Fundamentals and Methods of Machine and Deep Learning. Pradeep Singh
Table of Contents
1 Cover
4 Preface
5 1 Supervised Machine Learning: Algorithms and Applications 1.1 History 1.2 Introduction 1.3 Supervised Learning 1.4 Linear Regression (LR) 1.5 Logistic Regression 1.6 Support Vector Machine (SVM) 1.7 Decision Tree 1.8 Machine Learning Applications in Daily Life 1.9 Conclusion References
6 2 Zonotic Diseases Detection Using Ensemble Machine Learning Algorithms 2.1 Introduction 2.2 Bayes Optimal Classifier 2.3 Bootstrap Aggregating (Bagging) 2.4 Bayesian Model Averaging (BMA) 2.5 Bayesian Classifier Combination (BCC) 2.6 Bucket of Models 2.7 Stacking 2.8 Efficiency Analysis 2.9 Conclusion References
7 3 Model Evaluation 3.1 Introduction 3.2 Model Evaluation 3.3 Metric Used in Regression Model 3.4 Confusion Metrics 3.5 Correlation 3.6 Natural Language Processing (NLP) 3.7 Additional Metrics 3.8 Summary of Metric Derived from Confusion Metric 3.9 Metric Usage 3.10 Pro and Cons of Metrics 3.11 Conclusion References
8 4 Analysis of M-SEIR and LSTM Models for the Prediction of COVID-19 Using RMSLE 4.1 Introduction 4.2 Survey of Models 4.3 Methodology 4.4 Experimental Results 4.5 Conclusion 4.6 Future Work References
9 5 The Significance of Feature Selection Techniques in Machine Learning 5.1 Introduction 5.2 Significance of Pre-Processing 5.3 Machine Learning System 5.4 Feature Extraction Methods 5.5 Feature Selection 5.6 Merits and Demerits of Feature Selection 5.7 Conclusion References
10 6 Use of Machine Learning and Deep Learning in Healthcare—A Review on Disease Prediction System 6.1 Introduction to Healthcare System 6.2 Causes for the Failure of the Healthcare System 6.3 Artificial Intelligence and Healthcare System for Predicting Diseases 6.4 Facts Responsible for Delay in Predicting the Defects 6.5 Pre-Treatment Analysis and Monitoring 6.6 Post-Treatment Analysis and Monitoring 6.7 Application of ML and DL 6.8 Challenges and Future of Healthcare Systems Based on ML and DL 6.9 Conclusion References
11
7 Detection of Diabetic Retinopathy Using Ensemble Learning Techniques
7.1 Introduction
7.2 Related Work
7.3 Methodology
7.4