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Learner Reviews & Feedback for Structuring Machine Learning Projects by deeplearning.ai

4.8
32,042 ratings
3,364 reviews

About the Course

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....

Top reviews

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.

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.

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1 - 25 of 3,328 Reviews for Structuring Machine Learning Projects

By Liu H

Jun 11, 2019

This course would be immensely helpful for those who have not started on their first machine learning project. However, the insights shared are quite commonsensical and intuitive for those who have already had some minimal experience in machine learning. This course also does not feel as substantial as the other courses in the specialization, though the tips provided are definitely valuable.

By ABHISHEK K

May 31, 2019

I recommend this course. This will be a bit of theoretical which is good. It will talk about real world scenarios over the errors which is what we deal in day-to-day life and how to deal with it.

By Nazarii N

May 25, 2019

more practice!

By Walter G

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 ANKIT M

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 SAI V K

Feb 20, 2019

This is the knowledge in which we will get from lots of experience only, but the andrew has shared in this course which might help us in future by saving a lot of time through this course experience

By Damian C

Mar 08, 2018

Going beyond the technical details, this part of the course goes into the high level view on how to direct your efforts in a ML project. Really enjoyable and useful. Thanks for making this available!

By THAMMANA S R

Sep 22, 2018

This is a must course in the entire specialization. It covers the step by step procedure to approach and solve a problem. The case studies provided are real world problems which are so much helpful.

By Ziping Z

Apr 07, 2018

A lot of concrete examples, including those in the lectures and in the tests. Gained some thoughts on how to manage a ML project. Thanks Andrew and deeplearning.ai for providing such a great course.

By Matei I

Feb 16, 2019

I'm glad I spent some time on the "Flight simulator" assignments in this course. It's the first time in the specialization when I actually found the quiz questions challenging, and that's a welcome change. However, I didn't learn too much from the lectures. They were too repetitive, either repeating themselves or the material from the previous course. One or two videos could also do with better editing work: I could hear Andrew making a soundcheck, and there's a 30sec segment that's played twice in a row. Overall, it's probably worth doing this course, given that it requires very little time, and the assignments are useful.

By Vasu M

Dec 11, 2018

Informative but extremely theoretical.

By shijiatongxue

Dec 12, 2018

Very nice and I have learned a lot from this course.Thank you Andrew Ng!

By Faiz R

Dec 11, 2018

Very well structured course with lots of practical wisdom regarding structuring projects and managing implementation with respect to real world applications

By Khoo T S

Dec 12, 2018

Great, it helps to build a good deep learning models quickly.

By Mahmut K

Dec 10, 2018

Useful course not only for deep learning but for other ML algorithms. Reviews issues that one needs to be careful about using statistical methods.

By Oleksiy S

Dec 12, 2018

Valuable insight to ML system design

By Laurence C

Nov 24, 2018

Fascinating strategic insights

By Jhon S

Nov 26, 2018

cool

By Jabberwoo

Nov 26, 2018

Great course!

By Javier O

Nov 25, 2018

Very important common practices in ML, especially the tips of error analysis and ways to solve very common problems.

By 李子龙

Nov 24, 2018

really good lesson,thank coursera and deeplearning.ai

By 谢宇敏

Nov 24, 2018

I like it

By Bhaskar D

Dec 13, 2018

Excellent course. Loved the case study format - good break from the other style of the rest of the courses.

By Manish N

Dec 15, 2018

very informative course

By Wadigzon D

Dec 14, 2018

excellent overview of best practices when organizing your ML project