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Machine Learning employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the sud-domains of machine learning, classification, cluster analysis, data mining, database, and visualization. Machine learning is perhaps the hottest thing in Silicon Valley right now, especially deep learning. We have Google's class on Tensor Flow, which teaches you everything you need to know to work in Silicon Valley's top companies. The reason why it is so hot is because it can take over many repetitive, mindless tasks. It'll make doctor better doctors, and lawyers better lawyers and it makes cars drive themselves. For example, when you're booking a taxi, you're shown how much the trip would cost. Or when you're on the trip, you're shown the path the taxi would take to reach your destination. While booking a ride on Uber, you're always told the amount of time the trip would take and how much it would cost. All of that, is Machine Learning! The overall goal of this book "Machine Learning" is to provide a broad understanding of various faces of Machine Learning environment in an integrated manner. It covers the syllabi of all technical universities in India and aboard. The first edition of this book is also been awarded by AICTE and placed in AICTE's latest Model Curriculum in Engineering & Technology as well as Emerging Technology.
The book investigates the fundamental theories and practical applications of machine learning across various disciplines. It draws upon mathematics, statistics, information science, and computer science, with a particular focus on sub-domains like classification, cluster analysis, data mining, and visualization. The text aims to provide a comprehensive understanding of the machine learning environment, covering syllabi from technical universities globally and acknowledging its current prominence in Silicon Valley, especially deep learning.
The book is presented as a comprehensive overview of machine learning, designed to align with the curricula of technical universities worldwide. Its content spans foundational theories to practical applications, drawing from diverse fields like statistics and computer science. The text emphasizes the current significance of machine learning, particularly deep learning, in the technology sector and its potential to automate tasks and improve professional efficiency. Its inclusion in AICTE's Model Curriculum suggests a recognized academic value and relevance.
Page Count:
352
Publication Date:
2019-01-01
Publisher:
Khanna Publishing House
ISBN-10:
9386173662
ISBN-13:
9789386173669
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