machine learning training data

Machine Learning Training Data is a field which explores the development of algorithms that can learn from data and then use that learning to predict outcomes. There are primarily three categories that ML models are divided into that are supervised learning, unsupervised learning and reinforced learning. There are primarily three categories that ML models are divided into:

Supervised Learning
These algorithms are provided data as example inputs and desired outputs. The goal is to generate a function that maps the inputs to outputs with the most optimal settings that result in the highest accuracy.

Unsupervised Learning
There are no desired outputs. The model is programmed to identify its own structure in the given input data.

Reinforcement Learning
The algorithm is given a goal or target condition to meet and it is left to its devices to learn by trial and error. It uses past results to inform itself about both optimal and detrimental paths and charts the best path to the desired endgame result.

In each of these philosophies, the algorithm is designed for a generic learning process and exposed to data or a problem. In essence, the written program only teaches a wholesome approach to the problem and the algorithm learns the best way to solve it.