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Natural Language Processing on Google Cloud

4.4 Filled star Filled star Filled star Filled star Empty star
Based on 536 ratings
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Google Cloud Training

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.

Read more

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

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What's inside

Syllabus

Course introduction
This module addresses the reasons to learn NLP from Google and provides an overview of the course structure and goals.
NLP on Google Cloud
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Offers hands-on labs and interactive materials that strengthen an existing foundation for intermediate learners
Taught by Google Cloud Training, an institution recognized for its work in cloud computing
Covers unique perspectives and ideas that may add color to other topics and subjects
Builds a strong foundation for beginners and develops professional skills in natural language processing
Teaches skills, knowledge, and tools that are highly relevant in an academic setting and in industry

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Reviews summary

Natural language processing on google cloud: reviewed

According to learners, this course provides a largely positive introduction to applying NLP on Google Cloud. Many found the focus on Vertex AI and AutoML highly relevant and practical for developing real-world NLP projects. The hands-on labs were frequently cited as a major strength, helping students apply concepts learned in the lectures. While the course covers various NLP models, including advanced topics like Transformers and BERT, some students felt the theoretical depth was limited. Reviewers suggest having a solid grasp of Python, TensorFlow, and potentially basic SQL before starting, as the course moves at a steady pace.
Introduces various NLP models, including advanced.
"The course provides a good overview of different NLP models, from DNNs to Transformers."
"It covers advanced topics like BERT and attention mechanisms, which is valuable."
"I liked that they touched upon modern LLMs, although briefly."
Engaging and helpful for reinforcing concepts.
"The hands-on labs are a great way to solidify the concepts presented in the lectures."
"I appreciated the labs that walk you through building models on Vertex AI."
"The practical exercises were the most valuable part of the course, truly cementing my understanding."
Highly relevant for applying NLP on GCP.
"The labs and focus on Vertex AI are fantastic for applying NLP knowledge directly on Google Cloud."
"This course is practical and helps you build an end-to-end NLP workflow using Google Cloud tools."
"I learned how to use AutoML effectively on Vertex AI for text classification, which is very useful."
"The practical application of NLP using cloud services was the highlight for me."
Good intro, but lacks depth for advanced learners.
"While the course introduces advanced models, it doesn't go into enough theoretical detail for those wanting a deep dive."
"Could use more in-depth explanations on the math behind the models."
"I felt some sections on the advanced models were a bit superficial."
Solid background in Python/TF is needed.
"Make sure you are comfortable with Python and TensorFlow before taking this course; otherwise, it might be challenging."
"The course moves quickly, assuming you have the stated prerequisites mastered."
"If you're not already familiar with GCP or TensorFlow, be prepared to do extra background reading."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Natural Language Processing on Google Cloud with these activities:
Organize course materials
Organize your course materials to stay on track and improve your understanding.
Show steps
  • Create a system for organizing your notes
  • Review your notes regularly
  • Create a study guide to help you prepare for exams
Review basic machine learning concepts
Review the underlying concepts to ensure strong foundational understanding.
Browse courses on Supervised Learning
Show steps
  • Revisit materials from previous machine learning courses
  • Read articles and blog posts about basic machine learning concepts
  • Work through practice problems and exercises
Natural Language Processing with Python by Steven Bird and Edward Loper
This book is an excellent resource for natural language processing. It covers a wide range of topics, from basic concepts to advanced techniques.
View Natural Language Processing with Python:... on Amazon
Show steps
  • Read the book
  • Take notes on the key concepts
  • Complete the exercises at the end of each chapter
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Work through TensorFlow tutorials
Become familiar with TensorFlow, which will be used in the course.
Browse courses on TensorFlow
Show steps
  • Follow the official TensorFlow tutorials
  • Complete the TensorFlow tutorials on Coursera
  • Experiment with different TensorFlow models
Review machine learning algorithms
To be successful in this course, you will need to have a solid understanding of machine learning algorithms, both supervised and unsupervised.
Browse courses on Machine Learning
Show steps
  • Review the different types of machine learning algorithms
  • Understand the strengths and weaknesses of each algorithm
  • Practice implementing machine learning algorithms in a programming language
TensorFlow tutorials for NLP
Follow TensorFlow tutorials to learn how to build and train NLP models. These tutorials will provide you with practical experience.
Browse courses on TensorFlow
Show steps
  • Find TensorFlow tutorials on NLP
  • Follow the tutorials and complete the exercises
  • Experiment with different NLP techniques
Practice NLP exercises
Gain practical experience applying NLP techniques.
Browse courses on NLP
Show steps
  • Solve NLP problems on platforms like LeetCode and HackerRank
  • Take part in NLP competitions or challenges
  • Work on personal NLP projects
NLP exercises with Python
This course will involve hands-on coding exercises using Python. Practice drills will help you build proficiency and confidence in writing NLP code.
Browse courses on NLP
Show steps
  • Find online resources with NLP exercises
  • Complete the exercises using Python
  • Debug and optimize your code
NLP study group
Join a study group to discuss NLP topics, share ideas, and help each other with assignments.
Browse courses on NLP
Show steps
  • Find a study group or create your own
  • Meet regularly to discuss NLP topics
  • Share ideas and help each other with assignments
NLP project using Vertex AI
Develop a project that solves a real-world NLP problem using Vertex AI. This will give you hands-on experience with the tools and techniques used in NLP.
Browse courses on NLP
Show steps
  • Identify a problem to solve
  • Gather data to train your model
  • Build and train an NLP model using Vertex AI
  • Deploy and evaluate your model
Contribute to an open source NLP project
Contribute to an open source NLP project to gain experience, learn from others, and build your portfolio.
Browse courses on Open Source
Show steps
  • Find an open source NLP project to contribute to
  • Read the project documentation and code
  • Identify an area where you can contribute
  • Submit a pull request with your contribution

Career center

Learners who complete Natural Language Processing on Google Cloud will develop knowledge and skills that may be useful to these careers:
NLP Engineer
Natural Language Processing on Google Cloud covers topics that an NLP Engineer would find valuable, such as NLP models, text representation, and advanced NLP models. The course will be especially valuable for Engineers who prefer Google Cloud for their work, since it includes specific instructions and hands-on activities using Google Cloud's offerings, such as AutoML and Vertex AI.
See salaries and explore the career path for NLP Engineer
Machine Learning Engineer
An integral part of Machine Learning is understanding and leveraging NLP for meaningful data analysis. This course will teach someone who wants to be a Machine Learning Engineer how to create NLP models using a variety of techniques, including neural networks. This course is especially valuable for those interested in Google Cloud, because it emphasizes Google Cloud products, such as TensorFlow, Vertex AI, and AutoML.
See salaries and explore the career path for Machine Learning Engineer
Data Scientist
Someone who enrolls in Natural Language Processing on Google Cloud may wish to ultimately become a Data Scientist. This course teaches students how to prepare text data, represent it in different ways, and build models for NLP. A Data Scientist may need to solve business problems; NLP is a key skill for those looking to analyze text data, which can provide valuable insights. Moreover, this course emphasizes foundational models such as ANNs and RNNs; a solid understanding of these is necessary for those wishing to advance in the Data Science field.
See salaries and explore the career path for Data Scientist
AI Engineer
Understanding NLP is a key skill for someone who wants to be an AI Engineer. This course is especially valuable for those interested in using Google Cloud's AI products, since it covers how to use the Dialogflow API, Contact Center AI, and Document AI.
See salaries and explore the career path for AI Engineer
Data Analyst
Someone who wishes to advance as a Data Analyst would benefit from a course like Natural Language Processing on Google Cloud. It teaches important foundational concepts for NLP, including text representation, NLP models, and advanced NLP models. This course covers how to use Google AutoML, which is particularly valuable to Data Analyst teams.
See salaries and explore the career path for Data Analyst
Software Engineer
NLP is a key area of expertise for Software Engineers. This course provides a solid foundation for general NLP knowledge and Google Cloud Platform (GCP) tools, such as Vertex AI and AutoML. It covers advanced topics like transfer learning and BERT, making it valuable for someone who wants to specialize in NLP.
See salaries and explore the career path for Software Engineer
Product Manager
A Product Manager specializing in AI or NLP should have a strong understanding of the field. This course covers core NLP principles and Google Cloud's NLP products and solutions. It may be especially valuable for those who want to build NLP-powered products.
See salaries and explore the career path for Product Manager
Business Analyst
A Business Analyst can use NLP to extract insights from text data. This course provides a foundation for NLP concepts and Google Cloud's NLP tools. It covers practical applications of NLP in business, making it valuable for someone who wants to use NLP to solve business problems.
See salaries and explore the career path for Business Analyst
Technical Writer
Technical Writers need to understand NLP to create clear and concise documentation for software and other technical products. This course provides a foundation for NLP concepts and Google Cloud's NLP tools. It covers topics such as text representation and NLP models, which are valuable for someone who wants to write about NLP-powered products.
See salaries and explore the career path for Technical Writer
Content Strategist
Content Strategists use NLP to analyze and optimize content for search engines and other platforms. This course provides a foundation for NLP concepts and Google Cloud's NLP tools. It covers topics such as text representation and NLP models, which are valuable for someone who wants to use NLP to improve content performance.
See salaries and explore the career path for Content Strategist
Customer Success Manager
Customer Success Managers can use NLP to analyze customer feedback and identify opportunities for improvement. This course provides a foundation for NLP concepts and Google Cloud's NLP tools. It covers topics such as text representation and NLP models, which are valuable for someone who wants to use NLP to improve customer experiences.
See salaries and explore the career path for Customer Success Manager
Sales Engineer
Sales Engineers use NLP to demonstrate the value of their products and services. This course provides a foundation for NLP concepts and Google Cloud's NLP tools. It covers topics such as text representation and NLP models, which are valuable for someone who wants to use NLP to close deals.
See salaries and explore the career path for Sales Engineer
Marketing Manager
Marketing Managers can use NLP to analyze customer data and create targeted marketing campaigns. This course provides a foundation for NLP concepts and Google Cloud's NLP tools. It covers topics such as text representation and NLP models, which are valuable for someone who wants to use NLP to improve marketing ROI.
See salaries and explore the career path for Marketing Manager
UX Designer
UX Designers can use NLP to improve the user experience of their products. This course provides a foundation for NLP concepts and Google Cloud's NLP tools. It covers topics such as text representation and NLP models, which are valuable for someone who wants to use NLP to create user-friendly interfaces.
See salaries and explore the career path for UX Designer
Recruiter
Recruiters can use NLP to find and qualify candidates for job openings. This course provides a foundation for NLP concepts and Google Cloud's NLP tools. It covers topics such as text representation and NLP models, which are valuable for someone who wants to use NLP to improve their recruiting process.
See salaries and explore the career path for Recruiter

Reading list

We've selected 14 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Natural Language Processing on Google Cloud.
Cover image
Cross-Lingual Word Embeddings
Save
Provides a comprehensive overview of deep learning for NLP. It covers a wide range of topics, from basic neural networks to advanced deep learning architectures.
Cross-Lingual Word Embeddings (Synthesis Lectures...
Paperback
$$
Cover image
Speech and Language Processing. An Introduction to...
Save
Provides a comprehensive overview of speech and language processing. It covers a wide range of topics, from basic phonetics to advanced natural language understanding.
Speech and Language Processing An Introduction to...
Paperback
$$
Foundations of Statistical Natural Language...
Save
Provides a comprehensive overview of the statistical foundations of NLP. It covers a wide range of topics, from basic probability theory to advanced machine learning techniques.
Foundations of Statistical Natural Language...
Paperback
Check
Foundations of Statistical Natural Language...
Kindle Edition
Check
Cover image
TensorFlow in Action
Save
Provides a comprehensive overview of transformers for NLP. It covers a wide range of topics, from basic transformer architectures to advanced applications.
TensorFlow in Action
Paperback
$$$
TensorFlow in Action
Kindle Edition
$$$
Cover image
Natural Language Processing with Python
Save
Provides a comprehensive overview of natural language processing (NLP) concepts and techniques, covering topics such as text preprocessing, part-of-speech tagging, named entity recognition, and machine translation. It valuable resource for both beginners and experienced NLP practitioners.
Natural Language Processing with Python: Analyzing...
Paperback
$$$
Natural Language Processing with Python: Analyzing...
Kindle Edition
$$
Cover image
Machine Learning with PyTorch and Scikit-Learn
Save
Provides a comprehensive overview of machine learning techniques for NLP tasks such as text classification, named entity recognition, and machine translation. It valuable resource for both beginners and experienced NLP practitioners.
Machine Learning with PyTorch and Scikit-Learn:...
Kindle Edition
$$
Cover image
Natural Language Processing in Action, Second...
Save
Provides a practical introduction to NLP. It covers a variety of topics, from text preprocessing to machine learning models.
Natural Language Processing in Action, Second...
Paperback
$$$
Cover image
The Elements of Statistical Learning
Save
Provides a comprehensive overview of statistical learning, covering topics such as linear regression, logistic regression, and support vector machines. It valuable resource for both undergraduate and graduate students.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
Cover image
Natural Language Processing with Python
Save
Provides a comprehensive overview of the Natural Language Toolkit (NLTK), a popular open-source library for NLP. It valuable resource for both beginners and experienced NLP practitioners.
Natural Language Processing with Python: Analyzing...
Paperback
$$$
(中文) Python Natural Language Processing(Chinese Edition)
Paperback
$$
Natural Language Processing with Python: Analyzing...
Kindle Edition
$$
Pattern Recognition and Machine Learning
Save
Provides a comprehensive overview of pattern recognition and machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for both undergraduate and graduate students.
Pattern Recognition and Machine Learning...
Hardcover
$$$
Pattern Recognition and Machine Learning...
Paperback
$$$
Pattern Recognition and Machine Learning (text...
Unknown Binding
$$$$
Cover image
Anaphora Resolution
Save
This handbook provides a comprehensive overview of NLP, covering topics such as text mining, machine translation, and speech recognition. It valuable resource for both researchers and practitioners in NLP.
Anaphora Resolution
Paperback
Check
Anaphora Resolution
Kindle Edition
Check
Cover image
Deep Learning
Save
Provides a comprehensive overview of deep learning, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for both undergraduate and graduate students.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Cover image
Reinforcement Learning, second edition
Save
Provides a comprehensive overview of reinforcement learning, covering topics such as Markov decision processes, value functions, and policy gradient methods. It valuable resource for both undergraduate and graduate students.
Reinforcement Learning, second edition: An...
Hardcover
$$$
Reinforcement Learning, second edition: An...
Kindle Edition
$$$
Cover image
Probabilistic Graphical Models
Save
Provides a comprehensive overview of probabilistic graphical models, covering topics such as Bayesian networks, Markov random fields, and Gaussian processes. It valuable resource for both undergraduate and graduate students.
Probabilistic Graphical Models: Principles and...
Hardcover
$$$$
Probabilistic Graphical Models: Principles and...
Kindle Edition
$$$

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Rating
4.4 Filled star Filled star Filled star Filled star Empty star
Effort
Approx. 10 hours
Level
Advanced
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Natural Language Processing on Google Cloud belongs to the following collections:
  1. Advanced Machine Learning on Google Cloud
  2. Advanced Machine Learning on Google Cloud
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Offers hands-on labs and interactive materials that strengthen an existing foundation for intermediate learners
Taught by Google Cloud Training, an institution recognized for its work in cloud computing
Covers unique perspectives and ideas that may add color to other topics and subjects
Builds a strong foundation for beginners and develops professional skills in natural language processing
Teaches skills, knowledge, and tools that are highly relevant in an academic setting and in industry
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