Machine Learning and Its Application: A Quick Guide for Beginners

The introduction chapter summarizes various necessary topics on artificial intelligence and machine learning. It introduces the readers with a strong foundation on the basic concepts of advanced usage of artificial intelligence, which led to the beginning of an era of machine learning. Here, the readers will learn different aspects of machine learning concisely which are described in detail in the coming chapters. Firstly, this chapter discusses the dimensions of AI and the reasons for the transition of traditional AI to modern-day machine learning techniques. Secondly, it discusses the benefits of machine learning in day-to-day life. Thirdly, it discusses the machine learning algorithms' main pillars for training and developing prediction models. Finally, this chapter will give a broad outline of this book in a concise chapter-wise description.

This chapter introduces supervised machine learning algorithms. In this chapter, the popular classification algorithms such as decision tree, random forest, knearest neighbor, Naïve Bayes classifier, and support vector machine are described in detail. Each algorithm is defined starting with its overview, followed by an algorithmic framework and a hands-on example. A detailed Python program is given at the end of each algorithm to support the precise understanding of the working behavior of the classifiers. The Python code is executed on a real dataset, which eventually gives the reader in-depth knowledge about the algorithm's applicability.

This chapter introduces the readers to clustering algorithms as a part of unsupervised machine learning algorithms. This chapter describes the state-of-theart clustering algorithms. This chapter gives an elaborative definition of k-mean clustering, hierarchical clustering, and self-organizing map. It also defines the algorithmic framework of each algorithm with hands-on examples with detailed Python codes and outputs.

This chapter introduces the readers to the in-depth knowledge of regression analysis. Regression is a concept used both in statistics and computer science, specifically in machine learning. However, the concept remains unaltered, but the applications. This chapter will learn about two primarily used regression analysis algorithms, linear regression and logistic regression. Here, each of the algorithms will be described in detail, with hands-on application. We will also learn linear and logistic regression in a more elaborative way while demonstrating through Python program on a real-world dataset.

This chapter introduces the readers to a new concept of machine learning, other than supervised and unsupervised learning. This concept is popularly known as reinforcement learning. Reinforcement learning is a kind of machine learning algorithm, where the model learns itself based on surrounding behavior and new technique of rewarding. This chapter will gradually teach the readers about each concept for understanding reinforcement learning in-depth, alongside a basic application with Python. This chapter will look at the concepts to understand why it is getting so much attention these days. This serves as a beginner's guide to reinforcement learning. Reinforcement learning is undoubtedly one of the most visible study areas at the moment, with a promising future ahead of it, and its popularity is growing by the day.

The chapter will introduce the readers to the latest state-of-the-art deep learning algorithms from scratch. Deep learning is a modern field of machine learning capable of understanding the underlining patterns in the data on its own and identifying the nature of the data. This chapter will travel through all the algorithms, from basic neural network structure to advanced neural networks, such as convolution neural networks and recurrent neural networks. It covers artificial neural networks, perceptron learning algorithms, convolution neural networks, recurrent neural networks, long short term memory, and essential concepts such as backpropagation, gradient descent, activation functions, and optimizations. With the hands-on example and Pythonic approach to real-world applications, this chapter will enhance the readers' knowledge of advanced technologies.

The chapter on feature selection techniques deals with most state-of-theart feature selection techniques, which are being used alongside machine learning algorithms. The feature selection is a crucial element for the better performance of any machine learning algorithm. This chapter covers majorly two types of feature selection algorithms, namely, filter-based and evolutionary-based. This chapter covers two kinds of filter-based approaches in the filter-based algorithms, namely, hypothetical testing, such as t-test, z-test, ANOVA and MANOVA, and correlationbased such as Pearson's correlation, Chi-square test, and Spearman's rank correlation. This chapter also explains various methods such as genetic algorithms, particle swarm optimization, and ant colony optimization in evolutionary algorithms. For each of the algorithms, this chapter describes it in detail and the optimized algorithm for performing the feature selection approach.

Until now, we have covered all aspects of machine learning and deep learning models and understood their internal architecture in detail. This chapter familiarizes the readers with the state-of-the-art real-life applications of machine learning and deep learning algorithms. The chapter will cover real-world applications from every corner of the recent advancements, starting from daily usage of face recognition to object detection. Not only that, but this chapter also explains the application of machine learning and deep learning in day-to-day life usage. Amid many applications, this chapter will cover the essential applications covering pattern recognition, video processing, medical imaging, and computational linguistics. The chapter presents the Python implementation of all the applications. This chapter also mentions some of the other critical real-world applications used in our daily life.