Chevron Left
Back to Exploratory Data Analysis for Machine Learning

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

4.6
stars
1,649 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.

Filter by:

301 - 325 of 340 Reviews for Exploratory Data Analysis for Machine Learning

By Shubarani S

Mar 23, 2024

Its a fantastic platform

By IOVACCHINI F

Aug 2, 2023

well explained subjects.

By OMAR A A H

Nov 1, 2020

Very well structured

By Pampa D

Apr 18, 2022

Good content.

By Harsh P

Oct 21, 2023

informative

By Agnibha D R

Apr 30, 2023

NIce Course

By PATEL A S

Aug 6, 2023

goog

By Disha R

Apr 15, 2024

wow

By Gert-Jan D

Jul 29, 2022

Potentially this is a great course, but it falls short on a number of points.

* Content is mixed and/or duplicated

* Lab exercises are mostly just demo's. The video's 'explain' no more than you can read in the notebooks.

* The videos show a 'talking head' that is clearly reading the text from a screen. Not very engaging.

* The explanations are not very clear.

It is possible to learn from this course, but then you will have to work on the demo's yourself (deleting the answers first) and read more clear explanations on the topics from other sources.

By Eric J B

Mar 27, 2024

I was disappointed by this course. The initial portions that focused on Exploratory Data Analysis were ok, but I thought more tools and techniques would be explored. As we progressed into hypothesis testing, the content got progressively weaker. It seemed like an attempt to cover some basic material but without the depth to be truly useful.

By Hossam G M

May 27, 2021

The course material should be provided to allow better absorption of the large amount of information presented. some of the topics needs to be discussed further with more examples and concept declaration especially the hypothesis testing section.

By Ashwin R A R

Jun 21, 2023

The videos seem to be outdated. The material is honestly not that engaging. For a beginners course this might be good. Different people have different tastes. The content itself is pretty good I'd say.

By Ivan P

Sep 15, 2022

Non-working labs, a few incorrect sentences, some things are not explained well enough (At least I feel so), at least one duplicated video - it's not bad, but sloppy.

By Alexander S

Jul 14, 2023

Too much focus on "feature engineering", which is high-school level math on the columns. Better if more focus on the statistical concepts and theoretical backgroud.

By Gabriel Y H M

Feb 25, 2021

I liked the course content but I would like a more interactive approach that show us how to do hypothesis testing in python. The teacher just reads the courses.

By Azmine T W

Apr 16, 2022

I think, instructor went too fast in many cases. Some topics needs to be restructured with more real life examples and interpretations.

By Alexander D

Aug 7, 2022

Exam questions are phrased very poorly in a lot of cases and often don't do a good job of assessing what was taught.

By John C B

Jan 3, 2023

Quizzes are too easy and pretty insipid. The course isn't terrible, but it's not something to spend money on.

By Obinna N

Oct 27, 2023

The instructor was not explanatory enough. I suggest that it should be more of teaching than lecturing.

By Simon N

Apr 19, 2021

I do like the course in generall. But some slides, are very text heavy, which i do not prefer.

By Naveen G

Aug 25, 2022

Content is good but teaching can me more better.

So next time please hire a good teacher.

By Busola A

Mar 29, 2022

The videos are not well explanatory enough.

By Rakesh M

Mar 12, 2023

Items not properly explained

By Upendra J

Dec 3, 2022

jjefesf

By Sam R S E

Jan 16, 2024

good