Python for Deep Learning - Build Neural Networks in Python - Training and Test Sets: Splitting Data

The video tutorial explains the process of constructing machine learning algorithms that can learn from data and make predictions. It emphasizes the importance of dividing data into training and testing sets, with a recommended split of 80-90% for training. The tutorial demonstrates how to implement this using the train_test_split method from the scikit-learn library in a Jupyter Notebook, ensuring consistent data splitting with a fixed random state.

Describe the process of splitting data into training and testing sets.

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Why is it important to set a random state when splitting data?

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