Working with Google BERT: Elements of BERT
Artificial Intelligence
| Intermediate
- 15 videos | 1h 8m
- Includes Assessment
- Earns a Badge
Adopting the foundational techniques of natural language processing (NLP), together with the Bidirectional Encoder Representations from Transformers (BERT) technique developed by Google, allows developers to integrate NLP pipelines into their projects efficiently and without the need for large-scale data collection and processing. In this course, you'll explore the concepts and techniques that pave the foundation for working with Google BERT. You'll start by examining various aspects of NLP techniques useful in developing advanced NLP pipelines, namely, those related to supervised and unsupervised learning, language models, transfer learning, and transformer models. You'll then identify how BERT relates to NLP, its architecture and variants, and some real-world applications of this technique. Finally, you'll work with BERT and both Amazon review and Twitter datasets to develop sentiment predictors and create classifiers.
WHAT YOU WILL LEARN
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discover the key concepts covered in this course
compare approaches to supervised and unsupervised learning in NLP
define the concept of language models and recognize their purpose
list multiple legacy language models and their use cases
describe how deep learning neural networks can create language models
name state-of-the-art language models and recognize their utility
describe the purpose of language representation in NLP pipelines and neural network models
outline how developers make use of transfer learning -
describe the concept and purpose of transformer models
describe Google BERT and how it is used in NLP products
outline Google BERT's architecture and list use cases of its variants
name multiple real-world problems in NLP that are solved by Google BERT
work with an Amazon review dataset and Google BERT to develop sentiment predictors
work with a Twitter dataset and Google BERT to create disaster Tweet classifiers
summarize the key concepts covered in this course
IN THIS COURSE
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1m 11s
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5m 13sLearn how to compare supervised and unsupervised learning approaches in NLP. FREE ACCESS
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3. The Purpose of Language Models5mIn this video, you will learn how to define the concept of language models and their purpose. FREE ACCESS
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4. Legacy Language Models6m 45sUpon completion of this video, you will be able to list multiple legacy language models and their use cases. FREE ACCESS
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5. Deep Learning-based Language Models6m 21sUpon completion of this video, you will be able to describe how deep learning neural networks can create language models. FREE ACCESS
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6. Current State-of-art Language Models6m 37sUpon completion of this video, you will be able to name state-of-the-art language models and understand their usefulness. FREE ACCESS
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7. The Purpose of Language Representation2m 6sAfter completing this video, you will be able to describe the purpose of language representation in NLP pipelines and neural network models. FREE ACCESS
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8. Transfer Learning in NLP3m 34sIn this video, you will outline how developers use transfer learning. FREE ACCESS
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9. The Purpose of Transformer Models4m 59sUpon completion of this video, you will be able to describe the concept and purpose of transformer models. FREE ACCESS
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10. Google BERT and NLP5m 34sAfter completing this video, you will be able to describe Google BERT and how it is used in NLP products. FREE ACCESS
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11. BERT Architecture and Variants4mIn this video, you will outline Google BERT's architecture and list variants' use cases. FREE ACCESS
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12. NLP Problems Solved by BERT1m 51sUpon completion of this video, you will be able to name multiple real-world problems in NLP that Google BERT solves. FREE ACCESS
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13. Developing an Amazon Review Sentiment Predictor7m 2sFind out how to work with an Amazon review dataset and Google BERT to develop sentiment predictors. FREE ACCESS
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14. Creating a Disaster Tweet Classifier6m 56sLearn how to work with a Twitter dataset and Google BERT to create a disaster Tweet classifier. FREE ACCESS
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15. Course Summary52sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
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