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Learner Reviews & Feedback for Exploratory Data Analysis for Machine Learning by IBM

4.6
stars
1,607 ratings

About the Course

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....

Top reviews

AE

Sep 26, 2021

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

Sep 21, 2021

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.

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26 - 50 of 334 Reviews for Exploratory Data Analysis for Machine Learning

By EMANUELE F

Sep 26, 2021

The course touches all the topics that are of interest for the a Machine Learning pratictioneer. I've found the course sometimes oversimplified, that paradoxically made it harder to grasp some concepts, expecially the topics of the Week 2. Overall I've found It to be a good course because at least it gives you the path to follow from where you can study on your own to go deeper in the topics you are interested.

Note: I would suggest to edit the notebooks. It is not a good idea to have the solutions in the same notebook where you should do an exericise, because it makes also the video lectures that came after pretty useless. I suggest to separate the exercises from the solutions, and to put the solutions in the video lectures so you must follow them with some focus to understand what the solution was. Furhtermore i would review the notebooks. Some of them were different from the ones presented in the video lectures which made it a little bit confusing to follow.

By Ashish P

Dec 26, 2020

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!

By priyanka b

Nov 30, 2020

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.

By Sunil G

Jul 1, 2022

The speaker appeared to be a third party - reading off a script, and not the actual course instructor. This aspect made the subject drier that it actually is.

As for the course and the notebooks, it has been done very well.

I would still rate this as a 101 in terms of real depth of experience, but perhaps that is as is expected.

The assignments do not do a regirous test of skills aquired.

By Darish S

Dec 1, 2020

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.

By nakul c

Oct 11, 2021

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

By Dimitrios T

Jun 13, 2022

Poor explanation of many concepts. Felt i the instructor was reading the material in a neutral manner and was not emphasizing on key moments. Also lack hands on opportunities and practice to help understand the concepts.

Overall seemed more like a summary of various titles and definitions.

By Nabeel S

Dec 18, 2021

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

By Ritvik C

Jun 30, 2021

difficult for a beginner.

By Mohammad N

Jun 5, 2023

The instructor was not actually teaching, but just reading from the text. His lack of mastery of the topics was quite evident, especially in hypothesis testing, which was extremely confusing. I advise those who want to take this course to only study the notebooks and search the internet wherever necessary because the videos will not add anything special to you.

By Brinda p

Jul 26, 2022

i purchased whole machine course but after payment i can only able to access 1 course among all 6 and they ask me to pay extra for another 5 course.

By Akshat G

Dec 16, 2023

not worth. just a guy speaking . no practical knowledge by teacher . how can i learn everything in the lab

By Roberto G

Dec 8, 2022

Very poor course structure

By lera m

Sep 10, 2023

Very vague explanations.

By Eman A

Aug 1, 2022

bored

By SMRUTI R D

Jul 26, 2021

Although I had done such data analysis elsewhere in Coursera, this I found very comprehensive and systematic. I wish the topic of statistical significance tests was covered in some detail based on real data, rather random data generated for the purpose. I feel this area should receive more attention from the designers of the course. Thanks for all efforts put in by the faculty and all support person in the background. Thanks a lot..

By Dan M

Feb 13, 2023

As someone with a science background, exploring and visualising data as well as performing hypothesis testing is something I have already done a lot of. This course offered a very useful refresher in these topics, as well as introducing me to a lot of tools that can streamline my work in these areas. The course was very well presented and the coded examples are useful to keep as go-bys for use in future work.

By Hobbesian T

Jun 18, 2022

I am on the IBM machine learning specialization professional certificate track and this course is my first course in the track. It is a very simple course, but it touches on the most important topics before performing any machine learing related work. I highly recommend to complete the machine learning specialization certificate after completing the course.

By ulagaraja j

Jan 20, 2022

Very friendly and extraordinary course for those who are looking for machine learning profession. The Data analysis and other process were well taken throughout the course. The Teaching members are well qualified and understandable so that we can have a clear thought on a particular concept. Finally an awesome course that no one should miss!!!

By Takahide M

Jul 12, 2022

This is the first course where you will learn how to use Jupiter Notebooks. For this purpose, you will learn machine learning concepts and more. It is not designed for beginners to learn. The prerequisite is that you should have some knowledge of mathematics, as some mathematical formulas such as linear algebra will be used in the course.

By Nosaybeh A P

Feb 5, 2022

Thanks Coursera

my life has changed after Corona crisis and founding you!!!

Recommended for beginners as well as for those students, professionals who want to get their hands dirty in the data science life cycle.

Thanks to learning on Coursera , I'm able to add my courses to my Linkedin and resume that make me stand out from my peers.

By Anish K

Jun 6, 2023

I would highly recommend the IBM Machine Learning course to anyone interested in data science and machine learning. The course was well-structured and easy to follow, with plenty of practical examples and hands-on exercises. I appreciated the opportunity to apply what I learned through real-world scenarios and projects.

By Abhinav M

Oct 25, 2020

Peer Review needs some moderation, someone marked all zeros, for one of my assignments. We are doing Machine Learning clearly an algorithm for such can be made available. Overall a great Introduction and hands-on guidance towards the Tools and Statistics involved for various business applications in the real world.

By Mohammad K K

Aug 7, 2022

This course helped me to understand basics of AI /ML, Data Analysis and Hypotheisis Testing. Indepth explanation of some topics were plus point of the couse. Now, I am capable of doing Data Analysis with 100% confidence.

Thank you @Joseph Santarcangelo, @Svitlana (Lana) Kramar (Instructors) and IBM

By Samik B

Jun 5, 2022

This course has to be the best Data analysis course on Coursera. The explanation is to the point. A prior knowledge related to statistics, probability and discrete mathematics is very important, because the instructor assumes that you already know about the same. Superb course altogether!