This course is an introduction to sequence models and their applications, including an overview of sequence model architectures and how to handle inputs of variable length.
This course is part of the Advanced Machine Learning on Google Cloud Specialization
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Course 4 of 5 in the
Advanced Level
Approx. 15 hours to complete
English
Subtitles: English
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 4 of 5 in the
Advanced Level
Approx. 15 hours to complete
English
Subtitles: English
Offered by
Google Cloud
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Syllabus - What you will learn from this course
4 hours to complete
Working with Sequences
In this module, you’ll learn what a sequence is, see how you can prepare sequence data for modeling, and be introduced to some classical approaches to sequence modeling and practice applying them.
4 hours to complete
14 videos (Total 41 min), 1 reading, 4 quizzes
15 minutes to complete
Recurrent Neural Networks
In this module, we introduce recurrent neural nets, explain how they address the variable-length sequence problem, explain how our traditional optimization procedure applies to RNNs, and review the limits of what RNNs can and can’t represent.
15 minutes to complete
4 videos (Total 15 min)
4 hours to complete
Dealing with Longer Sequences
In this module we dive deeper into RNNs. We’ll talk about LSTMs, Deep RNNs, working with real world data, and more.
4 hours to complete
14 videos (Total 62 min)
2 hours to complete
Text Classification
In this module we look at different ways of working with text and how to create your own text classification models.
2 hours to complete
8 videos (Total 35 min)
1 hour to complete
Reusable Embeddings
Labeled data for our classification models is expensive and precious. Here we will address how we can reuse pre-trained embeddings to make our models with TensorFlow Hub.
1 hour to complete
6 videos (Total 28 min)
3 hours to complete
Encoder-Decoder Models
In this module, we focus on a sequence-to-sequence model called the encoder-decoder network to solve tasks, such as Machine Translation, Text Summarization and Question Answering.
3 hours to complete
10 videos (Total 84 min)
14 minutes to complete
Summary
In this final module, we review what you have learned so far about sequence modeling for time-series and natural language data.
14 minutes to complete
1 video (Total 4 min), 1 reading
Reviews
- 5 stars63.50%
- 4 stars22.36%
- 3 stars8.22%
- 2 stars2.95%
- 1 star2.95%
TOP REVIEWS FROM SEQUENCE MODELS FOR TIME SERIES AND NATURAL LANGUAGE PROCESSING
by NDFeb 28, 2020
I have only one remark, please improve lesson regarding NLP by includin model build etc. Otherwise course is excellent and was helpful for me
by DDJan 20, 2020
Felt somewhat advanced learning, which made tough to complete intime
by ASAug 16, 2019
I like it because it is very relevant to my work. The dialogflow part is a bit weak. I am not sure if it is the product or the course.
by AJJun 7, 2021
The lack of synchronization between the videos and labs is extreme in this course, but the lectures are excellent and the subject area covered is very broad.
About the Advanced Machine Learning on Google Cloud Specialization
This 5-course specialization focuses on advanced machine learning topics using Google Cloud Platform where you will get hands-on experience optimizing, deploying, and scaling production ML models of various types in hands-on labs. This specialization picks up where “Machine Learning on GCP” left off and teaches you how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text. It ends with a course on building recommendation systems. Topics introduced in earlier courses are referenced in later courses, so it is recommended that you take the courses in exactly this order.
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