This course synthesizes everything your have learned in the applied machine learning specialization. You will now walk through a complete machine learning project to prepare a machine learning maintenance roadmap. You will understand and analyze how to deal with changing data. You will also be able to identify and interpret potential unintended effects in your project. You will understand and define procedures to operationalize and maintain your applied machine learning model. By the end of this course you will have all the tools and understanding you need to confidently roll out a machine learning project and prepare to optimize it in your business context.
Optimizing Machine Learning Performance
Optimizing Machine Learning Performance
This course is part of Machine Learning: Algorithms in the Real World Specialization
Instructor: Anna Koop
6,911 already enrolled
Included with 
Gain insight into a topic and learn the fundamentals.
4.4
(48 reviews)
11 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
26 assignments
Taught in English
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This course is part of the Machine Learning: Algorithms in the Real World Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
This week we'll present tools for understanding the overall strategy your business needs in order to see the best returns on ML investment. From understanding the current status to navigating ownership and setting up a team, this week is about understanding applied machine learning in a successful business context.
What's included
8 videos1 reading6 assignments1 peer review1 discussion prompt
This week we'll talk about the broader context of machine learning: how as developers we have responsibilities regarding how our technology will be used. Using case studies and existing frameworks we'll give you the tools to figure out your own ethical approach to realize the best outcomes while deploying machine learning in the real world.
What's included
6 videos6 assignments1 discussion prompt
An important aspect of machine learning in the real world is considering how your machine learning models are integrated with existing systems, and what effect they have on your operations. This week we'll review things you should consider as you turn QuAMs and machine learning models into operational tools.
What's included
8 videos7 assignments
Work doesn't end just because your model is deployed! In our final week we'll go over all the things you need to consider in the context of an actual working system.
What's included
9 videos7 assignments1 peer review
Instructor
Instructor ratings
4.5 (11 ratings)Offered by
Recommended if you're interested in Machine Learning
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4.4
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