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Avis et commentaires pour l'étudiant pour Launching into Machine Learning par Google Cloud

4.6
2,741 notes
317 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

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.

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226 - 250 sur 316 Examens pour Launching into Machine Learning

par Attila B

Aug 22, 2018

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

par 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!

par Erwin V

Jul 17, 2018

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

par 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!

par Kimkangsan

Oct 19, 2018

nice intuition

par 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.

par 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.

par Vinothini B

Oct 01, 2018

good

par KyeongUk J

Oct 21, 2018

great

par 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.

par Hussain S K

Jul 15, 2018

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

par 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.

par Harsh A

Jun 21, 2018

History part was good.

par 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.

par Sandeep K

Jul 02, 2018

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

par Jitender S V

Jun 26, 2018

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

par Hasan R

Jun 03, 2018

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

par Suresh T

Jun 17, 2018

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

par 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 .

par 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.

par Andre A

May 26, 2018

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

par HYUNSANG H

Apr 16, 2019

Was good. Thank you!

par Minwook P

Apr 30, 2019

Good Course

par ohyesol

May 01, 2019

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

par 전진호

Apr 30, 2019

개념적 이해를 돕고, TensorFlow를 통하여 모델링 및 테스트를 직접 경험해 볼 수 있어서 좋습니다.