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

4.6
2,408 notes
280 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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1 - 25 sur 287 Examens pour Launching into Machine Learning

par Raghuram N

Apr 27, 2019

Great course. Gradient descent and loss function concepts were explained well.

par Kunal P

Jul 22, 2019

Amazing to learn

par vipul a

Jul 21, 2019

Amazing! Really Feel Great on Learning! Hope to see you all @Google IO

par THAIKA S A C S

Jul 21, 2019

yes

par Saurav K

Jul 21, 2019

It's not much helpful

par William S

Jul 17, 2019

Well laid out, organized, and packed with useful information.

par Avi k

Jul 14, 2019

Nice Explanation with examples and history.

par Prem P

Jul 12, 2019

this is the best course. i liked it very much

par Hafiz F A

Jul 12, 2019

Good course for learning

par Sachin K M

Jul 11, 2019

Really helped me to understand the nuts and bolts of ML and Data preparation along with preprocessing.

par Rocco R

Jul 10, 2019

Contingency tables and ROC graphs were poorly characterized and presenter resorted to obfuscation to mask his unfamiliarity with this basic statistical concept. Furthermore, when the proposed task is to "Identify pictures containing house cats", correctly identifying a picture that does not contain a house cat (True Negative) does NOT count as a successful prediction. You are confusing sensitivity with specificity in your so-called confusion matrix.

With respect to labs, you should warn students to leave their notebooks open so we do not have to reload everything. Also in the cab fare exercise the presenter did not elaborate on the fact that the RMSE's were higher than the predicted fare and mistakenly excluded time of day when in fact fares increase during rush hour.

par Jafed E

Jul 06, 2019

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

par Emily T

Jul 05, 2019

Excellent course for people who are completely new to ML, GCP, and TF.

par sheikzaidh

Jun 30, 2019

good exprience with gcp

par Nhan T N

Jun 30, 2019

Nice course for first imagination of TensorFlow and how to process dataset. Thanks!

par Mike W

Jun 22, 2019

The notebook based demos are unfortunately pretty useless as labs. All of these courses would be much improved with real labs that require the student to build the system.

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 Snehil S

Jun 15, 2019

Very detailed explanation

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 David N

Jun 14, 2019

Learning the approach was very valuable. The exercises were just copy and paste of a bunch of code that it isn't expect we understand.

par William A R B

Jun 09, 2019

Muy bueno para aprender lo básico del Machine Learning para antes de llegar a programar

par Someron B

Jun 01, 2019

Great course

par chang g k

May 31, 2019

helpful to understand ML conceptually

par Nayanajith P

May 26, 2019

It's Nice

par Feng N

May 14, 2019

Good tutorial with insights to real implementation of ML.