Rating 4.01 out of 5 (982 ratings in Udemy)

• 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