Data cleaning (or "data wrangling") is the process of detecting and correcting (or removing) corrupt, inaccurate, irrelevant, or incomplete records from a dataset. It's crucial because dirty data leads to flawed models, incorrect predictions, and poor decisions. It's often the most time-consuming part of an AI project!
A4: Bias occurs when a dataset does not accurately represent the real world, dataset leading to an AI model that makes unfair or inaccurate predictions, particularly for certain groups. For example, if a facial recognition dataset is primarily composed of light-skinned individuals, it might perform poorly on dark-skinned individuals.
Can I make my own datasets?
A5: Absolutely! For many local AI applications (e.g., identifying specific crop diseases in Mohadevpur, recognizing local dialects), creating your own custom dataset is often necessary. This involves careful planning for data collection, consistent labeling, and thorough cleaning.
A6: Dataset augmentation is the process of creating new training examples from existing ones by applying slight modifications. For images, this could be rotating, flipping, or zooming. For text, it might involve synonym replacement. It's cool because it effectively makes your dataset larger and more diverse without collecting new real-world data, improving your model's robustness and reducing overfitting.