Machine Learning and Big Data

Concepts, Algorithms, Tools and Applications

Uma N. Dulhare, Khaleel Ahmad, Khairol Amali Bin Ahmad, Uma N. Dulhare, Khaleel Ahmad, Khairol Amali Bin Ahmad

• Publisher: Wiley-Scrivener
• Year: 2020
• Print ISBN: 9781119654742
• eBook ISBN: 9781119654797
• Edition: 1
• Topic: Computer Science
• Subtopic: Programming Algorithms
• Index: Computer Science

This book is intended for academic and industrial developers, exploring and developing applications in the area of big data and machine learning, including those that are solving technology requirements, evaluation of methodology advances and algorithm demonstrations.

The intent of this book is to provide awareness of algorithms used for machine learning and big data in the academic and professional community. The 17 chapters are divided into 5 sections: Theoretical Fundamentals; Big Data and Pattern Recognition; Machine Learning: Algorithms & Applications; Machine Learning's Next Frontier and Hands-On and Case Study. While it dwells on the foundations of machine learning and big data as a part of analytics, it also focuses on contemporary topics for research and development. In this regard, the book covers machine learning algorithms and their modern applications in developing automated systems.

Subjects covered in detail include:

• Mathematical foundations of machine learning with various examples.
• An empirical study of supervised learning algorithms like Naïve Bayes, KNN and semi-supervised learning algorithms viz. S3VM, Graph-Based, Multiview.
• Precise study on unsupervised learning algorithms like GMM, K-mean clustering, Dritchlet process mixture model, X-means and Reinforcement learning algorithm with Q learning, R learning, TD learning, SARSA Learning, and so forth.
• Hands-on machine leaning open source tools viz. Apache Mahout, H 2 O.
• Case studies for readers to analyze the prescribed cases and present their solutions or interpretations with intrusion detection in MANETS using machine learning.
• Showcase on novel user-cases: Implications of Electronic Governance as well as Pragmatic Study of BD/ML technologies for agriculture, healthcare, social media, industry, banking, insurance and so on.

Section 1
THEORETICAL FUNDAMENTALS

1
Mathematical Foundation

Afroz* and Basharat Hussain

Department of Mathematics, Maulana Azad National Urdu University, Hyderabad, India

Abstract

The aim of this chapter is to provide the reader an overview of basics of linear algebra and introductory lecture on calculus. We will discuss concept of real vector spaces, basis, span, and subspaces. The idea of solving the system of equations using matrix approach will be discuss. Linear transformation by means of which we can pass from one vector space to another, inverse linear transformation, and transformation matrix will be explain with detail examples. Definition of eigenvectors, eigenvalues, and eigendecomposition along with thorough examples will be provided. Moreover, definition of function, limit, continuity, and differentiability of function with illustrative examples will be included.

Keywords: Vector spaces, basis, linear transformation, transformation matrix, eigenvalue, eigenvector, eigen decomposition, continuous functions, differentiation

1.1 Concept of Linear Algebra

1.1.1 Introduction

Basics problem of linear algebra is to solve n linear equations in n unknowns.

For example,

The above system is two dimensional (n = 2), i.e., two equations with two unknowns. The solution of the above system is the values of unknowns x, y, satisfying the above linear system. One can easily verify that x = 1, y = 2 satisfy the above linear system.

Geometrically, each of the above equation represents a line in R2-plane. We have two lines in same plane and if they do intersect (it is possible that they may not intersect as parallel line don’t intersect) on s...

Table of contents

• Cover
• Table of Contents
• Title Page
• Copyright Page
• Preface
• Section 1: THEORETICAL FUNDAMENTALS
• Section 2: BIG DATA AND PATTERN RECOGNITION
• Section 3: MACHINE LEARNING: ALGORITHMS & APPLICATIONS
• Section 4: MACHINE LEARNING’S NEXT FRONTIER
• Section 5: HANDS-ON AND CASE STUDY
• Index
• End User License Agreement