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In today’s world, businesses are moving towards the implementation of automatic intelligence for decision making. This is only possible with the help of a well-known intelligence technique otherwise known as Artificial Intelligence (AI). This intelligence technique also plays a vital role in the field of research, which is nothing but taking decisions instantly. The dimension of AI is bifurcated into sub-domains such as Machine Learning (ML) and Artificial Neural Networks (ANN) [1]. The term ML is also termed as augmented analytics [2] and depicts the development of machine’s performances. This is achieved through the previous experiences obtained by the machines, but the traditional learning (i.e. the intelligence used in the mid-1800s) works not so efficiently if compared with the ML [3]. In traditional learning, the user deals with data and programs as an input attribute and provides the output or results whereas, in the case of ML the user provides the data and output or desired results as an input attribute and produces the program or rules as an output attribute. This means that data is more important rather than the programs. This is so because the business world depends on the accuracy level of the program which is used for decision making. The block diagram of Traditional learning is shown below in Figure 1.1 for easy understanding.

Traditional Learning is a manual process whereas the functioning of ML is an automated one. Due to ML, the accuracy of analytic worthiness is increased in different diversified domains. These domains are utilized for the preparation of data (raw facts and figures), Outlier Detection (Automatic), Natural Language Interfaces (NLI), and Recommendations, etc. [4]. Due to these domains, the bias factor for taking decisions on a business problem is decreased.

Figure 1.1 Traditional learning.

Figure 1.2 Machine learning.

ML is a sub-group of AI and its primary work is allowing systems to learn automatically with the help of data or observations obtained from the environment through different devices [5]. The block-diagram of ML is shown below in Figure 1.2.

ML-based algorithms perform predictions as well as decisions by using mathematical models that are based on some training data [6–8]. Few popular implementations of Machine Learning are Filtering of E-mails [9], Medical Diagnosis [10], Classification [11], Extraction [12], etc. ML works for the growth of the accuracy level of the computer programs. This was done by accessing data from the surrounding, learn the data automatically, and enhancing the capacity of decision making. The main objective of ML is to minimize human intervention and assistance while performing any task. The next section of this chapter highlights the process of learning along with its different methodologies.