FUNDAMENTALS OF MACHINE LEARNING TECHNIQUES

Name: FUNDAMENTALS OF MACHINE LEARNING TECHNIQUES
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Mr. Dayakar Babu Kancherla · Ishita Arora · Maher Ali Rusho · Tasriqul Islam

Jan 2024 · Xoffencerpublication

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252

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About this ebook

Machine learning is a subfield of computing science that evolved both from the knowledge obtained through the process of learning how to classify data based on that understanding and also from the understanding gained through the process of learning the computational-based concepts of Artificial Intelligence, or AI. Machine learning, also known as ML, is a common abbreviation for the field. To put it another way, machine learning is the process of training computers to learn on their own via their interactions with data without being explicitly taught to do so. This is accomplished through the use of artificial neural networks. Both humans and animals may claim to be the first to conceptualize what we now call learning. There are a lot of similarities to be discovered between the way that machines learn and the way animals learn. In point of fact, many of the methods that are now used in machine learning were first created to imitate the foundations of animal and human learning using computer representations. This was done to further the field of artificial intelligence. The basic scientific concept of habituation, for instance, outlines the process by which an animal progressively ceases reacting to a stimulus that has been repeatedly shown to the animal. If a dog is taught to perform a range of tasks, such as rolling over, sitting, picking up objects, etc., it is considered to be an outstanding example of animal learning since it is capable of considerable learning if it is trained to do so. If a dog is taught to execute a number of tasks, such as rolling over, sitting, picking up items, etc., it is considered to be an excellent example of animal learning. Many people believe that dogs are the best representatives of animal intelligence. As opposed to the preceding example of successful learning, there aren't many real world applications of machine learning that we can point to as evidence that it's a helpful notion in the current world. This is in contrast to the earlier demonstration of successful learning. Virtual personal assistants, traffic predictions using GPS navigation, surveillance of multiple cameras by AI to detect crime or unusual behavior of people, social media uses ML for face recognition and news feed personalization, search engine result refinement, e-mail spam filtering where a machine memorize all the previously labeled spam e-mails by the user, and a lot more applications are just some of the many places where ML is widely used. Other applications include: a lot more applications. By using all of these applications, it has become abundantly evident that making use of knowledge and experience that one already has will result in a more efficient learning process. The close link that ML has to computational statistics, which also plays a vital role, makes the process of making predictions more simpler and more straightforward. Everyone is entitled to wonder "why does a machine need to learn something?" and there is no wrong answer to this question. There are just a few compelling arguments in favor of the need of machine learning. The fact that we just said that the development of learning capabilities in robots may help us better understand how animals and people gain information should not come as a surprise to anybody.

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About the author

Mr. Dayakar Babu Kancherla is a Technology Leader and currently works as an Engineering Manager from Plano, Texas. He has vast experience in technology including but not limited to System Design, Cloud, DevOps, Site Reliability Engineering, IT Operations, and Security Ops. He is currently working on Digitizing health and pharmacy experiences for one of the major retail chains in the US and Canada. He has multiple patents published in the field of Digitizing health, AI/ML, and Data Science. He has about more than a decade of experience mentoring engineers, and researchers and has been a judge in technical hackathons. He has been an IEEE senior member and published international papers in the field of health diagnosis, Data analytics, Generative AI, and Machine Learning.

Ishita Arora received B.Tech degree (86.50%) in Electronics and Communication Engineering from Guru Gobind Singh Indraprastha University, New Delhi. She was a Gold medal holder (96%) in M.Tech Degree (Digital Communication) from NSUT East Campus (formerly Ambedkar Institute of Advanced Communication Technologies & Research). She’s pursuing her Doctoral degree from NSUT East Campus. Presently she is working as an Assistant Professor in ADGITM (Dr Akhilesh Das Gupta Institute of Technology and Management), New Delhi. She has qualified for both the GATE and UGC NET examinations. Her research areas include Machine Learning, Image processing, Digital communication, Digital Signal processing, etc. She is the author of 8 papers published in International and National conference proceedings and of various other referred journals such as Multimedia Tools and Applications (Impact factor:3.60).

Maher Ali Rusho is a dedicated distance-learning advocate from Bangladesh. He is currently studying as a specialized program grad student of Lockheed Martin Performance Based Masters Of Engineering In Engineering Management (ME-EM) Degree Program, At the University Of Colorado, Boulder. In parallel, Maher is actively engaged in a Full Stack Data Science Bootcamp (Batch: 2022-2023) and a year-long internship with PWSkills and ineuron, contributing to his hands-on expertise. He holds an honorary fellowship in Information Technology (IT) with the International Academic and Management Association (IAMA-India). Maher's passion for data science has been evident since childhood, as he actively participated in international research competitions, Olympiads, and hackathons. This year: 2023, his machine learning-based earthquake detection project earned him recognition at the Genius Olympiad, where he was the sole Bangladeshi global finalist and received an honorable mention award for distinguished presentation. Additionally, Maher was honored with the Best Young Scientist and Best Research Project awards by IAMA-India for the same project, and he secured a renewable scholarship of $14,000 from RIT University, the host institution for the competition Genius Olympiad - 2023.

Tasriqul Islam working as a Researcher at Harvard University, Cambridge, MA, USA. Tasriqul Islam is a distinguished writer and researcher, celebrated for his extensive contributions at the intersection of Artificial Intelligence and Public Policy. His principal area of focus centers on technology and its imminent regulation, particularly within the context of fostering ethical business practices through technological advancements. Mr. Islam boasts a commendable academic background, having attained both a bachelor's and master's degree focused on engineering. Furthermore, he holds an additional master's degree in International Relations, endowing him with a distinct and perceptive vantage point for his literary endeavors. Tasriqul's unwavering commitment to exploring the dynamic relationship between technology and policy positions him as a prominent and influential figure within this specialized field. His substantive contributions undeniably continue to mold the discourse surrounding this pivotal subject matter.

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