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Avis et commentaires pour d'étudiants pour Launching into Machine Learning par Google Cloud

4.6
étoiles
3,904 évaluations
449 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

OD
30 mai 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.

PT
1 déc. 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 :)

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376 - 400 sur 447 Avis pour Launching into Machine Learning

par Kimkangsan

19 oct. 2018

nice intuition

par Richik G

16 sept. 2019

very valuable

par Ahmad T

25 août 2019

Excellent One

par Minwook P

30 avr. 2019

Good Course

par Stephen H

13 avr. 2019

good class

par Rohit K S

17 sept. 2020

Marvelous

par Saif A

18 avr. 2020

thank you

par Terry L

21 avr. 2019

따라하기가 어렵다

par Rohan M

23 juin 2020

Great

par woncheol y

29 avr. 2019

goood

par KyeongUk J

21 oct. 2018

great

par Carlos P

26 juin 2020

good

par 김세영

30 avr. 2019

GOOD

par 지현 송

22 avr. 2019

good

par Prasenjit P

1 févr. 2019

OK!!

par Vinothini B

1 oct. 2018

good

par loossy

27 avr. 2019

v

par Jeremy N B

8 juin 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

10 juil. 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

22 mai 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

23 sept. 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

'''SELECT

COUNT(vendor_id)

FROM

`nyc-tlc.yellow.trips`'''9

SELECT

COUNT(vendor_id)

FROM

`nyc-tlc.yellow.trips)

In that context, I think MOD(...) meaning is totally different ?

par Anubhav S

27 juil. 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 Tom

21 août 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

10 août 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

14 nov. 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.