Natural Language Processing on Google Cloud

This course is part of Advanced Machine Learning on Google Cloud Specialization

Instructor: Google Cloud Training

Access provided by Inter IKEA

23,144 already enrolled

7 modules

Gain insight into a topic and learn the fundamentals.

540 reviews

Advanced level

Designed for those already in the industry

Flexible schedule

9 hours to complete

Learn at your own pace

78%

Most learners liked this course

7 modules

Gain insight into a topic and learn the fundamentals.

540 reviews

Advanced level

Designed for those already in the industry

Flexible schedule

9 hours to complete

Learn at your own pace

78%

Most learners liked this course

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

5 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the Advanced Machine Learning on Google Cloud Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 7 modules in this course

This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.

- Recognize the NLP products and the solutions on Google Cloud. - Create an end-to-end NLP workflow by using AutoML with Vertex AI. - Build different NLP models including DNN, RNN, LSTM, and GRU by using TensorFlow. - Recognize advanced NLP models such as encoder-decoder, attention mechanism, transformers, and BERT. - Understand transfer learning and apply pre-trained models to solve NLP problems. Prerequisites: Basic SQL, familiarity with Python and TensorFlow

This module addresses the reasons to learn NLP from Google and provides an overview of the course structure and goals.

What's included

2 videos1 reading

This module introduces the NLP architecture on Google Cloud. It explores the NLP history, the NLP APIs such as the Dialogflow API, and the NLP solutions such as Contact Center AI and Document AI.

What's included

7 videos1 reading1 assignment

This module explores AutoML and custom training, which are the two options to develop an NLP project with Vertex AI. Additionally, the module introduces an end-to-end NLP workflow and provides a hands-on lab to apply the workflow to solve a task of text classification with AutoML.

What's included

7 videos1 reading1 assignment

This module describes the process to prepare text data in NLP and introduces the major categories of text representation techniques.

What's included

8 videos1 reading1 assignment1 app item1 plugin

8 videosTotal 36 minutes

Introduction2 minutes
Tokenization6 minutes
One-hot encoding and bag-of-words7 minutes
Word embeddings4 minutes
Word2vec9 minutes
Transfer learning and reusable embeddings3 minutes
Lab introduction: Reusable Embeddings1 minute
Summary3 minutes

1 readingTotal 10 minutes

Reading list10 minutes

1 assignmentTotal 8 minutes

Quiz8 minutes

1 app itemTotal 60 minutes

Lab: Reusable Embeddings60 minutes

1 pluginTotal 15 minutes

Accessing and completing labs15 minutes

This module describes different NLP models including ANN, DNN, RNN, LSTM, and GRU. It also introduces the benefits and disadvantages of each model.

What's included

9 videos1 reading1 assignment1 app item

9 videosTotal 48 minutes

Introduction2 minutes
ANN11 minutes
TensorFlow6 minutes
DNN7 minutes
RNN6 minutes
LSTM8 minutes
GRU2 minutes
Lab introduction: Text Classification with Keras2 minutes
Summary3 minutes

1 readingTotal 10 minutes

Reading list10 minutes

1 assignmentTotal 6 minutes

Quiz6 minutes

1 app itemTotal 120 minutes

Lab: Text Classification with Keras120 minutes

This module introduces the state-of-the-art technologies and models in NLP: encoder-decoder, attention mechanism, transformers, BERT, and large language models.