Supervised, Un-Supervised, Semi-Supervised machine and Reinforcement Learning algorithms
What is Supervised Machine Learning?
In Supervised Machine learning, the Machine is given a set of data which already knows how the output should look and have an idea about the relation between the input and out. Supervised learning problems are also categorized as “regression” and “classification” problems. In regression problems machine predict a numeric or continuous variable output where as in classification problems the predicted output is discrete. For example, if the machine is given a dataset of house prices with respect to house size, it can predict an unknown house price. Whereas if some image are labelled as dogs and cats, the machine can learn the relation between them and classify and separate some image as dog or cat. Below image may give you a better understanding-
What is Un-Supervised Machine Learning?
Un-Supervised allows the machine to approach a problem with minimum or no idea about how the output will look like. It can drive structures and relations from the given dataset and can find hidden patterns or grouping information from the data. It is mainly used for clustering, dimensionality reduction, feature learning, density estimation, etc. Example- KMean Clustering.
What is Semi-Supervised Machine Learning?
Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiring unlabeled data generally doesn’t require additional resources. Example: speech recognition.
What is Reinforcement Learning?
Reinforcement learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behaviour within a specific context in order to maximize its performance. It is employed by various software and machines to find the best possible behaviour or path it should take in a specific situation.