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

4.8
stars
49,809 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

ED

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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.

MG

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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.

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2626 - 2650 of 5,709 Reviews for Structuring Machine Learning Projects

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

By Hector D M P

Sep 2, 2017

Mainly because it was new material that I haven't reviewed

By Balakumar R m

Jul 11, 2021

Pretty interesting ideas this course can grid one's loins

By Tanishka M

Apr 24, 2021

Beautifully captured the essence of ML Project teamwork !

By vijay b n

Aug 12, 2020

Best course and I really enjoyed a lot during the course.

By Puri V

May 8, 2020

Great! Andrew Ng is a great teacher. Thank you very much!

By Nibir H

May 8, 2020

Really easy to understand and worthy deep learning course

By Pawan S J

May 8, 2020

Awesome!! No words to explain the quality of the course!!

By Anshraj S

May 4, 2020

Really good content, I really admire Andrew Ng sir a lot.

By Siddam M S

May 2, 2020

Machine Learning strategies, development and training set

By C.K. T

Apr 15, 2020

Love the "flight simulator" exercise. Great way to learn.

By Zhi L

Mar 17, 2020

It's really exciting that AI get better and better today!

By Shashank C

Apr 11, 2019

The course was very useful for me, thank you Prof Andrew!

By John G

Feb 1, 2019

Excellent for technical aspects (wich is very important!)

By HOA N

Oct 7, 2018

absolutely, this is a very useful course for ML projects.

By 朱欣宇

Sep 13, 2018

It really gives me a lot of inspiration,thank you Andrew!

By liyimeng

Sep 1, 2018

Very useful principals in building a deeplearning network