The video tutorial explains the process of encoding categorical data into numerical form, essential for machine learning algorithms. It introduces label encoding and one hot encoding, using the Sklearn library in Python. The tutorial demonstrates label encoding on gender data, converting 'female' and 'male' into 0 and 1, respectively. It also covers one hot encoding for geographical data, transforming countries into numerical arrays. Practical examples in Jupyter Notebook illustrate these encoding techniques.

What is the purpose of encoding categorical data in machine learning?

• To make data more readable

• To convert data into a format suitable for algorithms

• To increase data storage

• To enhance data security

In label encoding, what numerical value is typically assigned to the label 'female'?

• 2

• 3

• 1

• 0

Which library is highlighted for its robust tools in machine learning, including encoding?

• TensorFlow

• Sklearn

• Pandas

• NumPy

What function is used in Sklearn's label encoder to transform string values to numerical values?

• map_values

• transform_fit

• encode_values

• fit_transform

How does one-hot encoding differ from label encoding?

• It is used only for numerical data

• It represents each label with an array of integers

• It uses a single integer for each label

• It is less efficient than label encoding