Data Augmentation in machine learning

• Data Augmentation:-

                                       Data Augmentation is the process of increasing the amount and diversity of data. We do not collect new data, rather we transform the already present data. I will be talking specifically about image data augmentation in this article. So we will look at various ways to transform and augment the image data.

• Need for data augmentation:- 

                                                      Data Augmentation is an integral process in deep learning, as in deep learning we need large amounts of data and in some cases it is not feasible to collect thousands or millions of images, so data augmentation comes to the rescue.
           It helps us to increase the size of the dataset and introduce variability in the dataset.

• Operations in data augmentation:-

                                                              The most commonly used operations are-

(1). Rotate:-  Rotate operation as the name suggest just rotates the image by a certain specified degree.
Example:- the rotation degree as 4*.

(2). Shearing:- shearing is also used for transform the orientation of the image.

(3). Zooming:- it used for zoom out and zoom in the image.

(4). Cropping:- it is used for crop the image.

(5). Flipping:- flip the image.

(6). Changing the brightness level.