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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
2,781 个评分
321 个审阅

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

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.

筛选依据:

226 - Launching into Machine Learning 的 250 个评论(共 320 个)

创建者 Peter H

Jul 21, 2018

Great to get some theoretical background on machine learning as well as some practical experience

创建者 Attila B

Aug 22, 2018

Really Interesting Course.Just a bit difficult to use the virtual labs.

创建者 Abhishek k

Aug 26, 2018

Very good course for beginners!

-1 star because I find labs to be less informational and practical and course to be more theoretical that practical!

创建者 Erwin V

Jul 17, 2018

Lots of interaction to put the theory into practice, nice!

创建者 Evren G

Oct 11, 2018

Excellent course content. Would be 5 stars if the labs forced you to think about how you could apply your theoretical learnings. Unfortunately, labs already have all the code populated, so you just end up running things with the illusion that you have understood everything. Give us labs that require us to solve a problem!

创建者 Kimkangsan

Oct 19, 2018

nice intuition

创建者 Gautam S

Aug 19, 2018

Liked the way the datasplit using BigQuery is explained, but would appreciate if more references and links to explore BigQuery is provided at end of the video.

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 Vinothini B

Oct 01, 2018

good

创建者 KyeongUk J

Oct 21, 2018

great

创建者 Amir Y

Aug 31, 2018

I was initially considered that it was too mathematical. But you really don't need to understand the minute details and just get the concepts. good for someone like me that doesn't intend to code but be able to understand enough of challenges and the process for developing models.

创建者 Hussain S K

Jul 15, 2018

It would have been better if there was a separate module with hands-on lab of SQL.

创建者 Ashar M

Jul 14, 2018

Great presenter. High energy engaging. The material is more difficult and to develop intuition of why the sampling needs to result in constant RMSE didn't come across.

创建者 Harsh A

Jun 21, 2018

History part was good.

创建者 Tim H

Apr 02, 2018

An interesting and short but intensive course. It introduces a lot of new (to me) tech such as Tensor Flow and Big Query. I learned a lot in a short time, but felt that if I hadn't already had a bit of a grounding in ML I might have been lost. During the course there were a few references to it being part of a specialisation, but I couldn't find what this was and it was not made clear before I signed up that this was the case. Perhaps that is why in the beginning it felt a bit like coming into something half way through, Overall then, interesting and useful, but would benefit from a bit of a clearer setup and explanation of how it fits into the overall Google cloud catalogue.

创建者 Sandeep K

Jul 02, 2018

we need more examples on precision/recall F1 scores..

创建者 Jitender S V

Jun 26, 2018

Starting assignment is a pain. AWS is relatively faster. Nevertheless good course.

创建者 Hasan R

Jun 03, 2018

Along with the complete codes, should also have some hands-on exercises for students to work on.

创建者 Suresh T

Jun 17, 2018

Some of the lecture has only talking, would be better if it got included more slides/reading materials.

创建者 Aditya K

May 19, 2018

Very useful intro to data processing, specially the hashing mechanism to partition the datasets.

The last lab was confusing because the data might have some invalid value. in the jupyter notebook, the sin, and arcsin values were not getting computed (probably?) as I got warning from python .

创建者 Phac L T

Jun 26, 2018

Overall it was great, and very instructive. However, the Short History of ML was a little bit confusing with too many unexplained words and too many details too early.

创建者 Andre A

May 26, 2018

Poor lab setup - have to repeat the step of creating a vm for every lab.

创建者 HYUNSANG H

Apr 16, 2019

Was good. Thank you!

创建者 Minwook P

Apr 30, 2019

Good Course

创建者 ohyesol

May 01, 2019

책으로만 접하기 힘든 기술을더 가까이 접할 수 있는 기회가 되어 좋았습니다.