Machine learning (ML) is defined as a discipline of artificial intelligence (AI) that provides machines the ability to automatically learn from data and past experiences to identify patterns and make predictions with minimal human intervention.
Machine Learning Models being used for Pattern recognition and prediction whatever data being provide to ML Models Each new decision process begins with an initial estimate. Once the input data has been provided then ML algorithm will attempt to determine the type of pattern. The ML algorithm compare its estimate with existing examples or outputs. By quantifying how accurate it believes its initial prediction was, it can assess degree of result.
The algorithm would then assign each of these parameters a weight, depending on its perceived usefulness and relevance.
If the algorithm correctly identified a cat, the weights wouldn’t be adjusted, but if it was incorrect, the parameters used to reach that conclusion would be given lesser weighting.
That way, the model gradually reduces the likelihood of making further mistakes.
Then algorithm analyzes the decision process used to reach its estimate, adjusting for future iterations. By changing the “weights”' assigned to each parameter, it reduces the discrepancies between any provided examples and its own estimates. This continuous process of iterating, evaluating, and optimizing means the final model will produce more-accurate outputs. For example, to train an image recognition system, a data scientist could provide the algorithm with a labeled set of dog and cat pictures. The algorithm would take that input data and begin to distinguish the differences between cats and dogs. These different parameters could include the size and profile of each animal, the differing types of fur, and placement of facial features.