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Supervised Machine Learning: Classification

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IntermediateCourse

This course teaches the basics of Classification in Supervised Machine Learning, with a focus on using best practices for data sets with unbalanced classes. Ideal for aspiring data scientists. Prerequisites: Python, Data Cleaning, EDA, Calculus, Linear Algebra,

4.8 (403 Reviews)

Language

  • English

Topic

  • Machine Learning

Enrollment Count

  • 41.73K

Skills You Will Learn

  • Machine Learning, Machine Learning Algorithms, Probability & Statistics, General Statistics, Theoretical Computer Science

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 5 weeks

Platform

  • Coursera

Last Update

  • April 18, 2025
About this Course
This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes.


By the end of this course you should be able to:

  • Differentiate uses and applications of classification and classification ensembles
  • Describe and use logistic regression models
  • Describe and use decision tree and tree-ensemble models
  • Describe and use other ensemble methods for classification
  • Use a variety of error metrics to compare and select the classification model that best suits your data
  • Use oversampling and undersampling as techniques to handle unbalanced classes in a data set

Who should take this course?


This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Classification techniques 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 Data Cleaning, Exploratory Data Analysis, 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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Yan Luo

Data Scientist

Yan Luo

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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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