Online Course
Structuring Machine Learning Projects
deeplearning.ai and Stanford University via Coursera
Overview
Class Central Tips
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.
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.
Taught by
Andrew Ng
Reviews
5.0 rating, based on 3 reviews
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Rainer 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... -
Silveira 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. -
Raivis J.
Raivis completed this course, spending 4 hours a week on it and found the course difficulty to be very easy.
Interesting course, if you could call it that (more like a series of experience sharing videos) with some applicable ideas. Could benefit from coding exercises that actually model the situations discussed.
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