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

Mostrar otras versiones (2)

Including hands-on tools and numerous case studies, this book aims to provide awareness of algorithms used for machine learning and big data in the academic and professional community. --

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Dulhare, Uma N. (Autor), Ahmad, Khaleel (Autor), Bin Ahmad, Khairol Amali (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken : John Wiley & Sons, Inc., 2020.
Temas:
Big data.
Disk access (Computer science)
Données volumineuses.
Accès au disque (Informatique)
Big data
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • Section 1: Theoretical Fundamentals
  • Chapter 1 Mathematical Foundation
  • 1.1 Concept of Linear Algebra
  • 1.1.1 Introduction
  • 1.1.2 Vector Spaces
  • 1.1.3 Linear Combination
  • 1.1.4 Linearly Dependent and Independent Vectors
  • 1.1.5 Linear Span, Basis and Subspace
  • 1.1.6 Linear Transformation (or Linear Map)
  • 1.1.7 Matrix Representation of Linear Transformation
  • 1.1.7.1 Transformation Matrix
  • 1.1.8 Range and Null Space of Linear Transformation
  • 1.1.9 Invertible Linear Transformation
  • 1.2 Eigenvalues, Eigenvectors, and Eigendecomposition of a Matrix
  • 1.2.1 Characteristics Polynomial
  • 1.2.1.1 Some Results on Eigenvalue
  • 1.2.2 Eigendecomposition [11]
  • 1.3 Introduction to Calculus
  • 1.3.1 Function
  • 1.3.2 Limits of Functions
  • 1.3.2.1 Some Properties of Limits
  • 1.3.2.2 1nfinite Limits
  • 1.3.2.3 Limits at Infinity
  • 1.3.3 Continuous Functions and Discontinuous Functions
  • 1.3.3.1 Discontinuous Functions
  • 1.3.3.2 Properties of Continuous Function
  • 1.3.4 Differentiation
  • References
  • Chapter 2 Theory of Probability
  • 2.1 Introduction
  • 2.1.1 Definition
  • 2.1.1.1 Statistical Definition of Probability
  • 2.1.1.2 Mathematical Definition of Probability
  • 2.1.2 Some Basic Terms of Probability
  • 2.1.2.1 Trial and Event
  • 2.1.2.2 Exhaustive Events (Exhaustive Cases)
  • 2.1.2.3 Mutually Exclusive Events
  • 2.1.2.4 Equally Likely Events
  • 2.1.2.5 Certain Event or Sure Event
  • 2.1.2.6 Impossible Event or Null Event (.)
  • 2.1.2.7 Sample Space
  • 2.1.2.8 Permutation and Combination
  • 2.1.2.9 Examples
  • 2.2 Independence in Probability
  • 2.2.1 Independent Events
  • 2.2.2 Examples: Solve the Following Problems
  • 2.3 Conditional Probability
  • 2.3.1 Definition
  • 2.3.2 Mutually Independent Events
  • 2.3.3 Examples
  • 2.4 Cumulative Distribution Function
  • 2.4.1 Properties
  • 2.4.2 Example
  • 2.5 Baye's Theorem
  • 2.5.1 Theorem
  • 2.5.1.1 Examples
  • 2.6 Multivariate Gaussian Function
  • 2.6.1 Definition
  • 2.6.1.1 Univariate Gaussian (i.e., One Variable Gaussian)
  • 2.6.1.2 Degenerate Univariate Gaussian
  • 2.6.1.3 Multivariate Gaussian
  • References
  • Chapter 3 Correlation and Regression
  • 3.1 Introduction
  • 3.2 Correlation
  • 3.2.1 Positive Correlation and Negative Correlation
  • 3.2.2 Simple Correlation and Multiple Correlation
  • 3.2.3 Partial Correlation and Total Correlation
  • 3.2.4 Correlation Coefficient
  • 3.3 Regression
  • 3.3.1 Linear Regression
  • 3.3.2 Logistic Regression
  • 3.3.3 Polynomial Regression
  • 3.3.4 Stepwise Regression
  • 3.3.5 Ridge Regression
  • 3.3.6 Lasso Regression
  • 3.3.7 Elastic Net Regression
  • 3.4 Conclusion
  • References
  • Section 2: Big Data and Pattern Recognition
  • Chapter 4 Data Preprocess
  • 4.1 Introduction
  • 4.1.1 Need of Data Preprocessing
  • 4.1.2 Main Tasks in Data Preprocessing
  • 4.2 Data Cleaning
  • 4.2.1 Missing Data
  • 4.2.2 Noisy Data
  • 4.3 Data Integration

Ejemplares similares