Training data (also known as training dataset& learning dataset) is material with whom we feed our computer in process of Machine Learning in order to computer learn how to properly process the given information and save it for further or later processes. The Machine Learning process is based on algorithms that should mimic the abilities of the human brain. Algorithm hygiene is an imperative for this and any kind of processing, which is dependable on the algorithm processing. That implies detailed research for valuable data related to a desirable subject or sector, unbias it, and monitoring the machine learning process from start to end in order for the outcome of that process to be trustworthy and eligible for further processing. Training data is an essential part of the process, and errors in any above-mentioned steps can ruin the whole process. Unbiasing the data is maybe the prime part of the process because if you skip it or handle it unproperly, your data outcome is most likely worthless. Unlike the traditional programming data approach, Machine Learning enables the machine to learn from previously done observations, so valuable and trustworthy data is one of the main pillars of the whole process. Let’s simplify the process by dividing it into 3 steps:

1. Select the data and incorporate it as the input data.
2. Tag the selected data for desirable output. The model will transform it into text vectors- data featured represented through numbers.
3. Test your model with feeding it by unseen data, because algorithms are trained to associate feature vectors with tags based on manually tagged copies so it can make predictions when processing the unseen data in the future.

Machine Learning programming is a sophisticated but also complex process as it uses iterative training on each image to eventually recognize subjects, features, and shapes.