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

4.8
stars
49,627 ratings

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.

ED

Aug 22, 2020

Excellent start for digging into topics that are not taught nowhere else. The author books 'Machine Learning Yearning' is a great next read that goes deeper in some of the aspects, really recommended.

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2601 - 2625 of 5,688 Reviews for Structuring Machine Learning Projects

By Chandu W

Nov 3, 2019

Very systematic and thought-provoking approach. Thank you!

By Jonathan F

Aug 9, 2019

Helpful overview of how to conduct a deep learning project

By Nenad S

Jun 8, 2019

Builds great intuition for building and improving systems.

By Chris W

May 11, 2019

Great practical advice for applied deep learning projects!

By Manawaduge D

Feb 9, 2019

Excellent complement to the courses 1 & 2. Very practical.

By Ricardo A

Jan 8, 2019

Great Practical Knowledge and Tools to use in ML projects!

By Andreas G

Nov 4, 2018

A course that every machine learning engineer should take.

By Fred N

Oct 28, 2018

Very useful insights and guidelines from real-world cases!

By hong c

Aug 27, 2018

very good,thanks for all teachers.thanks for all your work

By Benigno C

Jul 21, 2018

Very interesting concepts to keep in mind in all projects.

By Hao D

Apr 25, 2018

Nice course, and extend my experience in DL. Thanks a lot.

By Hardik S

Apr 17, 2018

Very good course and very crisp and upto mark explanation.

By Ash H

Mar 13, 2018

Very Helpful as it will allow me to structure a ML project

By Yash N

Mar 13, 2018

Loved the course! Thanks for curating the amazing content!

By Adarshdeep S

Mar 3, 2018

One of the most interesting courses of the specialisation.

By Oleksandr P

Feb 11, 2018

Now I know much more about improving ML model performance!

By Sujit T

Jan 6, 2018

Quite valuable information on how to approach ML problems!

By Aniket P

Dec 31, 2017

Very good insights on how to go ahead in stuck up projects

By Jian Z

Nov 25, 2017

Very insightful class with clear and intuitive explanation

By Miguel M

Nov 16, 2017

You won't find the materials of this course anywhere else.

By Stoyan A

Nov 1, 2017

Gives great intuition on making progress on an AI project.

By Vin B

Oct 25, 2017

Thanks again for this amazing course Prof Andrew and staff

By Zhiyuan Y

Oct 9, 2017

这门课从很多角度为我们介绍了实际情况中训练机器学习模型可能遇到的问题和解决方法,是一个很有实战意义的课程,收益颇丰!

By Arnav C

Sep 20, 2017

One of the best courses I have ever taken. very practical.

By Adail M R

Sep 19, 2017

Very helpful tips on how to start and optimize ML projects