IoT stands for Internet of Things
Machine Learning is a computer science field which aims to study statistical algorithms and models that allows software to perform tasks without being explicitly programmed. Contrary to classical software where all instructions must be provided, machine learning algorithms construct (train) statistical models based on sample data (so-called training data). The statistical model is used to compute outcomes for new data samples.
Machine Learning is a branch of artificial intelligence. Subfields of machine learning include:
The process of building a statistical model based on data which contains an input feature vector (vector of numbers) and the desired output value (target column). The data used for model construction is known as training data. Each data sample from training data should have value for the target column. Trained model can be used to infer (predict) outputs for new data samples which were not used for training - the new data is so-called testing data.
The examples of supervised learning are:
The some examples of supervised algorithms:
In the case of supervised learning all samples have values for target column. However, in some situations obtaining all target values is impossible. In such cases, semi-supervised methods can be applied - it is technique of model training with missing target values.
The unsupervised learning describes types of algorithms that are searching for structure in the data without any guidance (supervisory target column). It is used in cluster analysis to define related subsets of samples in the data - clusters.
Reinforcement learning is a task of learning agents to take such actions which maximize the cumulative reward. The learning process is made by attempting many times to complete the process by the agent. The steps with undesired outcomes are penalized whereas actions with positive outcomes are rewarded.
Examples of machine learning use cases: