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

4.6
étoiles
3,290 évaluations
373 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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251 - 275 sur 371 Avis pour Launching into Machine Learning

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 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 Rohini M

Apr 20, 2019

Little challenging than the first part of the specialization but thoroughly enjoyed deep diving into understanding basic concepts of Machine Learning without being overwhelmed. Great for a person who does not have any previous knowledge.

par Rakesh T

Feb 25, 2019

Will be good to dumb it down further. The last part is good, the first two parts can have better examples and find easier ways to explain the theoretical concepts for folks who have not heard these before.

par Francois R

Apr 08, 2019

Tensorflow Playground is awesome to understand some of the theory of Deep Neural Nets !

Theory on creating/managing models was good too.

The labs with BigQuery were not that interesting too

par Ankit R

Aug 17, 2019

I got a whole idea on how to work on data from scratch. Model selection, generalization, splitting of data and performance metric were few things I learned from this course.

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 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 Seokchan Y

Apr 26, 2019

This course is more focused on technical side of using Google Cloud system.

It would be better if students could do mini-projects so that we get used to handling GCS.

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 Mario R

Jan 13, 2019

Nice course, kind of introductory but necessary for someone who has no knowledge about Machine Learning basics and most relevant algorithms so far.

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 Super-intelligent S o t C B

Oct 30, 2019

Good course. Teaches some basics of machine learning. Thank you to Google for putting it together, and to Coursera for making it available.

par Ayman S

Jul 26, 2019

The instructor made several mistakes in reading the code like when he read the size of the file and interpreted as the number of records.

par Abdullah K

Jun 15, 2019

some ideas discussed need further elaboration, and there should be a set of slides provided or notes that summarizes the key concepts.

par Mahendra S C

Oct 03, 2019

High level of Machine learning algo and its implementation in cloud using Bigquery. simple but required in upcoming courses.

par Afreen F

Nov 22, 2018

Theory is all good and important. Lab could have been made more challenging and not just mere marketing of Google products.

par Putcha L N R

Dec 09, 2018

It was quite an insightful and playful way to learn about how the world's biggest AI company deals with AI problems!

par Aditya h

Aug 10, 2018

Detailed overview of Data Preparation for ML, however it just gives a walk through of a single example on Big Query