May 31, 2020
Amazing course. For a beginner like me, it was a shot in the arm. Excellent presentation very lively and engaging. Hope to see the instructor soon in a another course. Thanks so much. I learned a lot.
Dec 02, 2018
This is an awesome module. It will open up so much inside story of ML process which is core of the topic with such a simplicity. It greatly increases my interest into this topic and this course :)
par Prasenjit P•
Feb 01, 2019
par Vinothini B•
Oct 01, 2018
Apr 27, 2019
par Jeremy N B•
Jun 09, 2018
I've spent the past three years studying ML and AI starting from the ground up with Calculus, Linear Algebra, basic data science techniques and eventually Deep Learning. I am primarily interested in this specialization because I would like to begin using GCP professionally. This course provides a very quick surface level overview of the "history" of ML and the techniques that have been aggregated to make up the current cutting edge of AI in practice. Already having a grasp on many of the concepts, I was able to zip through this course in a few hours and found it basic. If you're looking for something a bit more challenging, I would recommend the DeepLearning.ai specialization also available on Coursera. This course works well as a refresher and a high level overview. If you are completely new to the field, be warned that there is quite a terminology to be unpacked that is covered more thoroughly in other courses on Coursera. The University of Washington machine learning specialization (though sadly cut short) would be a much better starting place, if you are completely new to the topic.
par Rocco R•
Jul 10, 2019
Contingency tables and ROC graphs were poorly characterized and presenter resorted to obfuscation to mask his unfamiliarity with this basic statistical concept. Furthermore, when the proposed task is to "Identify pictures containing house cats", correctly identifying a picture that does not contain a house cat (True Negative) does NOT count as a successful prediction. You are confusing sensitivity with specificity in your so-called confusion matrix.
With respect to labs, you should warn students to leave their notebooks open so we do not have to reload everything. Also in the cab fare exercise the presenter did not elaborate on the fact that the RMSE's were higher than the predicted fare and mistakenly excluded time of day when in fact fares increase during rush hour.
par Breght V B•
May 22, 2018
Using hash function doesn't seem a good way to split the dataset:
-You could discard a relevant feature
-You will group data on a similar characteristic, which might not represent the population well
-You don't have control over the size of your split since the feature will not likely be uniformly distributed
Can't we add an index feature/column and do a modulo on the index?
par Tomomasa T•
Sep 23, 2018
In The last lab, teacher says that there is 100,000 in data set , then we extract 10,000 from data set.
But there is 1,000,000,000( I checked by
In that context, I think MOD(...) meaning is totally different ?
par Anubhav S•
Jul 27, 2019
I feel that the flight and taxi cost estimation was kinda rushed. It was hard for me to follow. Ii having less knowledge about SQL was finding it to be tough. Before that, everything was clean and awesome. I think I have to revisit these courses after learning SQL better.
Aug 21, 2018
The course is ok. Several complicated concepts are expected to be known, other very easy ones are explained in detail. However in some phases too high level, I am definitely missing some course resources to work with.
Was hoping for more hands-on experience.
par Venkata S S G•
Aug 10, 2019
good course. but it is just like an intro regarding how to use google cloud platform. but theory part was decent. can give it a try. but lectures were really indulging
par Matthew R•
Nov 15, 2018
Some good material here, but at times it feels like an ad for GCP. And the labs are not very inventive. You just run a python notebook with canned stuff in them.
par Anand H•
Oct 08, 2018
While the concepts covered were good and very valuable, I didn't like the lab sessions. Just having to walk through code is not a good way to get hands-on.
par José C L A•
Apr 18, 2020
Too much content for just one week. Exercises solved and not made for students to resolve them. Suggesting more complicated tasks is not teaching.
par Anupam S•
Nov 30, 2019
I could only sustain it because I have completed basic ML courses earlier. Too many tech concepts & jargons overloaded in a very short time.
par David N•
Jun 14, 2019
Learning the approach was very valuable. The exercises were just copy and paste of a bunch of code that it isn't expect we understand.
par Nour L•
Aug 29, 2018
It felt too hard. I liked because it gives a very good idea but the concept was too hard especially with the math involved
par Cooper C•
Jan 15, 2020
This course is just ok. It is not interactive and I don't feel that I learned much when compared with other ML courses.
par Nils W•
Sep 28, 2019
The course is good, but I missed the hands on part. You really do not need to code. That should be changed.
par Pravin A J D•
Jan 05, 2019
not enough practical content such as types of machine learning and different algorithms to be used etc
par Srinivasan D•
Jul 27, 2020
In the labs, I kept getting disconnected from the Jupyter notebooks, and had to keep reloading them.
par Jon B•
Jun 11, 2018
Course includes good presentation material which unfortunately is not available to download.
par Aseem B•
Aug 23, 2018
If you already know ML there isn't much in this course that will be value addition for you.
par Fabrizio F•
Jul 29, 2018
The course is very well explained, but I was already aware of most subjects.
par Kevin C•
Jul 15, 2018
There is a little more content here than in the 1st course.
par Prateek D•
Aug 11, 2018
Please add more content, don't make it just intro types