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 Mar 18, 2019
Though it might not seem imminently useful, the course notes I've referred back to the most come from this class. This course is could be summarized as a machine learning master giving useful advice.
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 Sep 21, 2018
This is a must course in the entire specialization. It covers the step by step procedure to approach and solve a problem. The case studies provided are real world problems which are so much helpful.
Recommended if you're interested in Data Science
DeepLearning.AI
Amazon Web Services
Duke University


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