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Exploratory Data Analysis for Machine Learning(으)로 돌아가기

IBM의 Exploratory Data Analysis for Machine Learning 학습자 리뷰 및 피드백

4.6
별점
687개의 평가
159개의 리뷰

강좌 소개

This first course in the IBM Machine Learning Professional Certificate introduces you to Machine Learning and the content of the professional certificate. In this course you will realize the importance of good, quality data. You will learn common techniques to retrieve your data, clean it, apply feature engineering, and have it ready for preliminary analysis and hypothesis testing. By the end of this course you should be able to: Retrieve data from multiple data sources: SQL, NoSQL databases, APIs, Cloud  Describe and use common feature selection and feature engineering techniques Handle categorical and ordinal features, as well as missing values Use a variety of techniques for detecting and dealing with outliers Articulate why feature scaling is important and use a variety of scaling techniques   Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience  with Machine Learning and Artificial Intelligence in a business setting.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics....

최상위 리뷰

AE

2021년 9월 26일

Very detailed course of Exploratory Data Analysis for Machine learning. Ready to take the next step in data science or Machine learning, this is great course for taking you to the next level.

ML

2021년 9월 21일

Excellent, very detailed. However, if the lessons can be expand for hypothesis testing and some of their common test like T test, Anova 1 and 2 way, chi square,..it would be better further.

필터링 기준:

Exploratory Data Analysis for Machine Learning의 163개 리뷰 중 1~25

교육 기관: peker m

2020년 11월 30일

This particular course as many others in Coursera, provides minimum possible knowledge with the lowest level of course quality. I will elaborate my point as following;

1) Instructor does not even know the actual mathematical foundations of what he is presenting. He provides example notebooks supposedly process a particular data which does even not exist. I personally and very discretely provided my comments regarding his conceptual mistakes in his presentations without receiving yet any feedback or observing a change in course material.

2) The final projects, even though presenters make money out of this course, are evaluated by peers. With that in hand I have a PhD in Physics, but somehow a random course taker who did not even acquire 10% of my math and coding throughout his/her education is evaluating my final project. Moreover, this person does not even understand well what is written in my project and gives me some random grades. As a result, I don't even get a feedback at all about my grade and or details of his/her grading.

Now, let me put these together. Coursera was a go-to place back in time. Nowadays its quality is not even close to be called 'mediocre'. I had the belief that at least some information can be gained and somehow it was worth taking class(es) back in time. After this horrible and totally not valuable experience, I do not think Coursera is doing a notable or at least an average job. I also have no faith in the comments that you guys publish here from your course takers. I have no reasons to believe them. I would like to clearly indicate that I am neither planning to take another course from Coursera, nor I am planning to suggest anyone to take a course from Coursera in near future.

교육 기관: Kevin S

2020년 11월 8일

Really Poor Teaching. Concepts that were clear earlier was made unclear due to poor intuitive examples. Few concepts were taught really well. But especially around the Hypothesis Testing part, the quality dropped very steeply.

교육 기관: Arnold D

2020년 11월 28일

I feel like the instructor's inability to explain things in detail stems from the fact the he doesn't really understand it as well. feels like:

Boss: "hey I need you to present this tutorial"

Instructor: "Sure thing boss, I just need to read it right?"

Boss: "Yes, but you also need to pretend that you actually understand it"

Peer reviews are also filled with a bunch of trolls who will give you a grade of 0 just for the fun of it - this was the final nail for me. I cancelled my subscription.

교육 기관: Tusarkanti N

2020년 11월 6일

Not clear pre-requisites. Instructions far off from the learning objectives mentioned in the beginning which makes it difficult to catch up.

교육 기관: Charley L

2020년 11월 18일

Does not go into detail and explain how to really code for hypothesis testing

교육 기관: Christopher W

2020년 12월 31일

ADVICE BEFORE YOU DO THIS COURSE -- Look at the assignment and choose a data set that you can work with. Try and replicate the techniques from the explanation videos on your data set as you go through the course and then you'll be pretty much have a completed assignment by the time you finish the videos.

A slight problem with this course is the hypothesis testing bit of the assignment. The problem could be as deep as the ocean. If you choose a data set that you know you can get a good binary test from you'll cut down your completion time without losing any valuable learning experience.

교육 기관: Nihar D

2020년 10월 19일

The concepts are not explained in details. The instructor seems to read from a transcript which may not be the best way of teaching. However, content is great and it can help build a strong foundation.

교육 기관: Shangying W

2020년 9월 5일

One jupyter notebook is not able to run because a dataset and a python module needed for running the notebook is not provided. Lots of classmates ask about help in the discussion forums, however, no TA or any help is provided.

교육 기관: Tao K

2021년 3월 19일

great course content overall. couple thoughts related to improvement opportunities: 1.could you consider sharing more python sample code for each section? These samples do not have to be talked through - just there available for students to download and keep. 2. I had trouble submitting my course assignment initially due to the confusing instructions on the webpage. The page said Additional Comment box was Optional but it turned out that one would still have to put in "No Additional Comments". Otherwise assignment could not be turned in. This was a frustrating experience that could be avoided for others if the webpage instruction was more clear and consistent.

교육 기관: Cevdet U E

2021년 2월 28일

It does provide useful information but not much. There is very less hands-on practice provided.

교육 기관: Sneha R

2021년 9월 1일

not very clear and not detailed. jumping through courses without teaching basics

교육 기관: Sashank T

2021년 1월 25일

In my opinion this course is really bad, the content was not that good and honestly it is not up to the level of a Professional Certificate.

교육 기관: Pulkit K

2021년 10월 9일

E​xcellent course . Covers all the necessary information for beginners. Although I noticed people from non-statistic backgorund have a lot of misunderstaning about hypothesis testing and p-values which is briefly talked about in the course. I would recommend bootstrapping for non-statistic background students ( https://moderndive.com/ - Although in 'R', still an excellent site that teaches about bootstrapping in very simple language for beginners. I highly recommend it for all non-stats students)

I​ have one more suggestion, it would be really nice, if the course can add some examples about usage of hypothesis testing in machine learning besides research purposes like A/B testing, binning of categorical features and so on.

교육 기관: Ferley A

2021년 1월 24일

if you really make the exercises and the final assignment the course really contributes you to better understand Data Analysis

교육 기관: Alice Y

2021년 9월 8일

I leant EDA in the uni with R. This course teaches the same thing in python and adds some extra stuff, really good course.

교육 기관: Verena-Henriette P S t K

2020년 10월 12일

A very good course if you take it seriously! Good practical tasks where you learn much!

교육 기관: Iddi A A

2020년 12월 7일

Excellent presentation. Learnt quite a lot.

교육 기관: Isa B

2021년 2월 21일

COOL COOL COOL

교육 기관: Ashish P

2020년 12월 26일

The Course is quite detailed and well explained regarding the techniques and fundamentals required for exploratory data analysis. Sometimes although I found the contents being spoken in the video hard to understand because of the flow and the accent, but then reading the subtitles helped. Also, one suggestion would be to provide a presentation or some pdf documents for the most commonly used Python commands for various libraries like Pandas etc. for data handling (starting from data reading, cleaning upto hypothesis testing and further). This is because to makes hand notes of all the commands from the demo videos takes quite some time.

All in all, big credits to the team for such a well prepared course material!

교육 기관: Priyanka B

2020년 11월 30일

The course was really helpful in understanding basic ML concepts and the computational framework we can use for EDA.

But a lot of students had problems with ghost reviews where they received 0 points across the rubric. It took me two days to finally get my assignment graded properly and lost significant time in correcting the problem. Coursera should really do something about this issue.

교육 기관: Darish S

2020년 12월 1일

The only reason that I do not give it 5 stars is because the website of coursera is not good enough to handle the peer review assignments at the end of the course.

교육 기관: nakul c

2021년 10월 11일

I​t doesn't cover T-tests and f-Tests which are often used. Also could be better descriptive about some topics.

교육 기관: Nabeel S

2021년 12월 18일

instructor does not capable to develop the user interest in course. Just reading the slides

교육 기관: Ritvik C

2021년 6월 30일

difficult for a beginner.

교육 기관: Zach S

2021년 5월 22일

As with every IBM course, they tell you "not to hard code" but every project/practical exercise from IBM is littered with hard code. To the point where the projects are unable to be completed, without the help from one or two forum posts from a random student who has spent the time to find a solution. This is a growing problem with IBM's courses. I've learned more from other students, finding workarounds for your mess, than I have from the actual course work. Also, the content for this course, and any examples of code, was produced in Jupyter Notebooks. You didn't even create content in your own IDE, IBM Watson Studio, which says everything a student needs to know about IBM products.