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Avis et commentaires pour d'étudiants pour Machine Learning for Data Analysis par Université Wesleyenne

250 évaluations
54 avis

À propos du cours

Are you interested in predicting future outcomes using your data? This course helps you do just that! Machine learning is the process of developing, testing, and applying predictive algorithms to achieve this goal. Make sure to familiarize yourself with course 3 of this specialization before diving into these machine learning concepts. Building on Course 3, which introduces students to integral supervised machine learning concepts, this course will provide an overview of many additional concepts, techniques, and algorithms in machine learning, from basic classification to decision trees and clustering. By completing this course, you will learn how to apply, test, and interpret machine learning algorithms as alternative methods for addressing your research questions....

Meilleurs avis


May 07, 2020

Clear and explanatory approach to the object. Instructors have great teaching transmissibility.


Oct 05, 2016

Very good course. I recommend to anyone who's interested in data analysis and machine learning.

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26 - 50 sur 52 Avis pour Machine Learning for Data Analysis

par Artem A

Apr 14, 2016


par Steven L

Aug 30, 2017


par Mathilde v E

Jul 21, 2016


par Shreyans J

Jun 27, 2019

It is definitely a good one and easy to understand... What I mostly struggled was with the data sets which were hard to find... probably if some data sets would have been provided would have really helped - would have been easier to run the program through with multiple sets and see the best results across.

Essentially the major learning happens when you actually run it on your own (for which you may have to go back and forth with the instructors examples / teachings.

par Michael B

Jan 03, 2017

Excellent introductory course on machine learning focusing on simple linear and multiple regression, lasso regression and k-means clustering. A background in Python programming is useful but not required as the instructors discuss the techniques with annotated code examples.

par Christine R

Aug 15, 2017

I definitely appreciate this information on Machine Learning. And from an outsider perspective would say it is quite clear - when I put it into practice will see how it goes. I do like the video format and will say that through out the course the instructor

par Manikanta K

Apr 27, 2020

Since it is a part of a specialization, the topics start somewhere in between and is only recommended for those who have completed the previous courses with in these specialization.

par Mengyue S

Mar 22, 2016

More examples in coding and results are expected. So it is more convenient for students to compare different results and understand deeper


Jan 06, 2018

More Implementation oriented and less math

also contains distracting background videos when explaining important concepts

par Oriana A

Mar 21, 2017

Very good. I enjoyed doing it and learned a lot.

I would have liked that it had included r as one of the softwares.

par Leonardo A

Oct 31, 2016

Excellent course, some basic tecniques of Machine Learning are implemented in Python and SAS.

par Ivan C

Mar 03, 2016

I would like to have an opportunity to contact my reviews.

par Drew M

Oct 13, 2018

Learned some really useful ML models.

par Lee X A

Mar 22, 2016

Disadvantages : Lacks Rigour, Lacks Support from instructors , Expensive , Peer review ( this is somewhat bad as most barely give any comments, though towards the end, reviews tend to be pretty good). *** DISCLAIMER *** I am not statistically significant as i only receive 3 reviews per week.

Advantages :Quick to earn cert, prewritten code available for easy use. Assignments on your own data. This is probably useful for people wanting to learn techniques for data analysis, who need not go too deep into the technique.

I would recommend this to people learning techniques for data analysis in various non-mathematical and non-statistical fields, though the content lacks rigour, and you need outside sources to help understand techniques.

This course IS NOT WORTH PAYING USD79, there are definitely other courses much more worth the money. You can audit it for free, if you do not want a cert.

par Dinesh B

Nov 05, 2017

The material is good but the functions should have been explained in more detail. There is kind of repetition of same thing. It should have given some more examples and changes in code to explain the different types of ways to apply same algorithm.

par Susanne W B

Mar 01, 2016

It was okay for an introduction to the methods, but I would have liked to learn about them in more details, i.e. the course was too short.

par Monika K

Apr 29, 2016

This level of detail was good for easier statistical concepts but there are much better courses on Coursera for Machine Learning

par Ponciano R

Jan 23, 2019

It´s a good course but it does not goes deep enough in the examples and techniques.

par Xiaoyang G

Apr 16, 2016

It's not an intro class. But you can practice a lot if you know something.

par Tristan B

Mar 01, 2016

Not deep enough on diagnostic and interpretation

par Karthick K

Dec 12, 2016

Course could be better

par Teo S

Nov 01, 2016

Personally felt this course have a lot more potential. The explanations in the lectures felt very robotic especially when describing the scripts. At times the lectures slides felt like displaying the subtitles and reading off them. A lot more diagrams could have been illustrated for explanations. I have to watch other videos in youtube to get a better grasp of the concepts.

Good thing is that this is an introductory course, and the codes are given.

par Vanessa Q M

Sep 05, 2017

It goes over and over about the adolescent examples, which makes it annoying. The quality and production of the video is bad. Why to use moving scenes in the background (like the horses or the highway)? That's distractive and takes the focus of the content, better to use a blackboard.

par Остроухов М Н

Mar 06, 2018

Unfirtunately superficial and outdated view on the subject.


Sep 03, 2016

Not good at all.We see different processes without anyone making clear the reason why we should apply this processes ,under which conditions and what is the question that we have to answer when we apply these processes.The only good is that we get into some new terms and see new things.I could say that for me,it wouldn't make such a difference if it wasn't in this specialization.