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

4.6
2,934 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

PT

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 :)

PA

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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301 - 325 sur 329 Examens pour Launching into Machine Learning

par Saurav K

Jul 21, 2019

It's not much helpful

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.

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 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 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 Shawn W

Sep 19, 2019

A bit difficult when introducing the ML history

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 Soroosh R

Feb 15, 2019

The instructors were talking most of the time in a monotonous way without showing proper slides or elaborating the materials. Overall it was a very poor introduction of the ML. The two labs were good, however it was surprising to me that they did not commented out the notebook codes!

par Ragy I

Mar 20, 2019

Maths and ML stuff are explained very poorly... very rushed and people speaking are clearly reading from a script which makes it difficult to understand and follow on =

par Sean S

Jan 28, 2019

The labs really just lay everything out for you, and don't challenge you to learn anything for yourself.

par Francesco C

Jul 12, 2018

labs not really useful

par Ligeng X

Nov 09, 2018

Barely learn anything

par Thomas V

Nov 15, 2018

No real implementation

par Srikar K

May 01, 2018

The LABs in this course did not work for me. I was not able to open DataLab in my Lab sessions. This is quite frustrating to me.

par Ronnie R

May 22, 2018

instructor more plain and hard to hear so many math concepts just with words not visual enough, seems like instrctor is not breaking it down like the first course.

par Brennon B

May 28, 2018

Google has clearly put a great deal of effort into constructing and polishing this course. However, the certificate is really nothing more than a participation trophy. I understand that putting the course materials (videos, demos, etc.) are time-consuming. An exceptional course, though, needs to actually test that the learner has, indeed, learned. When all of the labs/assignments require nothing more than clicking through them, they fall far short in this respect.

par Kevin D B

Sep 16, 2019

It's covering a lot but brushing over things too quickly and using a ton of jargon.

par Praveen K M

Feb 17, 2019

I'm not able to access the video lectures even though I purchased and completed this course 6 months back

par Arman A

Apr 11, 2019

Pros: Tensorflow is an excellent framework for deep learning

Cons :

1- The way this material is designed is 10 X SHIT

2- Either teach properly or don't teach at all.

par sasidhar m

Jul 16, 2018

No hands on learning.

par Yaron K

Jul 14, 2018

It's unclear for who this course is meant. It mixes basics like train-validate-test with lectures that use machine learning terms that only have meaning to someone who has already knows ML terminology. If you're looking for a good introduction to ML - check out Andrew Ng's course.

par Chi S S

Sep 02, 2018

Did not learn much! Poorly instructed courses.

par Diretnan D

Nov 05, 2018

Too much scary information provided at once combined with the mindbending sql queries and data parsing

par Ehsan F

Nov 13, 2018

This is the most superficial course I have ever taken. I just waisted my time.

par Neeraj

May 28, 2018

looks more like a promotional course from google instead of an acutal learning experience.

Also the labs have no data on the code used, it is assumed the learners are well acquianted with the technology used that is specific to google.