In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader.
Structuring Machine Learning Projects
This course is part of Deep Learning Specialization
Instructors: Andrew Ng
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There are 2 modules in this course
Streamline and optimize your ML production workflow by implementing strategic guidelines for goal-setting and applying human-level performance to help define key priorities.
What's included
13 videos3 readings1 assignment
Develop time-saving error analysis procedures to evaluate the most worthwhile options to pursue and gain intuition for how to split your data and when to use multi-task, transfer, and end-to-end deep learning.
What's included
11 videos2 readings1 assignment
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Recommended if you're interested in Data Analysis
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Reviewed on Aug 22, 2020
Excellent start for digging into topics that are not taught nowhere else. The author books 'Machine Learning Yearning' is a great next read that goes deeper in some of the aspects, really recommended.
Reviewed on Jun 20, 2019
Really a good course with mostly the theoretical knowledge on some aspects to reuse your model as well as some error analysis. Thoroughly taught with lots of real-life examples, thanks to Andrew Ng.
Reviewed on Feb 24, 2020
Generally, the course is great. This is a short course and could be combined with other courses in this series. Also, some knowledge such as data splitting has been introduced in the courses before.
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