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

4.8
stars
47,200 ratings
5,421 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

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.

JB
Jul 1, 2020

While the information from this course was awesome I would've liked some hand on projects to get the information running. Nonetheless, the two simulation task were the best (more would've been neat!).

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5276 - 5300 of 5,380 Reviews for Structuring Machine Learning Projects

By Pratik k c

Nov 5, 2017

Very Theoretical !!

By Tom M

Aug 24, 2017

I very simple class

By Mathew S

Dec 31, 2017

Its informational

By Kenneth C V

Oct 7, 2020

Complex Material

By Siwei Y

Nov 28, 2017

就两周的课, 我不知道算是凑数吗

By Mohit S

Jul 15, 2020

Not that good.

By Fotsing B K

Feb 25, 2018

to theoritcal

By Yide Z

Dec 17, 2017

too much bugs

By דוד ב

Aug 19, 2019

No Homework!

By Sean L

Oct 6, 2019

Bit tedious

By Leticia R

Aug 11, 2018

Bit boring.

By Wouter M

Jun 13, 2018

A bit short

By Zhen T

Dec 19, 2019

Too simple

By Gonzalo A M

Jan 16, 2018

Too short.

By Sunil S

May 26, 2020

Knowledge

By My I

Mar 15, 2019

too easy

By Артеменко Е В

Sep 3, 2017

Too easy

By vamshi

Aug 28, 2020

useful

By Jalis M C

Jan 7, 2021

good

By Debasish D

May 15, 2020

Good

By Sajal J

Oct 29, 2019

okay

By KimSangsoo

Sep 17, 2018

괜찮음

By Benedict B

Jul 27, 2018

ich

By Shawn P

Jun 8, 2018

k

By Daniel S

Mar 19, 2018

Definitely not worth paying for (and I literally completed this in one afternoon). Thankfully I did not pay, so it was not that bad value in fairness.

In honesty the lack of value from this course actually says a lot about Andrew Ng's original Machine Learning course, which was consistently excellent. Actually coding in Octave for that class cemented a lot of concepts as well, which this course does not.

The title of the course suggests this is pitched towards more advanced students who already know about Machine Learning but maybe not so much about best practices. This feels far too basic for that demographic. The practices are sensible though and useful, if maybe overly focussed on massive datasets as opposed to the ones that Google *doesn't* deal with on a daily basis. Things like SMOTE could have been mentioned as well, for example.

TL;DR: This feels like a missed opportunity. My advice is don't take it if you've done Andrew Ng's ML course. Google things after that and wait for a decent course that's pitched towards intermediate students.