Machine Learning
Machine learning (ML) is a subset of artificial intelligence. It focuses on the study of computer algorithms that improve automatically on their own, through experience. These algorithms are used in a large variety of different applications, from computer vision to mathematical optimizations. ML algorithms are necessary when it is difficult or not feasible to develop conventional algorithms to perform a necessary task.
The four main types of ML include:
- Supervised machine learning algorithms that can apply what has already been learned in previous situations to new data using labeled examples to predict possible future events.
- Unsupervised machine learning algorithms are used when the information used to train the model is neither classified nor labeled. Unsupervised learning can infer a function to describe a hidden structure from unlabeled data. Likewise, the system isn’t intended to figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
- Semi-supervised machine learning algorithms fall in between supervised and unsupervised learning. They use both labeled and unlabeled data for training, skewing towards unlabeled data. Semi- supervised learning is selected when the acquired labeled data requires qualified and appropriate resources to train/learn from that. Otherwise, unlabeled data acquisition typically needs no additional capital.
- Reinforcement machine learning algorithms is a learning method that produces actions and discovers errors through its environment. This method allows the algorithm to automatically understand behavior within a specific context in order to maximize its performance. Reward feedback is needed for the agent to understand which action is best, and this is known as a reinforcement signal.
