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Practical Machine Learning for Data Analysis Using Python
- 1st Edition - June 5, 2020
- Author: Abdulhamit Subasi
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 1 3 7 9 - 7
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 1 3 8 0 - 3
Practical Machine Learning for Data Analysis Using Python is a problem solver’s guide for creating real-world intelligent systems. It provides a comprehensive approach with conc… Read more
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Request a sales quotePractical Machine Learning for Data Analysis Using Python is a problem solver’s guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems.
- Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas
- Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data
- Explores important classification and regression algorithms as well as other machine learning techniques
- Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features
Researchers and graduate students in biomedical engineering, electrical and electronics engineering, computer science, biomedical informatics, as well as professionals in data science and data analytics
1.INTRODUCTION
2. DATA PRE-PROCESSING2.1. Data manipulation, Cross validation and Data over fitting2.2. Feature Extraction Methods2.3. Dimension Reduction/ Feature selection Methods2.4. Statistical Features2.5. Dimension Reduction using Principle Component Analysis (PCA)
3. MACHINE LEARNING TECHNIQUES3.1. Introduction3.2. Linear Regression3.3. Linear Discriminant Analysis3.4. K-Nearest Neighborhood3.5. Artificial Neural Networks3.6. Naïve Bayes3.7. Support Vector Machines3.8. Decision Tree Classifiers3.9. Random Forest3.10. Bagging3.11. Boosting3.12. Deep Learning3.13. Theano3.14. Tensorflow3.15. Keras3.16. K-means Clustering3.17. Fuzzy C-Means Clustering3.18. Performance EvaluationConfusion MatrixF-Measure AnalysisROC AnalysisKappa Statistic
4. CLASSIFICATION EXAMPLESHealthcare-related Examples4.1. EEG Signal Analysis4.1.1. Introduction4.1.2. Epileptic Seizure Prediction and Detection4.1.3. Emotion Recognition4.1.4. Automated Classification of Focal and Non-focal Epileptic EEG Signals4.2. EMG Signal Analysis4.2.1. Introduction4.2.2. Diagnosis of Neuromuscular Disorders4.2.3. EMG Signals in Prosthesis Control4.2.4. EMG Signals in Rehabilitation Robotics4.3. ECG Signal Analysis4.3.1. Introduction4.3.2. Diagnosis of Heart Arrhythmia4.4. Microarray Gene Expression Data Classification for cancer detection4.5. Breast Cancer Detection4.6. Classification of the Cardiotocogram Data for Anticipation of Fetal Risks4.7. Diabetes detection4.8. Heart Disease detection Non-Healthcare Classification Examples4.9. Sensor Based Human Activity Recognition4.10. Smartphone-Based Recognition of Human Activities4.11. Intrusion Detection4.12. Phishing Website Detection4.13. Spam E-mail Detection4.14. Credit scoring
5. REAL WORLD REGRESSION EXAMPLES 5.1. Introduction 5.2. Stock market price index return forecasting5.3. Inflation Forecasting5.4. Wind Speed Forecasting5.5. Electrical Load Forecasting 5.6. Tourism demand forecasting
6. CLUSTERING EXAMPLES6.1. K-Means Clustering6.2. Fuzzy C-Means Clustering
- No. of pages: 534
- Language: English
- Edition: 1
- Published: June 5, 2020
- Imprint: Academic Press
- Paperback ISBN: 9780128213797
- eBook ISBN: 9780128213803
AS
Abdulhamit Subasi
Abdulhamit Subasi is a highly specialized expert in the fields of Artificial Intelligence, Machine Learning, and Biomedical Signal and Image Processing. His extensive expertise in applying machine learning across diverse domains is evident in his numerous contributions, including the authorship of multiple book chapters, as well as the publication of a substantial body of research in esteemed journals and conferences. His career has spanned various prestigious institutions, including the Georgia Institute of Technology in Georgia, USA, where he served as a dedicated researcher. In recognition of his outstanding research contributions, Subasi received the prestigious Queen Effat Award for Excellence in Research in May 2018. His academic journey includes a tenure as a Professor of computer science at Effat University in Jeddah, Saudi Arabia, from 2015 to 2020. Since 2020, he has assumed the role of Professor of medical physics at the Faculty of Medicine, University of Turku in Turku, Finland
Affiliations and expertise
Institute of Biomedicine, Faculty of Medicine, University of Turku, Turku, Finland Department of Computer Science, College of Engineering, Effat University, Jeddah, Saudi ArabiaRead Practical Machine Learning for Data Analysis Using Python on ScienceDirect