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Machine Learning and Big Data Concepts, Algorithms, Tools and Applications /
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. --
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken :
John Wiley & Sons, Inc.,
2020.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Dulhare, Uma N., |e author. | |
| 245 | 1 | 0 | |a Machine Learning and Big Data |b Concepts, Algorithms, Tools and Applications / |c Uma N. Dulhare, Khaleel Ahmad, Khairol Amali Bin Ahmad. |
| 264 | 1 | |a Hoboken : |b John Wiley & Sons, Inc., |c 2020. | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 520 | |a 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. -- |c Edited summary from book. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Big data. | |
| 650 | 0 | |a Disk access (Computer science) | |
| 650 | 6 | |a Données volumineuses. | |
| 650 | 6 | |a Accès au disque (Informatique) | |
| 650 | 7 | |a Big data |2 fast | |
| 650 | 7 | |a Disk access (Computer science) |2 fast | |
| 700 | 1 | |a Ahmad, Khaleel, |e author. | |
| 700 | 1 | |a Bin Ahmad, Khairol Amali, |e author. | |
| 758 | |i has work: |a Machine learning and big data (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGT9tRxP4VQ9kcH8QKb7jK |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Dulhare, Uma N. |t Machine Learning and Big Data : Concepts, Algorithms, Tools and Applications |d Newark : John Wiley & Sons, Incorporated,c2020 |z 9781119654742 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=6268187 |z Texto completo |
| 938 | |a Askews and Holts Library Services |b ASKH |n AH37193850 | ||
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL6268187 | ||
| 938 | |a YBP Library Services |b YANK |n 301396749 | ||
| 994 | |a 92 |b IZTAP | ||