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.
Continue readingGuided Project
Machine Learning
65 EnrolledAt 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.
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
- 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.Â