Introduction to Machine Learning and Its Implementation Techniques

Introduction to Machine Learning and Its Implementation Techniques

Arul Murugan R., Sathiyamoorthi V.
DOI: 10.4018/978-1-6684-6291-1.ch001
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Abstract

Machine learning (ML) is one of the exciting sub-fields of artificial intelligence (AI). The term machine learning is generally stated as the ability to learn without being explicitly programmed. In recent years, machine learning has become one of the thrust areas of research across various business verticals. The technical advancements in the field of big data have provided the ability to gain access over large volumes of diversified data at ease. This massive amount of data can be processed at high speeds in a reasonable amount of time with the help of emerging hardware capabilities. Hence the machine learning algorithms have been the most effective at leveraging all of big data to provide near real-time solutions even for the complex business problems. This chapter aims in giving a solid introduction to various widely adopted machine learning techniques and its applications categorized into supervised, unsupervised, and reinforcement and will serve a simplified guide for the aspiring data and machine learning enthusiasts.
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Developing A Machine Learning Model

As discussed, machine Learning is the field where an agent is said to learn from the experience with respect to some class of tasks and the performance measure P. The task could be answering exams in a particular subject or it could be of diagnosing patients of a specific illness. As shown in the figure 1 given below, it is the subset of Artificial intelligence (AI) where it contains artificial neurons and reacts to the given stimuli whereas machine learning uses statistical techniques for knowledge discovery. Deep learning is the subset of machine learning where it uses artificial neural networks for learning process.

Figure 1.

Taxonomy of Knowledge Discovery

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