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웨슬리언 대학교의 Machine Learning for Data Analysis 학습자 리뷰 및 피드백

4.2
별점
313개의 평가
67개의 리뷰

강좌 소개

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....

최상위 리뷰

ME

2017년 9월 18일

I enjoyed this course a lot. It's easy and I've learnt what I need to apply the machine learning techniques. Easy and simple. You don't need to be a mathematician.

BC

2016년 10월 4일

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

필터링 기준:

Machine Learning for Data Analysis의 65개 리뷰 중 51~65

교육 기관: Dinesh B

2017년 11월 5일

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.

교육 기관: Susanne W B

2016년 3월 1일

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.

교육 기관: Monika K

2016년 4월 29일

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

교육 기관: Ponciano R

2019년 1월 23일

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

교육 기관: Xiaoyang G

2016년 4월 15일

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

교육 기관: Tristan B

2016년 3월 1일

Not deep enough on diagnostic and interpretation

교육 기관: Karthick K

2016년 12월 12일

Course could be better

교육 기관: Siyang

2016년 10월 31일

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.

교육 기관: Deleted A

2017년 9월 4일

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.

교육 기관: Остроухов М Н

2018년 3월 6일

Unfirtunately superficial and outdated view on the subject.

교육 기관: THEODOSIOS M A

2016년 9월 3일

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.

교육 기관: Aurimas D

2019년 2월 1일

Absolutely unbalanced course. Course has 4 different topics, but it does not explain well non of them. In reality whole course should be dedicated for at least one of provided topics.

교육 기관: Liuyijie

2016년 6월 15일

Actually i want rate 0, as the instruction for the installation of new tools are quite vague and misleading

교육 기관: Darrel M

2021년 4월 29일

I could not complete the course, since it required SAS. This is not spelled out at the start.

교육 기관: karishma d

2020년 10월 20일

Most horrible course. Material are not enough and plus projects are hardly curated