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Avis et commentaires pour l'étudiant pour Apprentissage mechanique pratique par Université Johns-Hopkins

4.5
2,639 notes
498 avis

À propos du cours

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation....

Meilleurs avis

AD

Mar 01, 2017

Issues of every stage of the construction of learning machine model, as well as issues with several different machine learning methods are well and in fine yet very understandable detail explained.

AS

Aug 31, 2017

Highly recommend this course. It makes you read a lot, do lot's of practical exercises. The final project is a must do. After finishing this course you can start playing with kaggle data sets.

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351 - 375 sur 489 Examens pour Apprentissage mechanique pratique

par Yew C C

Feb 04, 2016

Wish to have more systematic structure, detail information and hands-on exercises.

par Vincent G

Oct 22, 2017

appropriately challenging material.

par Piyush P

Jul 13, 2017

good context

par Minki J

Dec 29, 2017

good to know many concepts of machine learning model.

par Pieter v d V

Jun 28, 2018

Very quick overview. If you really want to know something about it read the reference books.

par A. R C

Oct 20, 2017

I enjoyed it but it needs indeed to deep into many concepts, which are just briefly named during the course.

par Matthew C

Dec 11, 2017

Lots of good material, but some things (like PCA) didn't receive enough coverage in the lectures. The quizzes also weren't great at testing the material in the lectures.

par KRISHNA R N

Apr 19, 2018

nice

par Brynjólfur G J

Sep 25, 2017

Some problems with current and old versions of packages and problems with using other packages on different operating systems. Though that did also help foster an independent research style which will help me in the future.

par Sabawoon S

Sep 14, 2017

Excellent course, very practical. Found the project challenging as preprocessing data required some knowledge of the limitation of the RandomForest method i.e. both train and test needs to have same classes of data with similar levels.

par Nguyen T T

Apr 20, 2018

Thank you! My teacher. The course very good. Many thanks

par Tiziano V

May 25, 2017

Interesting the final assignment.

par Níck F

Sep 27, 2016

Was pretty good, but quite short and some assignments did not align as well with the lecture material as they could have.

par Andrew K

Mar 13, 2017

So why four stars vs five stars, of all the Data Science Certification courses that I have taken: i) some of the examples and quiz challenges don't work as they should, ii) Machine Learning is rapidly changing area - should be updated to reflect this and perhaps a high level taste of Deep Learning, iii) posting the Final Project is overly complicated relative to methods of the other courses - this should be cleaned up - still not clear how point to a github repo link and also have a rendered html page working from that same link - requires two links to present materials and must use default names like index vs. a project name.

par Alia E

Jul 13, 2018

Really could have used a few more examples.

par Saurabh K

Mar 09, 2017

Very useful course to develop level knowledge in machine learning.

par Bruce I K

Oct 20, 2016

It's a great course but I hope you add a few things. The course about the machine learning algorithm is so basic. Please get deep into the machine learning algorithm. Then it would become the perfect course.

par carlos j m r

Oct 05, 2017

I thought there were Swirl practice as other courses, however this course is very good.

par Erika G

Jul 28, 2016

I learned a lot in this class. There are slight gaps from the depth of material covered in the lectures to the quizzes and assignment. If you're good at researching online, you'll be fine.

par Karthik R

Aug 07, 2017

Bit tough, but I will have to say, good introductory course.

par BIBHUTI B P

Jul 24, 2017

This was a superb module which created a deep learning insight within me focusing on future technology

par Coral P

Aug 19, 2017

The project is good in letting us practise what we learnt

par Subrata S

Mar 09, 2017

Very good course. The content can be enriched with some more technical details behind the various techniques. There needs to be 1 more course on Practical Machine Learning in the specialization as 1 course is far too less for such a vast topic.

par Paul K

Apr 08, 2017

Very good summary of strengths/weaknesses of various machine learning algorithms. This lecturer's style and production quality is much higher than in the previous two courses in the specialization series.

par Mehul P

Oct 03, 2017

Good ML overview.