book

Practical Machine Learning for Data Analysis Using Python

by Abdulhamit Subasi

June 2020

Beginner to intermediate

534 pages

13h 18m

English

Academic Press

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Abstract1.1. What is machine learning?1.2. Machine learning framework1.3. Performance evaluation1.4. The Python machine learning environment1.5. Summary
Abstract2.1. Introduction2.2. Feature extraction and transformation2.3. Dimension reduction2.4. Clustering for feature extraction and dimension reduction
Abstract3.1. Introduction3.2. What is machine learning?3.3. Python libraries3.4. Learning scenarios3.5. Supervised learning algorithms3.6. Unsupervised learning3.7. Reinforcement learning3.8. Instance-based learning3.9. Summary

Abstract4.1. Introduction4.2. EEG signal analysis4.3. EMG signal analysis4.4. ECG signal analysis4.5. Human activity recognition4.6. Microarray gene expression data classification for cancer detection4.7. Breast cancer detection4.8. Classification of the cardiotocogram data for anticipation of fetal risks4.9. Diabetes detection4.10. Heart disease detection4.11. Diagnosis of chronic kidney disease (CKD)4.12. Summary
Abstract5.1. Intrusion detection5.2. Phishing website detection5.3. Spam e-mail detection5.4. Credit scoring5.5. credit card fraud detection5.6. Handwritten digit recognition using CNN5.7. Fashion-MNIST image classification with CNN5.8. CIFAR image classification using CNN5.9. Text classification5.10. Summary
Abstract6.1. Introduction6.2. Stock market price index return forecasting6.3. Inflation forecasting6.4. Electrical load forecasting6.5. Wind speed forecasting6.6. Tourism demand forecasting6.7. House prices prediction6.8. Bike usage prediction6.9. Summary
Abstract7.1. Introduction7.2. Clustering7.3. The k-means clustering algorithm7.4. The k-medoids clustering algorithm7.5. Hierarchical clustering7.6. The fuzzy c-means clustering algorithm7.7. Density-based clustering algorithms7.8. The expectation of maximization for Gaussian mixture model clustering7.9. Bayesian clustering7.10. Silhouette analysis7.11. Image segmentation with clustering7.12. Feature extraction with clustering7.13. Clustering for classification7.14. Summary

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Acknowledgments

First of all, I would like to thank my publisher Elsevier and its team of dedicated professionals who have made this book-writing journey very simple and effortless, as well as all those who have worked in the background to make this book a success.

I would like to thank Sara Pianavilla and Rafael Trombaco for their great support. Also, I would like to thank Paul Prasad Chandramohan for being patient in getting everything necessary to complete this book.

Abdulhamit Subasi