Preprocessing refers to all the transformations on the raw data before it is fed to the machine learning or deep learning algorithm. For instance, training a convolutional neural network on raw images will probably lead to bad classification performances (Pal & Sudeep, 2016).

What is meant by preprocessing in machine learning?

Data preprocessing in Machine Learning refers to the technique of preparing (cleaning and organizing) the raw data to make it suitable for a building and training Machine Learning models.

Is preprocessing needed in deep learning?

This is because preprocessing takes about 50–80% of your time in most deep learning projects, and knowing some useful tricks will help you a lot in your projects. We will be using the flowers dataset from Kaggle to demonstrate the key concepts.