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It’s no secret that machine learning is one of the fastest-growing fields in tech, and the Google Cloud Platform has been instrumental in furthering its development. With a host of APIs, Google Cloud has a tool for just about any machine learning job. In this...
Read more
It’s no secret that machine learning is one of the fastest-growing fields in tech, and the Google Cloud Platform has been instrumental in furthering its development. With a host of APIs, Google Cloud has a tool for just about any machine learning job. In this...
Read more
It’s no secret that machine learning is one of the fastest-growing fields in tech, and the Google Cloud Platform has been instrumental in furthering its development. With a host of APIs, Google Cloud has a tool for just about any machine learning job. In this introductory Google Cloud Labs Series, you will get hands-on practice with machine learning as it applies to language processing by taking labs that will enable you to extract entities from text, and perform sentiment and syntactic analysis as well as use the Speech to Text API for transcription.
Note: you will have timed access to the online environment. You will need to complete the lab within the allotted time.
Register for this course and see more details by visiting:
OpenCourser.com/course/mnz2ti/introduction
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The course explores natural language processing, a critical topic in the tech industry, using Google Cloud Platform's APIs
The hands-on practice through labs lets learners apply their knowledge immediately
Google Cloud Training, the course provider, is a reputable name in the industry
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Reviews summary
Hands-on ml for nlp
This course provides an introduction to machine learning for natural language processing using Google Cloud Platform's (GCP) tools. The course includes hands-on labs that cover extracting entities from text, sentiment analysis, syntactic analysis, and using the Speech to Text API. Overall, reviewers found the course helpful for gaining practical experience with GCP's machine learning tools.
Provides practical experience with GCP's machine learning tools.
"With a host of APIs, Google Cloud has a tool for just about any machine learning job."
"In this introductory Google Cloud Labs Series, you will get hands-on practice with machine learning as it applies to language processing..."
May experience technical issues that can impact completion.
"I committed to completing this course in one week, but a technical issue did not allow me to access the final lab for over a week."
Focuses on using GCP tools rather than teaching machine learning concepts.
"Doesn't teach much about ML, just goes through tutorials of launching predefined workflows in google cloud."
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 Introduction to Machine Learning: Language Processing with these
activities:
Explore "Natural Language Processing with Python"
Show steps
Expand your understanding of NLP techniques by reviewing this introductory book, which covers fundamental NLP concepts, Python implementation, and practical applications.
View
Natural Language Processing with Python:...
on Amazon
Show steps
-
Read the relevant chapters of the book.
-
Work through the provided exercises and examples.
-
Summarize the key concepts and techniques presented in the book.
Review Text Processing
Show steps
Prepare for the course by reviewing basic text processing techniques and concepts, including natural language processing (NLP), string manipulation, and regular expressions.
Browse courses on
NLP
Show steps
-
Revisit basic NLP concepts like tokenization, stemming, and lemmatization.
-
Practice string manipulation techniques in your preferred programming language.
-
Experiment with regular expressions to match and extract patterns from text.
Explore Machine Learning APIs
Show steps
Familiarize yourself with the core Google Cloud APIs related to machine learning and NLP, such as the Natural Language API and the Cloud Vision API, to enhance your understanding of the course material.
Browse courses on
APIs
Show steps
-
Identify the available Google Cloud APIs relevant to machine learning.
-
Find and follow tutorials that demonstrate how to use these APIs in practice.
-
Explore the documentation and reference materials for the APIs.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Complete Hands-on Labs
Show steps
Solidify your understanding of the course concepts by actively participating in the provided hands-on labs, which offer practical exercises and real-world examples to reinforce your learning.
Browse courses on
Hands-on Labs
Show steps
-
Follow the lab instructions carefully and complete the exercises.
-
Troubleshoot any issues that arise during the labs.
-
Review the lab materials and reflect on your findings.
Assist Fellow Learners
Show steps
Reinforce your understanding by helping others learn. Engage in discussions, provide assistance to fellow learners, and actively participate in online forums related to the course topics or related technologies.
Show steps
-
Identify opportunities to share your knowledge and assist others.
-
Participate in online forums or discussion groups.
-
Offer help to fellow learners who may be facing challenges.
Develop a Machine Learning Project
Show steps
Apply your acquired knowledge to a practical project, demonstrating your ability to use machine learning and NLP techniques to solve real-world problems.
Show steps
-
Identify a problem statement and define the project scope.
-
Gather and prepare the necessary data.
-
Build and train a machine learning model.
-
Evaluate the model and make necessary adjustments.
-
Deploy the model and share your findings.
Participate in a Hackathon
Show steps
Challenge yourself and showcase your skills by participating in a hackathon focused on NLP or machine learning. Collaborate with others and apply your knowledge to solve complex problems.
Show steps
-
Find and register for a suitable hackathon.
-
Form a team or work individually.
-
Develop and present a solution to the challenge.
Explore "Natural Language Processing with Python"
Show steps
Expand your understanding of NLP techniques by reviewing this introductory book, which covers fundamental NLP concepts, Python implementation, and practical applications.
View
Natural Language Processing with Python:...
on Amazon
Show steps
Show steps
Show steps
- Read the relevant chapters of the book.
- Work through the provided exercises and examples.
- Summarize the key concepts and techniques presented in the book.
Review Text Processing
Show steps
Prepare for the course by reviewing basic text processing techniques and concepts, including natural language processing (NLP), string manipulation, and regular expressions.
Browse courses on
NLP
Show steps
Show steps
Show steps
- Revisit basic NLP concepts like tokenization, stemming, and lemmatization.
- Practice string manipulation techniques in your preferred programming language.
- Experiment with regular expressions to match and extract patterns from text.
Explore Machine Learning APIs
Show steps
Familiarize yourself with the core Google Cloud APIs related to machine learning and NLP, such as the Natural Language API and the Cloud Vision API, to enhance your understanding of the course material.
Browse courses on
APIs
Show steps
Show steps
Show steps
- Identify the available Google Cloud APIs relevant to machine learning.
- Find and follow tutorials that demonstrate how to use these APIs in practice.
- Explore the documentation and reference materials for the APIs.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Complete Hands-on Labs
Show steps
Solidify your understanding of the course concepts by actively participating in the provided hands-on labs, which offer practical exercises and real-world examples to reinforce your learning.
Browse courses on
Hands-on Labs
Show steps
Show steps
Show steps
- Follow the lab instructions carefully and complete the exercises.
- Troubleshoot any issues that arise during the labs.
- Review the lab materials and reflect on your findings.
Assist Fellow Learners
Show steps
Reinforce your understanding by helping others learn. Engage in discussions, provide assistance to fellow learners, and actively participate in online forums related to the course topics or related technologies.
Show steps
Show steps
Show steps
- Identify opportunities to share your knowledge and assist others.
- Participate in online forums or discussion groups.
- Offer help to fellow learners who may be facing challenges.
Develop a Machine Learning Project
Show steps
Apply your acquired knowledge to a practical project, demonstrating your ability to use machine learning and NLP techniques to solve real-world problems.
Show steps
Show steps
Show steps
- Identify a problem statement and define the project scope.
- Gather and prepare the necessary data.
- Build and train a machine learning model.
- Evaluate the model and make necessary adjustments.
- Deploy the model and share your findings.
Participate in a Hackathon
Show steps
Challenge yourself and showcase your skills by participating in a hackathon focused on NLP or machine learning. Collaborate with others and apply your knowledge to solve complex problems.
Show steps
Show steps
Show steps
- Find and register for a suitable hackathon.
- Form a team or work individually.
- Develop and present a solution to the challenge.
Career center
Learners who complete Introduction to Machine Learning: Language Processing will develop knowledge and skills
that may be useful to these careers:
Speech Recognition Engineer
Speech Recognition Engineer
Speech Recognition Engineers are responsible for developing and deploying models that can recognize human speech. They work with data scientists and machine learning engineers to develop models that can be used for a variety of applications, such as voice control, dictation, and language learning. This course will help you develop the skills you need to become a successful Speech Recognition Engineer, including hands-on experience with machine learning algorithms and techniques.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. They work with data scientists to identify the right problems to solve with machine learning, and then they develop and implement the models that will solve those problems. This course will help you develop the skills you need to become a successful Machine Learning Engineer, including hands-on experience with machine learning algorithms and techniques.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting large datasets. They use their skills in machine learning, statistics, and programming to develop models that can predict future outcomes or identify trends. This course will help you develop the skills you need to become a successful Data Scientist, including hands-on experience with machine learning algorithms and techniques.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers are responsible for developing and deploying models that can understand and generate human language. They work with data scientists and machine learning engineers to develop models that can be used for a variety of applications, such as machine translation, text summarization, and question answering. This course will help you develop the skills you need to become a successful Natural Language Processing Engineer, including hands-on experience with machine learning algorithms and techniques.
Computational Linguist
Computational Linguist
Computational Linguists are responsible for developing and deploying models that can understand and generate human language. They work with data scientists and machine learning engineers to develop models that can be used for a variety of applications, such as machine translation, text summarization, and question answering. This course will help you develop the skills you need to become a successful Computational Linguist, including hands-on experience with machine learning algorithms and techniques.
Information Retrieval Engineer
Information Retrieval Engineer
Information Retrieval Engineers are responsible for developing and deploying models that can search and retrieve information from large datasets. They work with data scientists and machine learning engineers to develop models that can be used for a variety of applications, such as search engines, recommender systems, and question answering. This course will help you develop the skills you need to become a successful Information Retrieval Engineer, including hands-on experience with machine learning algorithms and techniques.
Text Mining Analyst
Text Mining Analyst
Text Mining Analysts are responsible for extracting insights from text data. They use machine learning and statistical techniques to identify patterns and trends in text data, which can be used to make better decisions. This course will help you develop the skills you need to become a successful Text Mining Analyst, including hands-on experience with machine learning algorithms and techniques.
Recommender Systems Engineer
Recommender Systems Engineer
Recommender Systems Engineers are responsible for developing and deploying models that can recommend products, services, or content to users. They work with data scientists and machine learning engineers to develop models that can be used for a variety of applications, such as e-commerce, streaming services, and social media. This course will help you develop the skills you need to become a successful Recommender Systems Engineer, including hands-on experience with machine learning algorithms and techniques.
Machine Translation Engineer
Machine Translation Engineer
Machine Translation Engineers are responsible for developing and deploying models that can translate text from one language to another. They work with data scientists and machine learning engineers to develop models that can be used for a variety of applications, such as language learning, international business, and travel. This course will help you develop the skills you need to become a successful Machine Translation Engineer, including hands-on experience with machine learning algorithms and techniques.
Question Answering Engineer
Question Answering Engineer
Question Answering Engineers are responsible for developing and deploying models that can answer questions from text data. They work with data scientists and machine learning engineers to develop models that can be used for a variety of applications, such as search engines, educational software, and customer service. This course will help you develop the skills you need to become a successful Question Answering Engineer, including hands-on experience with machine learning algorithms and techniques.
Natural Language Generation Engineer
Natural Language Generation Engineer
Natural Language Generation Engineers are responsible for developing and deploying models that can generate human-like text. They work with data scientists and machine learning engineers to develop models that can be used for a variety of applications, such as chatbots, text summarization, and marketing. This course will help you develop the skills you need to become a successful Natural Language Generation Engineer, including hands-on experience with machine learning algorithms and techniques.
Conversational AI Engineer
Conversational AI Engineer
Conversational AI Engineers are responsible for developing and deploying models that can engage in natural language conversations with users. They work with data scientists and machine learning engineers to develop models that can be used for a variety of applications, such as chatbots, virtual assistants, and customer service. This course will help you develop the skills you need to become a successful Conversational AI Engineer, including hands-on experience with machine learning algorithms and techniques.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data. They use their skills in statistics and programming to identify trends and patterns in data, which can be used to make better decisions. This course will help you develop the skills you need to become a successful Data Analyst, including hands-on experience with machine learning algorithms and techniques.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and deploying software applications. They work with data scientists and machine learning engineers to develop and implement models that can be used for a variety of applications. This course will help you develop the skills you need to become a successful Software Engineer, including hands-on experience with machine learning algorithms and techniques.
Product Manager
Product Manager
Product Managers are responsible for the development and marketing of products. They work with data scientists and machine learning engineers to identify opportunities for new products and services and to develop and implement marketing campaigns. This course will help you develop the skills you need to become a successful Product Manager, including hands-on experience with machine learning algorithms and techniques.
For more career information including salaries, visit:
OpenCourser.com/course/mnz2ti/introduction
Reading list
We've selected 11 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
Introduction to Machine Learning: Language Processing.
Provides a comprehensive overview of natural language processing, covering topics such as tokenization, stemming, lemmatization, parsing, and machine learning. It valuable resource for anyone who wants to learn more about NLP.
Classic textbook on speech and language processing. It covers a wide range of topics, from the basics of phonetics and phonology to advanced topics such as machine translation and speech recognition.
Provides a comprehensive overview of machine learning for NLP. It covers a wide range of topics, from the basics of supervised and unsupervised learning to advanced topics such as deep learning and neural networks.
Provides a comprehensive overview of deep learning for NLP. It covers a wide range of topics, from the basics of deep learning to advanced topics such as attention mechanisms and transformers.
Provides a comprehensive overview of the Natural Language Toolkit (NLTK), a popular open-source library for NLP. It covers a wide range of topics, from the basics of NLTK to advanced topics such as machine learning and deep learning.
Provides a comprehensive overview of information theory, inference, and learning algorithms. It covers a wide range of topics, from the basics of information theory to advanced topics such as Bayesian inference and machine learning.
Provides a comprehensive overview of statistical natural language processing. It covers a wide range of topics, from the basics of probability theory to advanced topics such as machine learning and deep learning.
Save
Provides a comprehensive overview of machine learning for text. It covers a wide range of topics, from the basics of supervised and unsupervised learning to advanced topics such as deep learning and neural networks.
Save
Provides a comprehensive overview of computational linguistics. It covers a wide range of topics, from the basics of computational linguistics to advanced topics such as machine learning and deep learning.
Provides a comprehensive overview of natural language processing. It covers a wide range of topics, from the basics of natural language processing to advanced topics such as machine learning and deep learning.
Provides a comprehensive overview of natural language processing. It covers a wide range of topics, from the basics of natural language processing to advanced topics such as machine learning and deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/mnz2ti/introduction
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Time
4 hours
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
This course belongs to a
collection
called
Hands-on Machine Learning with Google Cloud Labs. It's comprised of three courses:
- Intermediate Machine Learning: TensorFlow on Google Cloud
- Advanced Machine Learning: Machine Learning Infrastructure
- Introduction to Machine Learning: Language Processing (viewing)
Good to know
The course explores natural language processing, a critical topic in the tech industry, using Google Cloud Platform's APIs
The hands-on practice through labs lets learners apply their knowledge immediately
Google Cloud Training, the course provider, is a reputable name in the industry
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