Machine learning

Machine learning explained

Machine learning is study of computer algorithms that provide systems the ability to automatically learn and improve from experience. It focuses on development of computer softwares which can access the data and utilise it for next development or advancement. It is seen as a subset of Artificial intelligence.

Machine learning is used on different kind of applications such as email filtering and computer visions. Machine is closely related to computational statistics and on Data Science. The process of machine learninrg starts with observation and analysis of data. The main aim for it is to allow computers to learn to find something valuable from data for further development of the program.

B. Machine learning approaches

1. Supervised learning

Both the input as well as output of the algorithm is provided in this kind of machine learning. It can apply what has learned before to new information to predict future events

2. Unsupervised learning

It involves algorithm that learn on unlabeled data. The algorithm scans data for some kind of connection. It explores data and describe hidden systems from unlabeled data.

3. Semi- supervised learning

It falls between supervised and unsupervised machine learning. Since it uses both labeled and unlabelled data.

4. Reinforcement learning

It is a learning method that interacts with environment producing actions. The algorithm decides by its own how to complete a task.

Machine learning is used on variety of applications. Some of the places where it is used are like Human resource Information System, self driving cars. Machine learning allows massive analysis of data so it is specially used on handling a large set of data.

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