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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.
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.
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
Register for this course and see more details by visiting:
OpenCourser.com/course/n4xtwu/natural
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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
Read more
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
Read more
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.
NLP with Vertex AI
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.
Text representatation
This module describes the process to prepare text data in NLP and introduces the major categories of text representation techniques.
NLP models
This module describes different NLP models including ANN, DNN, RNN, LSTM, and GRU. It also introduces the benefits and disadvantages of each model.
Advanced NLP models
This module introduces the state-of-the-art technologies and models in NLP: encoder-decoder, attention mechanism, transformers, BERT, and large language models.
Course summary
This module reviews the topics covered in the course and provides additional resources for further learning.
Good to know
Good to know
Know what's good ,
what to watch for , 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
Save this course
Save Natural Language Processing on Google Cloud to your list so you can find it easily later:
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Reviews summary
Engaging nlp on gcp course
Learners say that this well-paced NLP course is great for beginners and offers helpful use cases for GCP tools. Despite some outdated code and labs, learners appreciate the engaging lectures and overviews of NLP concepts. Be aware that a few learners mentioned issues with labs not matching lecture content, incorrect quizzes, and errors due to incompatible TensorFlow versions.
Course is beginner-friendly.
"Very good course to start with NLP."
"Course explains basics and advance concepts from NLP with simple language."
Labs may have issues.
"Labs didn't work as expected."
"Tutorial content frequently doesn't match that described in the videos."
"I had issues with the tensor2tensor tutorial."
Some content is outdated.
"Great course, but no more up to date with TF2"
"Everything was fine except the solution videos are old, that why you should update with update code."
"The material needs to be updated."
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Organize course materials
Show steps
Organize your course materials to stay on track and improve your understanding.
Show steps
Show steps
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
Show steps
Review the underlying concepts to ensure strong foundational understanding.
Browse courses on
Supervised Learning
Show steps
Show steps
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
Show steps
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
Show steps
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
Show steps
Become familiar with TensorFlow, which will be used in the course.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Follow the official TensorFlow tutorials
- Complete the TensorFlow tutorials on Coursera
- Experiment with different TensorFlow models
Review machine learning algorithms
Show steps
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
Show steps
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
Show steps
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
Show steps
Show steps
- Find TensorFlow tutorials on NLP
- Follow the tutorials and complete the exercises
- Experiment with different NLP techniques
Practice NLP exercises
Show steps
Gain practical experience applying NLP techniques.
Browse courses on
NLP
Show steps
Show steps
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
Show steps
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
Show steps
Show steps
- Find online resources with NLP exercises
- Complete the exercises using Python
- Debug and optimize your code
NLP study group
Show steps
Join a study group to discuss NLP topics, share ideas, and help each other with assignments.
Browse courses on
NLP
Show steps
Show steps
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
Show steps
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
Show steps
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
Show steps
Contribute to an open source NLP project to gain experience, learn from others, and build your portfolio.
Browse courses on
Open Source
Show steps
Show steps
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
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.
Machine Learning 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.
Data Scientist
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.
AI Engineer
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.
Data Analyst
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.
Software Engineer
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.
Product Manager
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.
Business Analyst
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.
Technical Writer
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.
Content Strategist
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.
Customer Success Manager
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.
Sales Engineer
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.
Marketing Manager
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.
UX Designer
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.
Recruiter
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.
For more career information including salaries, visit:
OpenCourser.com/course/n4xtwu/natural
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.
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.
Provides a comprehensive overview of speech and language processing. It covers a wide range of topics, from basic phonetics to advanced natural language understanding.
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.
Save
Provides a comprehensive overview of transformers for NLP. It covers a wide range of topics, from basic transformer architectures to advanced applications.
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.
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.
Provides a practical introduction to NLP. It covers a variety of topics, from text preprocessing to machine learning models.
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.
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.
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.
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.
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.
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.
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/n4xtwu/natural
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Level
Advanced
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Good to know
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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