Statistical Modeling in Machine Learning: Concepts and Applications presents the basic concepts and roles of statistics, exploratory data analysis and machine learning. The various aspects of Machine Learning are discussed along with basics of statistics. Concepts are presented with simple examples and graphical representation for better understanding of techniques. This book takes a holistic approach – putting key concepts together with an in-depth treatise on multi-disciplinary applications of machine learning. New case studies and research problem statements are discussed, which will help researchers in their application areas based on the concepts of statistics and machine learning.
Statistical Modeling in Machine Learning: Concepts and Applications will help statisticians, machine learning practitioners and programmers solving various tasks such as classification, regression, clustering, forecasting, recommending and more.
1. 1. Introduction to statistical modeling in machine learning: a case study - p. 1 2. 2. A technique of data collection: web scraping with python - p. 23 3. 3. Analysis of Covid-19 using machine learning techniques - p. 37 4. 4. Discriminative dictionary learning based on statistical methods - p. 55 5. 5. Artificial intelligence–based uncertainty quantification technique for external flow computational fluid dynamic (CFD) simulations - p. 79 6. 6. Contrast between simple and complex classification algorithms - p. 93 7. 7. Classification model of machine learning for medical data analysis - p. 111 8. 8. Regression tasks for machine learning - p. 133 9. 9. Model selection and regularization - p. 159 10. 10. Data clustering using unsupervised machine learning - p. 179 11. 11. Emotion-based classification through fuzzy entropy-enhanced FCM clustering - p. 205 12. 12. Fundamental optimization methods for machine learning - p. 227 13. 13. Stochastic optimization of industrial grinding operation through data-driven robust optimization - p. 249 14. 14. Dimensionality reduction using PCAs in feature partitioning framework - p. 269 15. 15. Impact of Midday Meal Scheme in primary schools in India using exploratory data analysis and data visualization - p. 287 16. 16. Nonlinear system identification of environmental pollutants using recurrent neural networks and Global Sensitivity Analysis - p. 307 17. 17. Comparative study of automated deep learning techniques for wind time-series forecasting - p. 327 18. Index - p. 357
Statistical Modeling in Machine Learning: Concepts and Applications presents the basic concepts and roles of statistics, exploratory data analysis and machine learning. The various aspects of Machine Learning are discussed along with basics of statistics. Concepts are presented with simple examples and graphical representation for better understanding of techniques. This book takes a holistic approach – putting key concepts together with an in-depth treatise on multi-disciplinary applications of machine learning. New case studies and research problem statements are discussed, which will help researchers in their application areas based on the concepts of statistics and machine learning.
Statistical Modeling in Machine Learning: Concepts and Applications will help statisticians, machine learning practitioners and programmers solving various tasks such as classification, regression, clustering, forecasting, recommending and more.
1. 1. Introduction to statistical modeling in machine learning: a case study - p. 1 2. 2. A technique of data collection: web scraping with python - p. 23 3. 3. Analysis of Covid-19 using machine learning techniques - p. 37 4. 4. Discriminative dictionary learning based on statistical methods - p. 55 5. 5. Artificial intelligence–based uncertainty quantification technique for external flow computational fluid dynamic (CFD) simulations - p. 79 6. 6. Contrast between simple and complex classification algorithms - p. 93 7. 7. Classification model of machine learning for medical data analysis - p. 111 8. 8. Regression tasks for machine learning - p. 133 9. 9. Model selection and regularization - p. 159 10. 10. Data clustering using unsupervised machine learning - p. 179 11. 11. Emotion-based classification through fuzzy entropy-enhanced FCM clustering - p. 205 12. 12. Fundamental optimization methods for machine learning - p. 227 13. 13. Stochastic optimization of industrial grinding operation through data-driven robust optimization - p. 249 14. 14. Dimensionality reduction using PCAs in feature partitioning framework - p. 269 15. 15. Impact of Midday Meal Scheme in primary schools in India using exploratory data analysis and data visualization - p. 287 16. 16. Nonlinear system identification of environmental pollutants using recurrent neural networks and Global Sensitivity Analysis - p. 307 17. 17. Comparative study of automated deep learning techniques for wind time-series forecasting - p. 327 18. Index - p. 357
