Lecture 3 : Converting Categorical Features into Numerical Format in Python

👋 Welcome to Lecture 3: Converting Categorical Features into Numerical Format! 🤖

In this lecture, we'll explore the process of converting categorical features into the numerical format, which is a crucial step in preparing data for machine learning. Specifically, we'll cover the following:

- The difference between categorical and numerical data 📊🔢
- Techniques for encoding categorical data, such as one-hot encoding and label encoding 📝🔠
- The advantages and disadvantages of different encoding techniques 🤔🤷‍♂️
- Best practices for handling categorical data in a machine learning project 🚀💻
- By the end of this lecture, you'll have a solid understanding of how to convert categorical features into the numerical format, an essential skill for any machine learning practitioner.

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! 🙌