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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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4551 - 4575 of 5,229 Reviews for Structuring Machine Learning Projects

By Kyle S

Oct 23, 2017

This is a great course. It will be an invaluable reference when tackling real-world problem.

By Yang Z

Oct 20, 2017

Some videos don't end properly and instead give you a black screen to stare at for a minute.

By Akhtar H

Feb 9, 2021

This is little bit tricky course. But main understanding comes when you solve case studies.

By Neel K

Oct 19, 2020

It was better to include some more case studies. This was a better real-time understanding.

By Michael L

Jun 10, 2020

Sometimes, the explanations/advices given were too lengthly and contained some repetitions.

By Amielle D

Jul 24, 2019

There were some typos throughout the course, but the core topics were still discussed well.

By Tariq A

Jan 12, 2019

A good quality course, would have loved to have some programming exercises to go with this.

By Ankur K

Nov 13, 2017

It would have been a little better if some assignments were also provided with this course.

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 W

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 Takshshila R

Jul 1, 2020

This gives subtle but important knowledge

about training with different distributions.