In October 2023, the Government of Bihar in northern India published the preliminary findings from its first state-wide caste survey and released a full report a month later. The findings from the survey led to immediate revisions in state-level reservation policy. At first glance, the recent case of Bihar offers an alternative, and more positive, ending to this book—which follows the case of a failed nationwide enumeration of caste in India. I argue in this book that the executive bureaucracy protected the invisibility of caste privilege when it blocked the inclusion of a caste-wise enumeration in Census 2011—a process I call bureaucratic deflection. The recent survey in Bihar actually supports this book's argument since one of the executive bureaucracy's most successful strategies to prevent a caste-wise enumeration in the decennial census has been to decentralize the project of enumerating caste to state governments. Bihar's political leadership undertook the survey after the central government failed to publish caste-wise data.

Building off the success of Bihar's caste survey, the opposition Congress party seeks to prevent the third successive term of the Bharatiya Janata Party (BJP) in office at the center with a national policy platform that includes a caste census during the 2024 parliamentary election. On the one hand, it feels disingenuous that Congress has chosen to rally around this issue—given its long history of excluding a caste-wise enumeration in the decennial censuses of independent India. The Congress leadership had every opportunity to collect caste-wise data in the Censuses of 1951, 1961, 1971, 1991, and 2011. In fact, this book details how the Congress leadership conceded to do so in the lead up to Census 2011 only to backtrack on its promise and push the project into a survey with a long history of producing poor quality data. The executive leadership—which switched from Congress to BJP in 2014—never published the caste-wise data collected as part of a revamped below poverty line (BPL) survey. Both dominant political parties have refused to collect caste-wise data once in power at the center and have supported the senior bureaucracy's seeming disdain for a caste-wise enumeration in the decennial census. In this policy area, they share a common history of using the promise of a caste census to secure votes but never implementing one.

10.1 Support Vector Machines

Support vector machines (SVMs) are supervised machine learning (ML) models used to solve regression and classification problems. However, it is widely used for solving classification problems. The main goal of SVM is to segregate the n-dimensional space into labels or classes by defining a decision boundary or hyperplanes. In this chapter, we shall explore SVM for solving classification problems.

10.1.1 SVM Working Principle

SVM Working Principle | Parteek Bhatia, https://youtu.be/UhzBKrIKPyE

To understand the working principle of the SVM classifier, we will take a standard ML problem where we want a machine to distinguish between a peach and an apple based on their size and color.

Let us suppose the size of the fruit is represented on the X-axis and the color of the fruit is on the Y-axis. The distribution of the dataset of apple and peach is shown in Figure 10.1.

To classify it, we must provide the machine with some sample stock of fruits and label each of the fruits in the stock as an “apple” or “peach”. For example, we have a labeled dataset of some 100 fruits with corresponding labels, i.e., “apple” or “peach”. When this data is fed into a machine, it will analyze these fruits and train itself. Once the training is completed, if some new fruit comes into the stock, the machine will classify whether it is an “apple” or a “peach”.

Most of the traditional ML algorithms would learn by observing the perfect apples and perfect peaches in the stock, i.e., they will train themselves by observing the ideal apples of stock (apples which are very much like apples in terms of their size and color) and the perfect peaches of stock (peaches which are very much like peaches in terms of their size and color). These standard samples are likely to be found in the heart of stock. The heart of the stock is shown in Figure 10.2.

1.1 Introduction

In today’s data-driven world, information flows through the digital landscape like an untapped river of potential. Within this vast data stream lies the key to unlocking a new era of discovery and innovation. Machine learning (ML), a revolutionary field, acts as the gateway to this wealth of opportunities. With its ability to uncover patterns, make predictive insights, and adapt to evolving information, ML has transformed industries, redefined technology, and opened the door to limitless possibilities. This book is your gateway to the fascinating realm of ML—a journey that empowers you to harness the power of data, enabling you to build intelligent systems, make informed decisions, and explore the boundless possibilities of the digital age.

ML has emerged as the dominant approach for solving problems in the modern world, and its wide-ranging applications have made it an integral part of our lives. Right from search engines to social networking sites, everything is powered by ML algorithms. Your favorite search engine uses ML algorithms to get you the appropriate search results. Smart home assistants like Alexa and Siri use ML to serve us better. The influence of ML in our day-to-day activities is so much that we cannot even realize it. Online shopping sites like Amazon, Flipkart, and Myntra use ML to recommend products. Facebook is using ML to display our feed. Netflix and YouTube are using ML to recommend videos based on our interests.

Data is growing exponentially with the Internet and smartphones, and ML has just made this data more usable and meaningful. Social media, entertainment, travel, mining, medicine, bioinformatics, or any field you could name uses ML in some form.

To understand the role of ML in the modern world, let us first discuss the applications of ML.

In a small, rectangular dimly lit room, Khatun Shaikh, a female qazi (Islamic judge) in a women's sharia court, lent a patient ear to women who approached her with complaints of marital discord and violence. The sharia court is an alternative dispute resolution forum run by members of the Bharatiya Muslim Mahila Andolan (Indian Muslim Women's Movement, henceforth BMMA), a social movement led by Muslim women aimed at achieving equality and justice in the adjudication of Muslim family law in India. These alternative forums were frequented by women from poor neighbourhoods in Mumbai who did not have the wherewithal to access the formal justice system. As cases of marriage, divorce, maintenance and domestic violence were discussed and debated in these forums, quarrels broke out between the spouses and their relatives. Allegations of abuse and counter-allegations flew thick and fast. In the midst of these heated exchanges between spouses, Shaikh often emphasised the importance of raham (compassion) as an everyday, lived ethical ideal that both the spouses ought to practice. While the disputes revolved around women claiming specific rights during and after the breakdown of their marriage, Shaikh insisted on how both men and women needed to be compassionate. According to Shaikh, one could display compassion in moments of crisis in the marriage by avoiding the use of harsh words, refraining from overt displays of anger and addressing each other respectfully. This practice of compassion thus entailed using the body in specific ways while claiming one's rights. Shaikh construed compassion as a lived ideal that resonated with the teachings of the Quran and the life of the Prophet. The pursuit of this ideal was closely tethered to the realisation of equality (barabari) and justice (insaf) in the domain of the family.

The sharia court emerged as a space of self-making for both the activists of the BMMA and the women visiting the court. Women spoke their mind. They spoke about the violence and injustice in the family. Interactions between activists, lawyers and the women who visited these forums helped in creating a supportive community space for women who faced injustice in their marital homes. On some days, the court room also doubled as a space where activists of the BMMA conducted training sessions on Muslim family law, the Quran and the Constitution for women of the neighbourhood.

16.1 Introduction to Artificial Neural Network

Neural networks and deep learning are the buzzwords in modern-day computer science. And, if you think that these are the latest entrants in this field, you probably have a misconception. Neural networks have been around for quite some time, and they have only started picking up now, putting up a huge positive impact on computer science.

Artificial neural network (ANN) was invented in the 1960s and 1970s. It became a part of common tech talks, and people started thinking that this machine learning (ML) technique would solve all the complex problems that were challenging the researchers during that time. But sooner, the hopes and expectations died off over the next decade.

The decline could not be attributed to some loopholes in neural networks, but the major reason for the decline was the “technology” itself. The technology back then was not up to the right standard to facilitate neural networks as they needed a lot of data for training and huge computation resources for building the model. During that time, both data and computing power were scarce. Hence, the resulting neural network remained only on paper rather than taking centerstage of the machine to solve some real-world problems.

Later on, at the beginning of the 21st century, we saw a lot of improvements in storage techniques resulting in reduced cost per gigabyte of storage. Humanity witnessed a huge rise in big data due to the Internet boom and smartphones.