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

4.8
stars
49,631 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

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.

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.

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4976 - 5000 of 5,688 Reviews for Structuring Machine Learning Projects

By Rohini H

Jun 9, 2020

still with some more example & more simplest way to solve them .with simple basic examples

By Martin B

Mar 17, 2018

Again, excellent, but proof reading of the test and proof-viewing of the videos is needed.

By Sudeep K

Apr 7, 2020

It could be more detailed. More Code intense! However, the course was really informative.

By Abhishek P

Sep 24, 2019

Initially a bit hard to understand but repeating the session helped to grasp the concepts

By Pedro L A V

Feb 26, 2018

Good course, but there are too many small topics in each week and no hands-on assignment.

By Blake C

Sep 20, 2017

Not quite sure, but there are some problems in exam. Hopely fix them as soon as possible.

By Bharat M

Jul 24, 2020

A good theoretical course to help remember the nuances of how to structure a ML project.

By Vikas C

Jun 20, 2019

It was a nice course, it can better if some demo codes are used as an example separately

By Shuo N

Jan 3, 2018

Pro: useful practical suggestions;

Con: language used in quiz should be further polished.

By Ridvan S

Oct 15, 2017

"Chillout course", but "test-by-real-cases" is very exiting and very fine idea. Strong 4

By riad s

Sep 21, 2017

I wish there was some practice assignments related to the concepts learnt in this course

By Vinay K

Feb 5, 2020

Info about the approach in applying DL/ML concepts to various scenarios were explained.

By Vishal G

Dec 19, 2019

Interesting course, but all this could have been summarised in the first course itself!

By Hao W

Jun 16, 2019

This course is not so well organized as the previous two. Everything else is very good!

By donato t

Jun 15, 2019

Very valuable insight in real world usage of DL and guidance in approaching DL projects

By Wilson L

Sep 16, 2017

It would be even better if there are more quiz and assignments made in ipython notebook

By Eugene K

Jul 16, 2023

Not a necessary course in my opinion. His other courses in this series is much better.

By Ілля С

Jun 8, 2023

The theory from videos is awesome but the tests have less specific problem description

By Takshshila R

Jul 1, 2020

This gives subtle but important knowledge

about training with different distributions.

By Rohit G

Mar 26, 2020

The pacing was a little off, and perhaps some code would help. Otherwise, pretty good.

By Kirill S

Feb 29, 2020

Useful and short course, but with a lot of information repetition. Could be optimized.

By Ashraf A

Nov 16, 2019

Very helpful in understanding the way the machine learning projects should be managed.

By Dominik F

Jul 25, 2019

Very useful, but some questions were misleading or were not discussed until next week.

By Cristian D T

Oct 30, 2017

The material is good, but too short. It could have been compressed in another course.

By manpreet s

Oct 8, 2018

Good module to learn about how to structure the ML project and getting insights of it