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Coursera: Structuring Machine Learning Projects

 with  Andrew Ng
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. This is the third course in the Deep Learning Specialization.

Syllabus

ML Strategy (1)


ML Strategy (2)


2 Student
reviews
Cost Free Online Course (Audit)
Pace Upcoming
Provider Coursera
Language English
Certificates Paid Certificate Available
Calendar 2 weeks long
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2 reviews for Coursera's Structuring Machine Learning Projects

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3 weeks ago
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Rainer Dreyer completed this course, spending 4 hours a week on it and found the course difficulty to be medium.
The course covered a range of practical issues, such as creating a single performance metric to quickly compare algorithms, how to compare the algorithm with human error to estimate Bayes (ideal) error rates and how to manually inspect and analyze errors to decide on further improvements to the algorithm. This course Read More
The course covered a range of practical issues, such as creating a single performance metric to quickly compare algorithms, how to compare the algorithm with human error to estimate Bayes (ideal) error rates and how to manually inspect and analyze errors to decide on further improvements to the algorithm.

This course was the first one in the series to have no programming assignments, opting instead for a quiz at the end of each week presented as a 45-minute case study or "flight simulator". The idea behind these "flight simulators" was to present the student with more complex, long term issues a practitioner would encounter over the course of a real-world machine learning project.

The previous course introduced a first small example using Tensorflow, so not getting to implement some of the new concepts in this course (like transfer learning and multi-task learning) was surprising, but these might be covered again in more detail in a future course.
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3 weeks ago
Silveira Homero completed this course, spending 9 hours a week on it and found the course difficulty to be medium.
This course follows Neural Networks and Deep Learning and Improving Deep Neural Networks. the practical tips are sure valuable to the practitioner.
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