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Learner Reviews & Feedback for Structuring Machine Learning Projects by DeepLearning.AI

4.8
stars
46,214 ratings
5,282 reviews

About the Course

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader. By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning. This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

Top reviews

MG
Mar 30, 2020

It is very nice to have a very experienced deep learning practitioner showing you the "magic" of making DNN works. That is usually passed from Professor to graduate student, but is available here now.

AM
Nov 22, 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.

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201 - 225 of 5,228 Reviews for Structuring Machine Learning Projects

By Rahuldeb D

Jul 29, 2018

This course provides us an overview of the errors we have to encounter while solving a machine learning problem and shows us a clear direction of overcoming those. Though the contents are not mathematical but these information will help us to deal with machine learning projects in efficient way. I really liked this course.

By Wei-Chuang C

Aug 19, 2017

The course is very practical and also leads you to learn the real challenge you will encounter while working on machine learning project. While it's easy to follow as the previous courses, you need to think more strategically. I would recommend bringing an idea or a project you are planning and apply what you learned here.

By ANIKET A G

Jul 17, 2020

The course really streamlines and puts forth a structured approach to go for delivering a machine learning solution to a problem. It helps to complete my project in 2-3 months instead of a year that sometimes some of my colleagues take. They need to look at this course. Also the interview with Ruslan was very informative.

By Azamat K

Aug 17, 2019

Really liked this course, especially the case studies, where the task is clear and possible scenarios are explained. Have to response in the most promising way using the knowledge obtained during the previous 2 courses. Really appreciate this experience. Only wish is to have more case studies in the other courses as well.

By Bradley W

Dec 14, 2017

Great course. The pragmatic insights were invaluable. I think addressing problems such as missing input data and data preparation would help. I also think a programming assignment that explores these ideas would help. You could take the sign language number exercise from week 2 and explore some of the ideas this week.

By Gopinath

Jan 16, 2020

I can confidently say that this course has content which is only unique to this course. To my knowledge no other course has topics like Avoidable bias, Bayes optimal error, Error analysis and emphasis on train, dev & test set data distribution mismatch. This course is definitely a must for any Deep learning practitioner.

By Deepak V

Jan 6, 2018

Looking at the title of this course I predicted that it will be regarding to teach me how to organise the source code files of ML project and more specifically how to build a ML project and components of deep learning project but it was all about DEBUGGING ml project so for me this was in off beat course from its title.

By Tony H

Aug 30, 2017

Extremely useful, practical techniques for deep learning projects. I feel much more able to construct my own neural networks, diagnose and solve issues with them after following this course. Professor Ng is a gifted teacher. His style is careful, methodical and never less than very well prepared and deeply enlightening.

By Ayan G

Apr 20, 2020

Its really nice to get the valuable insight of managing an AI project, this course not only thought us about deep learning, but also how to manage them efficient and take smart decision. I like the concept of Transfer learning as it can same a lot of efforts and time to build an system for complex. Thank you very much.

By Kwan T

Oct 1, 2017

I am very lucky to be able to learn from Andrew the DOs and DON'Ts of how to develop a successful practical deep neural network for real applications. It would take a machine learning developer many years of working experience to acquire any one of the topics that Andrew articulated in this course. Thank you so much!!!

By Konstantinos K

Dec 31, 2020

The course is great. It tackles a lot of problems regarding strategic decision making and at the same time important concepts such as human-level error, avoidable bias, transfer learning, end-to-end deep learning and others are being taught. The questions/exercises really test the core concepts that are being taught!

By Mark Z

Jun 11, 2019

I've decided to take this course after seeing its feedback from other people and the comment which got me was the following: "This course is could be summarized as a machine learning master giving useful advice". I think it perfectly describes the course's content. This course is definitely worth investing time into.

By Dunitt M

Feb 10, 2019

Excelente curso, muy recomendado para quienes tienen una idea de Deep Learning pero con frecuencia se encuentran en situación que no saben cómo afrontar o cuál camino intentar primero. El conjunto de habilidades impartidas aquí no te harán un mejor programador, pero te ahorraran muchas horas de esfuerzo innecesario.

By Gaurav K

Sep 7, 2017

Amazing tips shared for structuring machine learning projects, which were ignored in most of the other ML books. Building a model is one thing, but tuning it to make it work better in the real world is more important which this course focuses.

Thanks Prof. Andrew Ng for the consistent support of spreading knowledge!

By Muhammad A

Aug 13, 2020

Although, this course of specialization was simple with no assignment still the case studies were quite informative. I would suggest to include a case study related to google machine learning for navigation and voice recognition. We youth can easily relate to this case study. Overall this course was a full package.

By Yuezhe L

Nov 19, 2018

This is a very helpful class. I have been working on machine learning projects for years. This course provides methods to systematically trouble shoot problems in a machine learning project. Despite all the samples are using neural networks, the methodology can be applied to improve other machine learning projects.

By Danial A

Jan 3, 2021

Andrew NG has a peculiar style when it comes to teaching data science. I have never seen someone explaining the terms this effectively. The material in this course is a direct revelation of his years of experience and entails the unique feature of being the lessons learned from the experience. Really great course.

By Bernard O

Oct 24, 2018

Excellent course on managing through the thick of bias/variance tradeoffs. Been doing a lot just based on things I have picked up through experience, but this course puts a the quantitative rigor and discipline behind the art. The sections on transfer and end to end deep learning were eye opening sections for me.

By Gema P

Feb 25, 2018

This course is strategically very important so congrats on making it

I would add a programming assignment including transfer learning or multi-task learning implementation due to the multiple cases of use that are today in the industry.

Thanks again for making this Wonderfull material available to the community ^^

By BAZIL F

Dec 29, 2019

Very useful course for understanding nuances of AI and different useful techniques in strategizing the approaches. Extremely useful in architecting, designing and delivery of the complex solutions involving AI (even as a sub-component). Prof. Andrew Ng is always a pleasure and honor to learn from. Thank You Sir!

By Harvey Q

Sep 4, 2017

Really inspiring course, and UNIQUE. No other class, I think, provide these suggestions on the big question "what's next?" in ML projects. The videos are a bit weirdly sequenced. But they provide very systematic ways of project starting, data splitting, model evaluating, problem finding and tuning. Great course!

By Pedro B M

Feb 28, 2019

This a course on key practices one should have when developing a ML project. Once again Andrew Ng is very pedagogical, teaching sometimes complex concepts in a easy to understand and practical way. I particularly liked the case studies, where the learned concepts had to be put into practice for decision taking.

By Niyas M

Oct 29, 2017

What a great session! Full of practical advice and strategies to help you iterate fast. Prof. Andrew draws on his years of hands-on experience at top companies to put together the best practices for structuring your machine learning projects. This has been the most valuable course in this series for me so far!

By Nikhil K

Jul 9, 2020

super helpful! something that's really valuable in-terms of optimally organizing the thought process i should use to approach an issue i want to solve with Deep Learning.

also, the Quizzes in this course (in-particular) were very important for me because it helped ingrain the tenets of this course in my mind.

By Zebin C

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.