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Learner reviews & feedback for Exploratory Data Analysis for Machine Learning

4.62,562 reviews

About this 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.

Featured reviews

4.0Reviewed Jul 1, 2023

Well explained concepts and spoke at the right speed. But, some of the hypothesis testing, probability, and Bayesian statistics material could've been explained better with more visuals perhaps.

5.0Reviewed Apr 23, 2024

The course includes hands-on exercises that allows us to apply the learned EDA techniques to real-world data. This practical approach helps solidify my understanding.

5.0Reviewed Feb 25, 2023

This course was amazing. I always assumed that EDA was the challenging part of ML, But in this course I found it so cool. can't wait for the next course.

5.0Reviewed Nov 3, 2022

Very helpful for beginner but must have some basic knowledge on python and other libraries such as sklearn, spicy, pandas, etc,.... Thanks very much!

5.0Reviewed May 25, 2023

The instructor are great to demo and teach what it is. He sounds professional and the notebook are useful and the example are essential with guiding the questions 1 by 1.

4.0Reviewed Feb 16, 2024

It was a very code course, however, it would be nice if the code was available on a notepad while videos played to make things faster. Also, some of the online notebooks were not working.

5.0Reviewed Aug 1, 2021

This course was really good for me because it went into depth on what I believe is the most important part of ML which is the data analysis and preparation.

5.0Reviewed Jun 15, 2025

I found the course very helpful, It taught me how to extract useful information from data by exploring different visualization and feature engineering tricks.

4.0Reviewed Nov 30, 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.

5.0Reviewed Aug 12, 2021

This is by far the best course I've encountered. It has an in-depth explanation of the codes they provide. Smooth and easy to understand language.

5.0Reviewed Jun 9, 2021

Very nice course which explains beautifully about data cleaning and the statistical approach and then statistic model and then it ends with the hypothesis testing.

5.0Reviewed Nov 23, 2021

The course is exceptional and a huge learning opportunity for Exploratory Data Analysis. The final project is the best part of the course and helps to apply the concepts to real life data.