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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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.
Reviewed on Aug 19, 2021
Very helpful tips for navigating possible problems that would likely occur while building/training a model. The "pilot-training" exercieses, that mimick real-life problems / projects, are excellent !
Reviewed on Jun 15, 2020
Useful to know what are the steps that should be taken after obtaining results. Tho there isn't much information regarding making machine learning projects here (ie. there isn't any hands on project)
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