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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
4,121 个评分
467 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:

301 - Launching into Machine Learning 的 325 个评论(共 469 个)

创建者 엄윤식

Apr 28, 2019

good

创建者 정태표

Apr 28, 2019

good

创建者 김경훈

Apr 27, 2019

Good

创建者 박성우

Apr 27, 2019

good

创建者 강효진

Apr 26, 2019

GOOD

创建者 kim m s

Apr 25, 2019

good

创建者 김정민

Apr 25, 2019

사랑해요

创建者 JeeinOh

Apr 25, 2019

good

创建者 고상균

Apr 24, 2019

good

创建者 unranker

Apr 23, 2019

good

创建者 KimHJ

Apr 18, 2019

good

创建者 dhokim

Apr 17, 2019

nice

创建者 이동규

Apr 14, 2019

good

创建者 Atichat P

May 31, 2018

Good

创建者 Bruno E S B

Nov 17, 2019

TOP

创建者 SOOFI O

Jul 21, 2019

yes

创建者 Woojin J

Apr 22, 2019

fun

创建者 吳金霖

Sep 13, 2019

OK

创建者 지정수

May 2, 2019

bu

创建者 ᄋᄋ

Apr 30, 2019

..

创建者 K J

Apr 21, 2019

11

创建者 Harshkumar

Jan 7, 2019

NA

创建者 Matthias D

May 11, 2020

T

创建者 Ravi P

Sep 22, 2018

Very good course, but I did have some problems with how the instructors recommend reproducible splitting the Train, Validation and test datasets. By splitting on a date-hash you are not choosing a random sample. For example, in both the airplane and taxi example, If more winter dates were samples, we would expect more delays and longer taxi rides (and thus more expensive). Wouldn't it be better to split randomly, but reproducibly? In both R and Python SKlearn library you can 'set the seed' when splitting the data which seems like a much less biased and just as reproducible way to split the dataset. The other constructive criticism is to not give all the answers right away to encourage us students to actually write the SQL code (or at least part of it) ourselves.

创建者 Armen F

Jan 29, 2019

This course provides a great synopsis of different machine learning models and their nuances. If you haven't seen machine learning before, you will probably need to go slowly and look up some of the concepts on your own. There are a lot of ML terms thrown around without any explanation, so be ready for that if you're new to this. The best part of the course was the Google TensorFlow Playground, where you can experiment with tuning neural networks to classify different types of datasets. The speakers in this course are all very good and the material is well organized. The reason for giving 4 stars is that the quizzes and lab exercises were much too easy, so anybody can get 100% for this course, which makes the grade (and passing score) meaningless.