Machine Learning (ML)
Definition
In general terms, Machine learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- Arthur Samuel
Machine learning facilitates exposure to new scenarios, testing and adaptation, while employing pattern and trend detection for improved decisions in subsequent (though not identical) situations.
Types of Machine Learning
Machine learning tasks are classified into several broad categories:
- Supervised learning - the algorithm builds a mathematical model from a set of data that contains both the inputs and the desired outputs. the data is known as training data and consists of a set of training examples. Classification and Regression algorithms are examples of supervised learning.
- Unsupervised learning - is a paradigm devised to empower practical intelligence without human dependency for making sense of data without sticking to specific tasks and supervisory signals. The algorithm builds a mathematical model from a set of data that contains only inputs and no desired output labels. Unsupervised learning algorithms are used to find structure in the data, like grouping or clustering of data points.
- Semi-supervised learning - is obviously a combination of both which utilize both labeled and unlabeled data.
- Reinforcement learning - In this the algorithm meets an un-explored situation, then operates in a try-and-error manner to find the solution. Basically the solution is either rewarded or enforced with penalty for the conducted action as a guide to how it should behave. The purpose is to maximize the ultimate reward.
People often misunderstand ML with data mining. Data mining is a field of study within machine learning and focuses on exploratory data analysis through unsupervised learning. In its application across business problems.
Machine Learning in RPA
In the field of robotic process automation (RPA), machine learning allows software robots and AI to learn new processes through pattern recognition rather than needing to be individually and precisely programmed for each new scenario in their Business Process.