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

Structuring Machine Learning Projects
This course is part of Deep Learning Specialization



Instructors: Andrew Ng
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Skills you'll gain
- Machine Learning
- Model Evaluation
- Deep Learning
- AI Product Strategy
- Transfer Learning
- Applied Machine Learning
- Performance Tuning
- Data-Driven Decision-Making
- MLOps (Machine Learning Operations)
- Debugging
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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 Jul 25, 2018
Very important and valuable intuitions about DNN training/optimization. It's full of really practical information while implementing my own models.DNN을 실제 적용할때 반드시 이해하고 적용해야 할 실질적 내용들로 구성된 멋진 코스 입니다!
Reviewed on Jul 7, 2020
I think this is the best way of understanding the models we build and train. Now I can understand where are the errors are coming from and how to focus and choose an error rate problem to solve.
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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