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Structuring Machine Learning Projects (Coursera)

You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team’s work, this course will show you how. Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two “flight simulators” that let you practice decision-making as a machine learning project leader. This provides “industry experience” that you might otherwise get only after years of ML work experience.

After 2 weeks, you will:

– Understand how to diagnose errors in a machine learning system, and

– Be able to prioritize the most promising directions for reducing error

– Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance

– Know how to apply end-to-end learning, transfer learning, and multi-task learning

I’ve seen teams waste months or years through not understanding the principles taught in this course. I hope this two week course will save you months of time.

This is a standalone course, and you can take this so long as you have basic machine learning knowledge.

Who is this class for: This course is aimed at individuals with basic knowledge of machine learning, who want to know how to set technical direction and set prioritization for their work or their team’s work.

Course 3 of 5 in the Deep Learning Specialization.



ML Strategy (1)

Graded: Bird recognition in the city of Peacetopia (case study)


ML Strategy (2)

Graded: Autonomous driving (case study)