Chevron Left
Structuring Machine Learning Projects に戻る

Structuring Machine Learning Projects,

(26,959 件の評価)


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....


by AM

Nov 23, 2017

I learned so many things in this module. I learned that how to do error analysys and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.

by WG

Mar 19, 2019

Though it might not seem imminently useful, the course notes I've referred back to the most come from this class. This course is could be summarized as a machine learning master giving useful advice.



by Ayon Banerjee

May 19, 2019

Very good indeed. The methods shared are rarely found in books.

by John Schneider

May 19, 2019

I like the "flight simulator" quizzes a lot and other courses might benefit from a similar assessment (in addition to regular quizzes and programming exercises), but I do think this course would benefit from some programming exercises too. Thanks!

by Sanika Awasthi

May 19, 2019

loved this course...

by Rashmi Nagpal

May 19, 2019

Thanks a real bunch, Coursera for providing financial aid and bringing up this course, truly loved each and every section, coupled with quiz section at the end, is so much helpful and of course, very thoroughly made! Thanks to all the hardworking instructors and teaching assistance, and of course, coursera team for making this course so effectively! :)

by Artyom Kravchenko

May 19, 2019

I understood such concepts as: evaluation metric, percentage of distributions, estimating train and dev set errors,

training a basic model first,


softmax activation,

carrying out error analysis

on images that the algorithm got wrong,

algorithm will be able to use mislabeled example,

dev and test set should have the closest possible distribution to “real”-data, and so on.

by Mohamed Saber Rizk Ibrahim

May 18, 2019

Very useful content that is not taught in universities. Thank you!

by Muzammil

May 18, 2019

I believe Andrew Ng shared some key insights into building successful machine learning projects. I really enjoyed the course and believe the shared information to be invalueable for my further research.

by Zebin Chen

May 18, 2019

In the course, I learned how to divide train set, dev set, and test set, and how to solve the problem of different distributions of train set and test set. Impressive is the transfer learning. Transfer learning is a very effective way to help me provide a completely different approach to solving new problems.

by Catherine Chaput

May 18, 2019

great class. I am a big fan of the full package. I am at the 3rd module and I learnt so much. The material is clear and well structured.

by Rakshit Joshi

May 18, 2019

The flight simulators are really interesting and generate interest in the field. They also give a glimpse of structuring problems faced while developing an ML project.