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Algorithms Used in a Machine Learning System

Introduction

Machine learning systems, being part of artificial intelligence learn from their experience and get better with time without being explicitly programmed. The systems use algorithms to access and learn patterns and behaviours.

There are three types of machine learning algorithms: supervised machine learning, unsupervised machine learning and reinforced machine learning.

Supervised Machine Learning

This is a type of machine learning where as the system learns, it is also being supervised. In this case, supervision means that the machine is provided with tons of data and an outcome as well. The outcome is referred to as labeled data. Accurately Labeled data gives good outcomes in supervised learning. 

Unsupervised Machine Learning

Unsupervised learning is where a system works with data that is not labelled and no trainer is involved, this allows for more data to be worked on by the program. Here, the algorithms then find the actual nature of the relation between two different data points using unlabeled data allows.

Algorithms perceive the relationship between two data points in a conceptual manner with no human involvement. These hidden structures being formed is what makes unsupervised learning algorithms adaptable.

Unsupervised machine learning algorithms adapt the data by changing the hidden structures, which in turn offers better post-implementation development than supervised learning.

Reinforced Machine Learning

This is a learning method where the algorithms interact with their environment and learns from their experience; the output is usually negative or positive feedback and it gets awarded. For a positive feedback a reward or a punishment for a negative result. The main characteristic of reinforcement machine learning is trial and error, search and delayed reward.

This method allows software or an agent by automation to determine the most suitable behaviour within a certain environment to make the best of its performance.

Topics in Machine Learning

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