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Classification Methods: Problems and Solutions

BeginnerCourse

This hands-on course will introduce you to the captivating world of classification, where data becomes organized, patterns emerge, and insights are uncovered! By understanding the power of classification, you will be able to predict outcomes based on existing data. You will learn the essential techniques for classifying data into distinct categories using Python libraries including scikit-learn and seaborn. Through practical labs and exercises, you will excel in solving real-world problems, making data-driven decisions, and unlocking valuable insights from data.

4.6 (268 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Industries

  • Insurance, Banking, Retail, Healthcare

Enrollment Count

  • 1.26K

Skills You Will Learn

  • Artificial Intelligence, Data Science, Python, Deep Learning, Machine Learning

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 6 hours

Platform

  • SkillsNetwork

Last Update

  • March 16, 2025
About this Course
Welcome to the world of classification, one of the main types of modelling families in supervised Machine Learning! Through a series of engaging labs, you will delve into the entire classification process, starting from preprocessing your data to training and evaluating models. Additionally, you will learn how to effectively visualize and interpret the results and to handle data sets with unbalanced classes.
 
Classification serves as a critical foundation in data analysis, from categorizing data into their respective classes to training and fine-tuning generative LLMs that can generate new and meaningful content. In this course, different types of classification methods will be covered, showcasing which one is most suitable for a particular use case. 

By the end of this course, you should be able to:
  • Differentiate between the uses and applications of classification and classification ensembles.
  • Utilize logistic regression, KNN, and SVM models. 
  • Use decision tree and tree-ensemble models.
  • Demonstrate proficiency in other ensemble methods for classification.
  • Implement a variety of error metrics to compare the efficiency of various classification models to choose the one that suits your data the best.
  • Employ oversampling and undersampling techniques to handle unbalanced classes in a dataset.

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Enrol now and don't miss the chance to be at the forefront of AI innovation.

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.

Recommended Skills Prior to Taking this Course

To get the most out of this course, you should have familiarity with programming in a Python development environment, as well as a fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

Instructors

Roodra Kanwar

Data Scientist at IBM

I am a data scientist by day, superhero by night. Psych! I wish I was that cool. Only the former part is true which is still pretty cool! I believe in constant learning and it is an essential part of being a productive data enthusiast. I am also pursuing my masters in computer science from Simon Fraser University specializing in Big Data. Moreover, knowledge is transfer learning (pun intended!) and what I have gained, I plan on reflecting it back to the data community.

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Yan Luo

Data Scientist

Yan Luo

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Fateme Akbari

Data Scientist @IBM

I'm a data-driven Ph.D. Candidate at McMaster University and a data scientist at IBM, specializing in machine learning (ML) and natural language processing (NLP). My research focuses on the application of ML in healthcare, and I have a strong record of publications that reflect my commitment to advancing this field. I thrive on tackling complex challenges and developing innovative, ML-based solutions that can make a meaningful impact—not only for humans but for all living beings. Outside of my research, I enjoy exploring nature through trekking and biking, and I love catching ball games.

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Contributors

Artem Arutyunov

Data Scientist

Hey, Artem here! I am excited about answering new challenges with data science, machine learning and especially Reinforcement Learning. Love helping people to learn, and learn myself. Studying Math and Stats at University of Toronto, hit me up if you are from there as well.

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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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