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Produktbild: Machine Learning Big Data

Machine Learning Big Data Concepts, Algorithms, Tools and Applications

264,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

01.01.1900

Herausgeber

Uma N. Dulhare + weitere

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

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

01.01.1900

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 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