Connectionist AI models require well-prepared data to learn effectively. Data preparation or data preprocessing is the critical phase where we transform raw data into a clean and usable format for training. In simple terms, data preprocessing involves evaluating, filtering, manipulating, and encoding data so that a machine learning algorithm can understand it. This step is vital because, as the saying goes, “if garbage goes in, garbage comes out”, a model’s success depends on the quality of the input data. Below are break down of the key parts of data preparation.
https://lakefs.io/blog/data-preprocessing-in-machine-learning/#:~:text=Data preprocessing is the process,useful for machine learning purposes
https://www.ibm.com/think/topics/data-labeling#:~:text=Data labeling involves identifying raw,them to make accurate predictions
https://milvus.io/ai-quick-reference/how-do-you-preprocess-data-for-a-neural-network
https://www.datacamp.com/tutorial/complete-guide-data-augmentation