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Avis et commentaires pour d'étudiants pour Launching into Machine Learning par Google Cloud

3,740 évaluations
433 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


May 31, 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.


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 :)

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276 - 300 sur 431 Avis pour Launching into Machine Learning

par 고상균

Apr 24, 2019


par unranker

Apr 23, 2019


par KimHJ

Apr 19, 2019


par dhokim

Apr 18, 2019


par 이동규

Apr 14, 2019


par Atichat P

Jun 01, 2018


par Bruno E S B

Nov 17, 2019



Jul 21, 2019


par Woojin J

Apr 22, 2019


par 吳金霖

Sep 13, 2019


par 지정수

May 02, 2019


par 김예린

Apr 30, 2019


par 김진혁

Apr 21, 2019


par Harshkumar

Jan 08, 2019


par Matthias D

May 11, 2020


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 Armen D 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.

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 SanjaySuman S G

May 02, 2020

This course gave me a practical understanding on how machine learning works , how an ML model can be optimized with minimum error and enhance the performance of the model in a better way . I like to thank Google Cloud Team who has taught this course in very interesting way and I'm looking forward to learn the next course of this specialization.

par Matthew B

Jun 17, 2019

wish they teach you more of the programming side of things and knowing exactly what and why to upload different libraries and or show us how to build these in the labs. Not the first ML course, I've taken but some new people may be a bit confused on the python / setting up / sql even if they have a general knowledge to python and sql.

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 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 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 Shivam K

Oct 01, 2019

Lesson Learnt: Best model might not be a good model in real world! Generalization is important!

The labs had issue of disconnection. My jupylab notebooks were frequently disconnecting from the server and I had to manually reconnect them to kernel.

par R. K E P

Apr 19, 2020

Great introductory to Machine Learning, although the history part is a bit overwhelming for me because he is using a lot of jargon and there is a lack of visualization. But, the rest of the course is great, especially about the sampling.