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Produktbild: Machine Learning and Big Data

Machine Learning and Big Data Concepts, Algorithms, Tools and Applications

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Beschreibung

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...

Beschreibung

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, H2O.
* 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.

Produktdetails

Einband

Gebundene Ausgabe
Erscheinungsdatum

01.09.2020
Herausgeber Uma N. Dulhare + weitere
Verlag John Wiley & Sons Inc

Produktdetails

Einband

Gebundene Ausgabe
Erscheinungsdatum

01.09.2020
Herausgeber
Verlag John Wiley & Sons Inc
Seitenzahl

544
Maße (L/B/H)

22,9/15,2/2,9 cm
Gewicht

875 g
Auflage

1. Auflage
Sprache

Englisch
ISBN

978-1-119-65474-2
Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Machine Learning and Big Data
  • Preface xix
     
    Section 1: Theoretical Fundamentals 1
     
    1 Mathematical Foundation 3
    Afroz and Basharat Hussain
     
    1.1 Concept of Linear Algebra 3
     
    1.1.1 Introduction 3
     
    1.1.2 Vector Spaces 5
     
    1.1.3 Linear Combination 6
     
    1.1.4 Linearly Dependent and Independent Vectors 7
     
    1.1.5 Linear Span, Basis and Subspace 8
     
    1.1.6 Linear Transformation (or Linear Map) 9
     
    1.1.7 Matrix Representation of Linear Transformation 10
     
    1.1.8 Range and Null Space of Linear Transformation 13
     
    1.1.9 Invertible Linear Transformation 15
     
    1.2 Eigenvalues, Eigenvectors, and Eigendecomposition of a Matrix 15
     
    1.2.1 Characteristics Polynomial 16
     
    1.2.1.1 Some Results on Eigenvalue 16
     
    1.2.2 Eigendecomposition 18
     
    1.3 Introduction to Calculus 20
     
    1.3.1 Function 20
     
    1.3.2 Limits of Functions 21
     
    1.3.2.1 Some Properties of Limits 22
     
    1.3.2.2 1nfinite Limits 25
     
    1.3.2.3 Limits at Infinity 26
     
    1.3.3 Continuous Functions and Discontinuous Functions 26
     
    1.3.3.1 Discontinuous Functions 27
     
    1.3.3.2 Properties of Continuous Function 27
     
    1.3.4 Differentiation 28
     
    References 29
     
    2 Theory of Probability 31
    Parvaze Ahmad Dar and Afroz
     
    2.1 Introduction 31
     
    2.1.1 Definition 31
     
    2.1.1.1 Statistical Definition of Probability 31
     
    2.1.1.2 Mathematical Definition of Probability 32
     
    2.1.2 Some Basic Terms of Probability 32
     
    2.1.2.1 Trial and Event 32
     
    2.1.2.2 Exhaustive Events (Exhaustive Cases) 33
     
    2.1.2.3 Mutually Exclusive Events 33
     
    2.1.2.4 Equally Likely Events 33
     
    2.1.2.5 Certain Event or Sure Event 33
     
    2.1.2.6 Impossible Event or Null Event (Õ) 33
     
    2.1.2.7 Sample Space 34
     
    2.1.2.8 Permutation and Combination 34
     
    2.1.2.9 Examples 35
     
    2.2 Independence in Probability 38
     
    2.2.1 Independent Events 38
     
    2.2.2 Examples: Solve the Following Problems 38
     
    2.3 Conditional Probability 41
     
    2.3.1 Definition 41
     
    2.3.2 Mutually Independent Events 42
     
    2.3.3 Examples 42
     
    2.4 Cumulative Distribution Function 43
     
    2.4.1 Properties 44
     
    2.4.2 Example 44
     
    2.5 Baye's Theorem 46
     
    2.5.1 Theorem 46
     
    2.5.1.1 Examples 47
     
    2.6 Multivariate Gaussian Function 50
     
    2.6.1 Definition 50
     
    2.6.1.1 Univariate Gaussian (i.e., One Variable Gaussian) 50
     
    2.6.1.2 Degenerate Univariate Gaussian 51
     
    2.6.1.3 Multivariate Gaussian 51
     
    References 51
     
    3 Correlation and Regression 53
    Mohd. Abdul Haleem Rizwan
     
    3.1 Introduction 53
     
    3.2 Correlation 54
     
    3.2.1 Positive Correlation and Negative Correlation 54
     
    3.2.2 Simple Correlation and Multiple Correlation 54
     
    3.2.3 Partial Correlation and Total Correlation 54
     
    3.2.4 Correlation Coefficient 55
     
    3.3 Regression 57
     
    3.3.1 Linear Regression 64
     
    3.3.2 Logistic Regression 64
     
    3.3.3 Polynomial Regression 65
     
    3.3.4 Stepwise Regression 66
     
    3.3.5 Ridge Regression 67
     
    3.3.6 Lasso Regression 67
     
    3.3.7 Elastic Net Regression 68
     
    3.4 Conclusion 68
     
    References 69
     
    Section 2: Big Data and Pattern Recognition 71
     
    4 Data Preprocess 73
    Md. Sharif Hossen
     
    4.1 Introduction 73
     
    4.1.1 Need of Data Preprocessing 74
     
    4.1.2 Main Tasks in Data Preprocessing 75
     
    4.2 Data Cleaning 77
     
    4.2.1 Missin