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Offered By: IBMSkillsNetwork

Classification Methods: Problems and Solutions

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.

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Course

Artificial Intelligence

1.12k+ Enrolled
4.6
(198 Reviews)

At a Glance

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.

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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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.

Estimated Effort

6 Hours

Level

Beginner

Industries

Banking, Healthcare, Insurance, Retail

Skills You Will Learn

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

Language

English

Course Code

AI0120EN

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