Nominal Encoding in Machine Learning Handling Unordered Categories with Code | BrainLab Techies

Welcome to another important video in our Beginners to Advanced Machine Learning series on BrainLab Techies!

In this session, we focus on Nominal Encoding, used for categorical data without any intrinsic order—like colors, cities, product names, etc. You’ll learn the theory behind it and how to apply it using Python and pandas/sklearn.

💡 What you’ll learn in this video:

What is Nominal Data in machine learning?

Why special encoding is needed for unordered categories

Different techniques:

One-Hot Encoding (for nominal data)

Dummy Encoding (with drop-first approach)

How to implement both in Python using pandas.get_dummies() and sklearn

Real-world examples to understand impact on models

📘 This tutorial is a must-watch for data science beginners working with messy categorical data and preparing datasets for machine learning models.

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