Fundamentals and Methods of Machine and Deep Learning (eBook, ePUB)

Algorithms, Tools, and Applications
Redaktion: Singh, Pradeep

• Format: ePub

Produktbeschreibung

FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. Audience Researchers and engineers in artificial intelligence, computer scientists as well as software developers.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 480
• Erscheinungstermin: 1. Februar 2022
• Englisch
• ISBN-13: 9781119821885
• Artikelnr.: 63445865

Autorenporträt

Pradeep Singh PhD, is an assistant professor in the Department of Computer Science Engineering, National Institute of Technology, Raipur, India. His current research interests include machine learning, deep learning, evolutionary computing, empirical studies on software quality, and software fault prediction models. He has more than 15 years of teaching experience with many publications in reputed international journals, conferences, and book chapters.

Inhaltsangabe

Preface xix

1 Supervised Machine Learning: Algorithms and Applications 1
Shruthi H. Shetty, Sumiksha Shetty, Chandra Singh and Ashwath Rao

1.1 History 2

1.2 Introduction 2

1.3 Supervised Learning 4

1.4 Linear Regression (LR) 5

1.4.1 Learning Model 6

1.4.2 Predictions With Linear Regression 7

1.5 Logistic Regression 8

1.6 Support Vector Machine (SVM) 9

1.7 Decision Tree 11

1.8 Machine Learning Applications in Daily Life 12

1.8.1 Traffic Alerts (Maps) 12

1.8.2 Social Media (Facebook) 13

1.8.3 Transportation and Commuting (Uber) 13

1.8.4 Products Recommendations 13

1.8.5 Virtual Personal Assistants 13

1.8.6 Self-Driving Cars 14

1.8.7 Google Translate 14

1.8.8 Online Video Streaming (Netflix) 14

1.8.9 Fraud Detection 14

1.9 Conclusion 15

References 15

2 Zonotic Diseases Detection Using Ensemble Machine Learning Algorithms 17
Bhargavi K.

2.1 Introduction 18

2.2 Bayes Optimal Classifier 19

2.3 Bootstrap Aggregating (Bagging) 21

2.4 Bayesian Model Averaging (BMA) 22

2.5 Bayesian Classifier Combination (BCC) 24

2.6 Bucket of Models 26

2.7 Stacking 27

2.8 Efficiency Analysis 29

2.9 Conclusion 30

References 30

3 Model Evaluation 33
Ravi Shekhar Tiwari

3.1 Introduction 34

3.2 Model Evaluation 34

3.2.1 Assumptions 36

3.2.2 Residual 36

3.2.3 Error Sum of Squares (Sse) 37

3.2.4 Regression Sum of Squares (Ssr) 37

3.2.5 Total Sum of Squares (Ssto) 37

3.3 Metric Used in Regression Model 38

3.3.1 Mean Absolute Error (Mae) 38

3.3.2 Mean Square Error (Mse) 39

3.3.3 Root Mean Square Error (Rmse) 41

3.3.4 Root Mean Square Logarithm Error (Rmsle) 42

3.3.5 R-Square (R²) 45

3.3.5.1 Problem With R-Square (R²) 46

3.3.6 Adjusted R-Square (R²) 46

3.3.7 Variance 47

3.3.8 AIC 48

3.3.9 BIC 49

3.3.10 ACP, Press, and R²-Predicted 49

3.3.11 Solved Examples 51

3.4 Confusion Metrics 52

3.4.1 How to Interpret the Confusion Metric? 53

3.4.2 Accuracy 55

3.4.2.1 Why Do We Need the Other Metric Along With Accuracy? 56

3.4.3 True Positive Rate (TPR) 56

3.4.4 False Negative Rate (FNR) 57

3.4.5 True Negative Rate (TNR) 57

3.4.6 False Positive Rate (FPR) 58

3.4.7 Precision 58

3.4.8 Recall 59

3.4.9 Recall-Precision Trade-Off 60

3.4.10 F1-Score 61

3.4.11 F-Beta Sore 61

3.4.12 Thresholding 63

3.4.13 AUC - ROC 64

3.4.14 AUC - PRC 65

3.4.15 Derived Metric From Recall, Precision, and F1-Score 67

3.4.16 Solved Examples 68

3.5 Correlation 70

3.5.1 Pearson Correlation 70

3.5.2 Spearman Correlation 71

3.5.3 Kendall's Rank Correlation 73

3.5.4 Distance Correlation 74

3.5.5 Biweight Mid-Correlation 75

3.5.6 Gamma Correlation 76

3.5.7 Point Biserial Correlation 77

3.5.8 Biserial Correlation 78

3.5.9 Partial Correlation 78

3.6 Natural Language Processing (NLP) 78

3.6.1 N-Gram 79

3.6.2 BELU Score 79

3.6.2.1 BELU Score With N-Gram 80

3.6.3 Cosine Similarity 81

3.6.4 Jaccard Index 83

3.6.5 ROUGE 84

3.6.6 NIST 85

3.6.7 S