Deep Learning - Deep Neural Network for Beginners Using Python - Final Project Part 1

The video tutorial covers the final project of predicting iris species using a dataset with features like sepal length, sepal width, petal length, and petal width. The project involves multi-class classification with three labels: Iris setosa, Iris versicolor, and Iris virginica. The tutorial explains the need for one hot encoding of labels and demonstrates data import, shuffling, and splitting into training, testing, and validation sets. The next steps involve implementing the project using forward and backpropagation.

What does the term 'feature scaling' refer to in the context of this project?

What libraries are imported for data manipulation and model training in the project?

How is the training data further divided into validation data?

What are the expected outcomes after implementing the project as described?