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Avis et commentaires pour l'étudiant pour Launching into Machine Learning par Google Cloud

2,937 notes
330 avis

À propos du cours

Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of data science problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization; we talk about methods of doing so in a repeatable way that supports experimentation. Course Objectives: Identify why deep learning is currently popular Optimize and evaluate models using loss functions and performance metrics Mitigate common problems that arise in machine learning Create repeatable and scalable training, evaluation, and test datasets...

Meilleurs avis


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 :)


Aug 04, 2018

Good course, covering all the basics about machine learning and most importantly, everything that surrounds an ml project and you need to take into account to make your ml project successful.

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276 - 300 sur 329 Examens pour Launching into Machine Learning

par Ankit R

Aug 17, 2019

I got a whole idea on how to work on data from scratch. Model selection, generalization, splitting of data and performance metric were few things I learned from this course.

par Ahmad T

Aug 26, 2019

Excellent One

par xin w

Aug 31, 2019

It is better to give an explanation in detail of the codes

par Richik G

Sep 16, 2019

very valuable

par 전진호

Apr 30, 2019

개념적 이해를 돕고, TensorFlow를 통하여 모델링 및 테스트를 직접 경험해 볼 수 있어서 좋습니다.

par 김세영

Apr 30, 2019


par Abdullah K

Jun 15, 2019

some ideas discussed need further elaboration, and there should be a set of slides provided or notes that summarizes the key concepts.

par Shivam K

Oct 01, 2019

Lesson Learnt: Best model might not be a good model in real world! Generalization is important!

The labs had issue of disconnection. My jupylab notebooks were frequently disconnecting from the server and I had to manually reconnect them to kernel.

par Mahendra S C

Oct 03, 2019

High level of Machine learning algo and its implementation in cloud using Bigquery. simple but required in upcoming courses.

par 令安 趙

Oct 08, 2019

I hope for traditional Chinese ver. subtitle for those who are not good at English.

par Vinit K

Jan 22, 2019

Very Basic again

par KimNamho

Apr 12, 2019

thank you

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 Kevin C

Jul 15, 2018

There is a little more content here than in the 1st course.

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 Prateek D

Aug 11, 2018

Please add more content, don't make it just intro types

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 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 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 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 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 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 Jon B

Jun 11, 2018

Course includes good presentation material which unfortunately is not available to download.

par Tom

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 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.