Lecture 3 (Part 2) : Implementing One-Hot Encoding in Python

👋 Welcome to Lecture 3 (Part 2): Implementing One-Hot Encoding! 🤖

In this lecture, we'll continue our discussion on converting categorical features into the numerical format, focusing specifically on one-hot encoding. We'll cover the following:

- What is one-hot encoding and why it's useful 🤔🔢
- How to implement one-hot encoding in Python using sci-kit-learn and pandas 🐍📈
- Best practices for using one-hot encoding in machine learning projects 📝🤖

By the end of this lecture, you'll have a clear understanding of how to use one-hot encoding to preprocess categorical data for machine learning projects. Whether you're a beginner or have some experience with machine learning, this lecture will be a useful resource to help you advance your skills.

Check out the code and examples from this lecture, on my Github :

If you have any questions or feedback, please don't hesitate to contact me. I'm always happy to help! 🙌