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
Amazon Web Services
DeepLearning.AI
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Reviewed on Mar 30, 2020
It is very nice to have a very experienced deep learning practitioner showing you the "magic" of making DNN works. That is usually passed from Professor to graduate student, but is available here now.
Reviewed on Nov 10, 2017
Awesome course as always. The course teaches real world practical aspects of how to get started and navigate in the real world projects. The guidelines are actual learnings from years of experience.
Reviewed on May 6, 2020
This course gave some very useful tips on how to start with a Machine learning project when I was struggling to do so. It also gave useful information about error analysis and data set distribution.
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