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学生对 Google 云端平台 提供的 Launching into Machine Learning 的评价和反馈

4.6
4,078 个评分
465 条评论

课程概述

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...

热门审阅

OD
May 30, 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
Dec 1, 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 :)

筛选依据:

401 - Launching into Machine Learning 的 425 个评论(共 467 个)

创建者 Kimkangsan

Oct 19, 2018

nice intuition

创建者 Richik G

Sep 16, 2019

very valuable

创建者 Ahmad T

Aug 25, 2019

Excellent One

创建者 Minwook P

Apr 30, 2019

Good Course

创建者 Stephen H

Apr 13, 2019

good class

创建者 Rohit K S

Sep 17, 2020

Marvelous

创建者 Saif A

Apr 18, 2020

thank you

创建者 Terry L

Apr 21, 2019

따라하기가 어렵다

创建者 Rohan M

Jun 23, 2020

Great

创建者 woncheol y

Apr 29, 2019

goood

创建者 KyeongUk J

Oct 21, 2018

great

创建者 Carlos P

Jun 26, 2020

good

创建者 김세영

Apr 30, 2019

GOOD

创建者 송지현

Apr 22, 2019

good

创建者 Prasenjit P

Feb 1, 2019

OK!!

创建者 Vinothini B

Oct 1, 2018

good

创建者 loossy

Apr 27, 2019

v

创建者 Jeremy B

Jun 8, 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.

创建者 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.

创建者 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?

创建者 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

'''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 ?

创建者 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.

创建者 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

创建者 Matthew R

Nov 14, 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.

创建者 Anand H

Oct 7, 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.