To ensure your Machine Learning and Artificial Intelligence projects thrive, you need two key ingredients: Unstructured and Structured Data. Unstructured Data refers to raw, unprocessed data, while Structured Data is the data that is processed in a form understandable by ML algorithms. Data augmentation involves enriching an existing dataset by adding additional data from internal or external sources, usually through annotation.
Data Augmentation in AI
Data augmentation is a method used in AI to make training datasets bigger and more varied by changing the existing data in different ways. Here’s how computer vision and natural language processing (NLP) use data augmentation:
Computer Vision
1. Image classification
In tasks that involve image classification, data augmentation is a way to create more perspectives of images by rotating, flipping, or scaling them. This broadens the scope of the dataset, which in turn helps the model acquire new discriminatory characteristics.
2. Object detection
In tasks that involve object detection, data augmentation is a way to produce more pictures by randomly cropping, flipping, and scaling the original image. In addition to broadening the dataset, this also aids the model in learning to recognize things from various angles.
Natural Language Processing
1. Text classification
In tasks that require text classification, data augmentation is used to provide more training instances by using strategies like synonym substitution, random word insertion, and random word deletion. This expands the dataset’s size and variety, which improves the model’s ability to categorize text.
2. Sentiment analysis
Data augmentation is used in projects involving sentiment analysis in order to provide more instances for training by using methods such as negation, paraphrase, and text translation. This increases the variety and quantity of the training dataset, facilitating the model’s ability to categorize sentiment effectively.
3. Named entity recognition
By using methods like synonym substitution, character-level modification, and word swapping, data augmentation can be employed in named entity identification tasks to provide more training instances. This expands the dataset’s quantity and variety, which improves the model’s ability to learn and identify named items.
