers in Python - AD-TEAM - 11-06-2025
![[Image: b62b3c8b64847a1f7de0ffb3200ccad9.jpg]](https://i126.fastpic.org/big/2025/1106/d9/b62b3c8b64847a1f7de0ffb3200ccad9.jpg)
Natural Language Processing with Transformers in Python
Duration: 11h 23m | .MP4 1280x720, 30 fps® | AAC, 44100 Hz, 2ch | 3.28 GB
Genre: eLearning | Language: English
Learn next-generation NLP with transformers using PyTorch, TensorFlow, and HuggingFace!
What you'll learn
How to use transformer models for NLP
Modern natural language processing technologies
An overview of recent development in NLP
Python
Machine Learning
Natural Language Processing
Tensorflow
PyTorch
Transformers
Sentiment Analysis
Question and Answering
Named Entity Recognition
Requirements
Knowledge of Python
Experience with data science a plus
Experience with 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 learn all you need to know 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
More Info
![[Image: 51eIGu3R_o.jpg]](https://images2.imgbox.com/76/ff/51eIGu3R_o.jpg)
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