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Offered By: IBMSkillsNetwork

Data classification with Naive Bayes

Learn to classify data with Naive Bayes. Dive into this supervised machine learning algorithm that's widely used in text classification scenarios, and learn with a practical application. Navigate through a step-by-step tutorial, emphasizing its performance in spam filtering. Using Pandas and sklearn, master the art of understanding and implementing Naive Bayes for precision and efficiency in data classification.

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Guided Project

Machine Learning

151 Enrolled
4.6
(27 Reviews)

At a Glance

Learn to classify data with Naive Bayes. Dive into this supervised machine learning algorithm that's widely used in text classification scenarios, and learn with a practical application. Navigate through a step-by-step tutorial, emphasizing its performance in spam filtering. Using Pandas and sklearn, master the art of understanding and implementing Naive Bayes for precision and efficiency in data classification.

Start your introduction to data classification with Multinomial Naive Bayes with this hands-on guided project. Master its foundational principles and its application in spam filtering, and develop an understanding of this algorithm's precision and efficiency in text classification. Navigate through this guided project by exploring evaluation techniques with confusion matrices, then extend your knowledge to Gaussian and Bernoulli Naive Bayes.

This hands-on project is based on the Classifying data using the Multinomial Naive Bayes algorithm tutorial. The guided project format combines the instructions of the tutorial with the environment to execute these instructions without the need to download, install, and configure tools. Generated with AI

A look at the project ahead

While completing this project, you:
  • Gain a solid understanding of Naive Bayes concepts in the context of data classification
  • Learn to load and manipulate data sets using essential libraries such as NumPy and Pandas
  • Learn to preprocess the data with natural language processing tasks
  • Get hands-on experience with evaluating your model using sklearn

What you'll need

  • No installation required: Everything is available in the JupyterLab, including any Python libraries and data sets.
  • Some understanding of Python: Having some understanding of Python is required for some preprocessing text tasks using NLTK.
  • Some understanding of statistical concepts: It's helpful to have some understanding of statistic concepts, particularly Naive Bayes. 

Estimated Effort

30 Minutes

Level

Intermediate

Skills You Will Learn

Artificial Intelligence, Machine Learning, NLTK, Pandas, Python, sklearn

Language

English

Course Code

GPXX09X7EN

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