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Opiniones y comentarios de aprendices correspondientes a Launching into Machine Learning por parte de Google Cloud

4.6
estrellas
4,149 calificaciones
470 reseña

Acerca del Curso

The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training....

Principales reseñas

OD

30 de may. de 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 de dic. de 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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401 - 425 de 473 revisiones para Launching into Machine Learning

por HYUNSANG H

15 de abr. de 2019

Was good. Thank you!

por Effi M J M

18 de abr. de 2020

Un très bon contenu

por Kaushal K

11 de ago. de 2019

Awesome Experience.

por NIRMAL C I

11 de jul. de 2020

a bit complicated

por Kunal P

22 de jul. de 2019

Amazing to learn

por Kimkangsan

19 de oct. de 2018

nice intuition

por Richik G

16 de sep. de 2019

very valuable

por Ahmad T

25 de ago. de 2019

Excellent One

por Minwook P

30 de abr. de 2019

Good Course

por Stephen H

13 de abr. de 2019

good class

por Rohit K S

17 de sep. de 2020

Marvelous

por Saif A

18 de abr. de 2020

thank you

por Terry L

21 de abr. de 2019

따라하기가 어렵다

por Rohan M

23 de jun. de 2020

Great

por woncheol y

29 de abr. de 2019

goood

por KyeongUk J

21 de oct. de 2018

great

por Carlos P

26 de jun. de 2020

good

por 김세영

30 de abr. de 2019

GOOD

por 송지현

22 de abr. de 2019

good

por Prasenjit P

1 de feb. de 2019

OK!!

por Vinothini B

1 de oct. de 2018

good

por loossy

27 de abr. de 2019

v

por Jeremy B

8 de jun. de 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.

por Rocco R

10 de jul. de 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.

por Breght V B

22 de may. de 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?