In this chapter, the author explained the importance of the R language in machine learning and steps to the installation of R in a different environment like Windows and Linux. The author also describes the basic concepts of R like its syntax, data types, variables, function, operator, etc. with examples in detail. In advanced R, the author explained different charts to plot different data using a barplot function. Using barplot, different graphs like histograms, pie charts can be drawn. The author has also shown how to label the axis of the graph and how to plot a different color. The chapter also consists of some basic R programming examples like a program to make a calculator, checking Armstrong's number, etc. The author also describes the steps and process to install tensor flow.

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In today's world, a huge amount of data is available. So, all the available data are analyzed to get information, and later this data is used to train the machine learning algorithm. Machine learning is a subpart of artificial intelligence where machines are given training with data and the machine predicts the results. Machine learning is being used in healthcare, image processing, marketing, etc. The aim of machine learning is to reduce the work of the programmer by doing complex coding and decreasing human interaction with systems. The machine learns itself from past data and then predict the desired output. This chapter describes machine learning in brief with different machine learning algorithms with examples and about machine learning frameworks such as tensor flow and Keras. The limitations of machine learning and various applications of machine learning are discussed. This chapter also describes how to identify features in machine learning data.

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Humans communicate with each other and express emotions using facial expressions. Facial expression is an important part of expressing emotions. Facial features can be considered as eyes, mouth, and nose. In this chapter, the authors considered these facial features for emotion detection and processed them with convolutional neural network (CNN). There are mainly six basic types of emotions: fear, disgust, anger, sadness, happiness, and surprise. These emotions can be classified into two types: positive emotions and negative emotions. A positive emotion is a feeling where there is no negativity such as happy, neutral. A negative emotion is a feeling of depression, frustration including anger, sadness, fear. This chapter describes a step-by-step method in processing an image in CNN and giving an output. CNN classifies different emotions. Further classification is done for emotion as negative and positive, and when the negative emotion is detected, music is played to change the emotion of a person.