Learn BERT - essential NLP algorithm by Google

Understand and apply Google's game-changing NLP algorithm to real-world tasks. Build 2 NLP applications.

4.27 (1196 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Understand the history about BERT and why it changed NLP more than any algorithm in the recent years

Understand how BERT is different from other standard algorithm and is closer to how humans process languages

Use the tokenizing tools provided with BERT to preprocess text data efficiently

Use the BERT layer as a embedding to plug it to your own NLP model

Use BERT as a pre-trained model and then fine tune it to get the most out of it

Explore the Github project from the Google research team to get the tools we need

Get models available on Tensorflow Hub, the platform where you can get already trained models

Clean text data

Create datasets for AI from those data

Use Google Colab and Tensorflow 2.0 for your AI implementations

Create customs layers and models in TF 2.0 for specific NLP tasks

Course Gallery

Learn BERT - essential NLP algorithm by Google – Screenshot 1 — Screenshot 1 – Learn BERT - essential NLP algorithm by Google

Learn BERT - essential NLP algorithm by Google – Screenshot 2 — Screenshot 2 – Learn BERT - essential NLP algorithm by Google

Learn BERT - essential NLP algorithm by Google – Screenshot 3 — Screenshot 3 – Learn BERT - essential NLP algorithm by Google

Learn BERT - essential NLP algorithm by Google – Screenshot 4 — Screenshot 4 – Learn BERT - essential NLP algorithm by Google

Loading charts...

Comidoc Review

Our Verdict

This course offers a solid introduction to BERT and how it has revolutionized NLP. It covers essential topics like tokenizing text data, creating NLP applications, and implementing the BERT layer as an embedding in your models. However, aspiring learners should be prepared for uneven complexity levels in explanations and limited practical application of fine-tuning. The course could benefit from incorporating more visual elements to aid in understanding complex theories and reducing reliance on slides.

What We Liked

Covers the history and reasoning behind BERT's impact on NLP
Introduction to tokenizing tools and using BERT layer as an embedding
Hands-on experience in building two NLP applications using BERT
Detailed explanation of word embeddings

Potential Drawbacks

Lack of live coding & visuals, making it hard for some students to follow
Theoretical explanations could be denser with added visualizations
Limited practical exploration of fine-tuning on specific tasks
Uneven explanation complexity hampers some students' understanding