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Google Cloud
In this course, we’ll learn how to make predictions on sequences of data.
In this course, we’ll learn how to make predictions on sequences of data. We’ll cover common business use cases like- 1.time-series prediction and how to deal with more recent data points getting more relevance 2.translating entire sentences (aka sequences of words) into other languages You will get hands-on practice building and optimizing your own text classification and sequence models on a variety of public datasets in the labs we’ll work on together.
Register for this course and see more details by visiting:
OpenCourser.com/course/qxlaqw/sequence
What's inside
Syllabus
Working with Sequences
Recurrent Neural Networks
Dealing with Longer Sequences
Text Classification
Read more
Syllabus
Working with Sequences
Recurrent Neural Networks
Dealing with Longer Sequences
Text Classification
Read more
Reusable Embeddings
Encoder-Decoder Models
Summary
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops machine-learning modeling skills for time-series prediction, text classification, and language translation
Suitable for learners with some machine-learning experience who want to apply their skills to sequence prediction
Taught by Google Cloud instructors, leveraging their expertise in cloud-based machine learning
Provides live coding labs, offering hands-on practice for building and optimizing models, making it interactive and engaging
May require prior knowledge of machine-learning fundamentals
Designed by Google Cloud, a leader in cloud computing and machine learning
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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 Sequence Models for Time Series and Natural Language Processing on Google Cloud with these
activities:
Review 'Data Mining Techniques'
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Review a fundamental book on data mining techniques to reinforce understanding of key concepts and algorithms.
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Data Mining Techniques: For Marketing, Sales,...
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Read the book thoroughly and take notes on important concepts.
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Summarize the main ideas and techniques presented in the book.
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Identify any areas where you need further clarification or practice.
Review Prerequisites
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Review the required foundational skills needed for this course before getting started.
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Review basic data processing and cleansing techniques.
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Practice using basic data mining algorithms on practice datasets.
Explore Data Science Tutorials
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Explore data science tutorials to expand understanding of data mining and analysis techniques.
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Data Mining
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Find a series of tutorials on data mining or data analysis.
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Work through the tutorials and take notes on key concepts and techniques.
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Apply these techniques to practice datasets to gain hands-on experience.
Four other activities
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Practice Data Mining Drills
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Practice data mining techniques through drills and exercises to improve skills and understanding.
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Data Mining
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Find a set of data mining drills or exercises.
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Work through the drills, applying data mining techniques to solve specific problems.
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Review solutions and compare your results to identify areas for improvement.
Compile Data Science Resources
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Gather and organize resources on data science to support learning and reference in the future.
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Create a list of relevant websites, articles, tutorials, and tools.
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Categorize and organize the resources in a useful manner.
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Share the compilation with others to contribute to the learning community.
Mentor a Junior Data Scientist
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Share your knowledge and skills by mentoring a junior data scientist to enhance your understanding and reinforce learning.
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Identify a junior data scientist who can benefit from your guidance.
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Establish clear goals and expectations for the mentorship.
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Provide regular support, guidance, and feedback to the mentee.
Contribute to Open-Source Data Science Projects
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Contribute to open-source data science projects to gain practical experience and support the community.
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Identify open-source data science projects that align with your interests and skills.
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Join the community and learn about the project's goals and guidelines.
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Identify a specific task or issue to work on and submit a pull request.
Review 'Data Mining Techniques'
Show steps
Review a fundamental book on data mining techniques to reinforce understanding of key concepts and algorithms.
View
Data Mining Techniques: For Marketing, Sales,...
on Amazon
Show steps
Show steps
Show steps
- Read the book thoroughly and take notes on important concepts.
- Summarize the main ideas and techniques presented in the book.
- Identify any areas where you need further clarification or practice.
Review Prerequisites
Show steps
Review the required foundational skills needed for this course before getting started.
Show steps
Show steps
Show steps
- Review basic data processing and cleansing techniques.
- Practice using basic data mining algorithms on practice datasets.
Explore Data Science Tutorials
Show steps
Explore data science tutorials to expand understanding of data mining and analysis techniques.
Browse courses on
Data Mining
Show steps
Show steps
Show steps
- Find a series of tutorials on data mining or data analysis.
- Work through the tutorials and take notes on key concepts and techniques.
- Apply these techniques to practice datasets to gain hands-on experience.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Data Mining Drills
Show steps
Practice data mining techniques through drills and exercises to improve skills and understanding.
Browse courses on
Data Mining
Show steps
Show steps
Show steps
- Find a set of data mining drills or exercises.
- Work through the drills, applying data mining techniques to solve specific problems.
- Review solutions and compare your results to identify areas for improvement.
Compile Data Science Resources
Show steps
Gather and organize resources on data science to support learning and reference in the future.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Create a list of relevant websites, articles, tutorials, and tools.
- Categorize and organize the resources in a useful manner.
- Share the compilation with others to contribute to the learning community.
Mentor a Junior Data Scientist
Show steps
Share your knowledge and skills by mentoring a junior data scientist to enhance your understanding and reinforce learning.
Show steps
Show steps
Show steps
- Identify a junior data scientist who can benefit from your guidance.
- Establish clear goals and expectations for the mentorship.
- Provide regular support, guidance, and feedback to the mentee.
Contribute to Open-Source Data Science Projects
Show steps
Contribute to open-source data science projects to gain practical experience and support the community.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Identify open-source data science projects that align with your interests and skills.
- Join the community and learn about the project's goals and guidelines.
- Identify a specific task or issue to work on and submit a pull request.
Career center
Learners who complete Sequence Models for Time Series and Natural Language Processing on Google Cloud will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers create machine learning algorithms and models that can learn from historical data and predict future events. They may also develop and implement machine learning solutions for a variety of applications, such as natural language processing, computer vision, and time series analysis. This course may be useful in helping you build a foundation in machine learning and develop the skills needed to become a Machine Learning Engineer. The course covers topics such as recurrent neural networks, dealing with longer sequences, text classification, and encoder-decoder models, which are all relevant to this field.
Data Scientist
Data Scientist
Data Scientists collect, analyze, and interpret data to help businesses make informed decisions. They may also develop and implement machine learning and artificial intelligence solutions to solve business problems. This course may be useful in helping you build a foundation in data science and develop the skills needed to become a Data Scientist. The course covers topics such as recurrent neural networks, dealing with longer sequences, and encoder-decoder models, which are all relevant to this field.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They may also work on machine learning and artificial intelligence projects. This course may be useful in helping you build a foundation in software engineering and develop the skills needed to become a Software Engineer. The course covers topics such as recurrent neural networks, dealing with longer sequences, text classification, and encoder-decoder models, which are all relevant to this field.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data and make investment recommendations. They may also develop and implement machine learning and artificial intelligence solutions to solve problems in the financial industry. This course may be useful in helping you build a foundation in quantitative analysis and develop the skills needed to become a Quantitative Analyst. The course covers topics such as recurrent neural networks, dealing with longer sequences, and time series analysis, which are all relevant to this field.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers develop and implement machine learning models for processing and understanding human language. They may also work on natural language processing applications such as machine translation, chatbots, and text summarization. This course may be useful in helping you build a foundation in natural language processing and develop the skills needed to become a Natural Language Processing Engineer. The course covers topics such as text classification, reusable embeddings, and encoder-decoder models, which are all relevant to this field.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and models. They may also work on theoretical and applied aspects of machine learning. This course may be useful in helping you build a foundation in machine learning research and develop the skills needed to become a Machine Learning Researcher. The course covers topics such as recurrent neural networks, dealing with longer sequences, and encoder-decoder models, which are all relevant to this field.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make informed decisions. They may also develop and implement data visualization and reporting solutions. This course may be useful in helping you build a foundation in data analysis and develop the skills needed to become a Data Analyst. The course covers topics such as recurrent neural networks, dealing with longer sequences, and time series analysis, which are all relevant to this field.
Business Analyst
Business Analyst
Business Analysts help businesses identify and solve problems by analyzing data and developing recommendations. They may also work on projects such as process improvement, organizational change, and strategic planning. This course may be useful in helping you build a foundation in business analysis and develop the skills needed to become a Business Analyst. The course covers topics such as recurrent neural networks, dealing with longer sequences, and time series analysis, which are all relevant to this field.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They may also work on product strategy, marketing, and sales. This course may be useful in helping you build a foundation in product management and develop the skills needed to become a Product Manager. The course covers topics such as recurrent neural networks, dealing with longer sequences, and text classification, which are all relevant to this field.
Technical Writer
Technical Writer
Technical Writers create and maintain technical documentation, such as user manuals, white papers, and training materials. They may also work on projects such as product documentation, knowledge base development, and content marketing. This course may be useful in helping you build a foundation in technical writing and develop the skills needed to become a Technical Writer. The course covers topics such as recurrent neural networks, dealing with longer sequences, and text classification, which are all relevant to this field.
User Experience Designer
User Experience Designer
User Experience Designers create and evaluate user interfaces for websites, software applications, and other products. They may also work on projects such as user research, prototyping, and usability testing. This course may be useful in helping you build a foundation in user experience design and develop the skills needed to become a User Experience Designer. The course covers topics such as recurrent neural networks, dealing with longer sequences, and text classification, which are all relevant to this field.
Information Architect
Information Architect
Information Architects design and organize websites, software applications, and other products to make them easy to use and find information. They may also work on projects such as content strategy, navigation design, and search engine optimization. This course may be useful in helping you build a foundation in information architecture and develop the skills needed to become an Information Architect. The course covers topics such as recurrent neural networks, dealing with longer sequences, and text classification, which are all relevant to this field.
Content Strategist
Content Strategist
Content Strategists develop and implement content strategies for websites, software applications, and other products. They may also work on projects such as content creation, content marketing, and social media management. This course may be useful in helping you build a foundation in content strategy and develop the skills needed to become a Content Strategist. The course covers topics such as recurrent neural networks, dealing with longer sequences, and text classification, which are all relevant to this field.
Digital Marketing Manager
Digital Marketing Manager
Digital Marketing Managers plan and implement digital marketing campaigns for websites, software applications, and other products. They may also work on projects such as search engine optimization, social media marketing, and email marketing. This course may be useful in helping you build a foundation in digital marketing and develop the skills needed to become a Digital Marketing Manager. The course covers topics such as recurrent neural networks, dealing with longer sequences, and text classification, which are all relevant to this field.
Salesforce Developer
Salesforce Developer
Salesforce Developers develop and implement Salesforce applications for businesses. They may also work on projects such as data integration, workflow automation, and custom object development. This course may be useful in helping you build a foundation in Salesforce development and develop the skills needed to become a Salesforce Developer. The course covers topics such as recurrent neural networks, dealing with longer sequences, and text classification, which are all relevant to this field.
For more career information including salaries, visit:
OpenCourser.com/course/qxlaqw/sequence
Reading list
We've selected ten 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
Sequence Models for Time Series and Natural Language Processing on Google Cloud.
Delves into the theory and practice of recurrent neural networks, providing a comprehensive overview of different architectures, training algorithms, and applications. It valuable resource for gaining a deeper understanding of the underlying concepts behind sequence models.
Save
Comprehensive reference on deep learning, covering a wide range of topics from basic concepts to advanced architectures and applications. It provides a detailed overview of the field and serves as a valuable resource for further exploration beyond the scope of the course.
Provides a comprehensive introduction to deep learning, covering the underlying principles, architectures, and applications. It useful reference for understanding the theoretical foundations of sequence models and natural language processing.
Provides a comprehensive overview of machine learning using Python. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning. The book also includes practical examples and exercises that can be applied to real-world problems.
Provides a comprehensive overview of natural language processing with transformers. It covers a wide range of topics, including transformer architectures, pre-trained models, and fine-tuning for specific NLP tasks. The book also includes practical examples and exercises that can be applied to real-world problems.
Focuses specifically on natural language processing using PyTorch, covering various techniques such as text classification, language modeling, and machine translation. It provides practical insights and code examples that can supplement the course's materials.
Provides a comprehensive overview of time series analysis and its applications. It covers a wide range of topics, including time series decomposition, forecasting, and statistical methods for time series analysis. The book also includes practical examples and exercises that can be applied to real-world problems.
Save
Provides a comprehensive treatment of time series analysis and forecasting techniques, including both classical and modern approaches. It covers topics such as time series decomposition, modeling, and evaluation, which are relevant to the course's focus on time-series prediction.
Provides a comprehensive introduction to deep learning using the R programming language. It covers the fundamentals of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks. The book also includes practical examples and exercises that can be applied to real-world problems.
Provides a quick introduction to TensorFlow 2.0, the open-source machine learning library used in the course. It covers the basics of TensorFlow, including its architecture, data structures, and operations, which is helpful for learners who are new to the platform.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/qxlaqw/sequence
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Time
271 hours
Level
Advanced
Via
Pluralsight
Instructor
Google Cloud
Language
English
This course belongs to a
collection
called
Advanced Machine Learning on Google Cloud. It's comprised of five courses:
- End-to-End Machine Learning with TensorFlow on Google Cloud
- Production Machine Learning Systems
- Image Understanding with TensorFlow on GCP
- Sequence Models for Time Series and Natural Language Processing on Google Cloud (viewing)
- Recommendation Systems with TensorFlow on GCP
Good to know
Develops machine-learning modeling skills for time-series prediction, text classification, and language translation
Suitable for learners with some machine-learning experience who want to apply their skills to sequence prediction
Taught by Google Cloud instructors, leveraging their expertise in cloud-based machine learning
Provides live coding labs, offering hands-on practice for building and optimizing models, making it interactive and engaging
May require prior knowledge of machine-learning fundamentals
Designed by Google Cloud, a leader in cloud computing and machine learning
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