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Exploratory Data Analysis for Machine Learning

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IntermediateCourse

Learn to prepare data for Machine Learning and AI with this course. Understand data sources, feature selection, feature engineering, scaling and more. Suitable for aspiring data scientists with basic programming and math skills.

4.6 (2k+ Reviews)

Language

  • English

Topic

  • Machine Learning

Enrollment Count

  • 157.56K

Skills You Will Learn

  • Data Analysis, Machine Learning, Feature Engineering, Business Analysis, Exploratory Data Analysis

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 6 weeks

Platform

  • Coursera

Last Update

  • June 15, 2025
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.

Instructors

Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Svitlana Kramar

Data Scientist

I’m a passionate data science educator whose goal is to learn by teaching innovative data science tools that can improve our day-to-day tasks and our quality of life. My interests are in Natural Language Processing: text classification, summarization, and generation. Research can take a long time because there are a lot of resources and new opinions posted every day. Having tools to summarize and extract the information can save a lot of time. I hope we can all learn, approve, and apply the data science tools to cut down on the repetitive and tedious tasks, to make more informed decisions in life, to differentiate fake from real, and to open communication spaces to language-diverse or hearing-impaired audiences. The applications are limitless! My personality: I am a foodie and I love cooking and learning different cuisines. I also love travelling and connecting with people by learning a little bit of their language, about their food and music. I hold Data Science and Analytics master’s degree, specializing in Machine Learning, from University of Calgary.

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