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LifestyleFeature Engineering
What is machine learning, and what kinds of problems can it solve? What are the five phases of converting a candidate use case to be driven by machine learning, and why is it important that the phases not be skipped? Why are neural networks so popular now? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent and a thoughtful way of creating datasets? Learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models. and offer high-performance predictions. Convert raw data to feature
Created by: Google Cloud Training
Quality Score
Overall Score : 80 / 100
Course Description
Instructor Details
- 4.0 Rating
107 Reviews
Google Cloud Training
The Google Cloud Training team is responsible for developing, delivering and evaluating training that enables our enterprise customers and partners to use our products and solution offerings in an effective and impactful way. Google Cloud helps 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.
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Reviews
By Super-intelligent S o t C B on 12-Nov-19
This wasn't a bad course, but it is more geared towards showcasing GCP features (BigQuery, Dataflow, Apache Beam, etc.) rather than teaching feature engineering.
By A A on 8-Nov-19
the lectures are good, can be boring. The course would have been more interesting if it had thought-out assignments instead of demo-code to just run as labs
By Alex H on 21-Oct-19
Great instructor but (1) the coding challenges are buggy and don't really teach you anything and (2) a lot of the material in this course is tedious for someone with professional training in AI but no experience with GCP
By Shivam K on 14-Oct-19
Labs were good and required effort to complete!
By ni_tempe on 7-Oct-19
this is useless...google is advertising their product and making us pay for it. They should learn dr Andrew Ng and create courses which teach us without using a specific platform.
By Ting-Shuo Y on 23-Sep-19
Feature engineering is important but less discussed compared to general ML or DNN. Feature cross is a new concept and yet very useful for dealing with large datasets.
By Thibault D on 14-Sep-19
The gap between the lecture and the coding is too big. The coding sessions need to be more interactive to be useful.
By Ayush T on 5-Sep-19
This course and the next course of the specialization is the most important course of the specialization. The reason is that other course except the first course deals with the working of APIs which might change in the near future but the insight that this couse provide on some of the topics is really really important, which I've not seen much discussed. This course is definitely a must-do.
By Keith H on 1-Sep-19
Love the course but this specialization is fairly complex and is new type of thinking as such take a bit of understanding.
By Ahmad T on 27-Aug-19
Great
By Anupam P on 26-Aug-19
Comprehensive yet precise and clear.
By Sri B on 15-Aug-19
Being a beginner I felt very difficult to understand concepts taught in this course.But the content was awesome
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- Video Duration:
11h - Provider: Coursera
- University:
Google Cloud - Language:
English - Certificate:
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