Practical Machine Learning for Data Analysis Using Python

Practical Machine Learning for Data Analysis Using Python

1st Edition - June 5, 2020

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  • Author: Abdulhamit Subasi
  • eBook ISBN: 9780128213803
  • Paperback ISBN: 9780128213797

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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 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.

Key Features

  • 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

Table of Contents


    2.1. Data manipulation, Cross validation and Data over fitting
    2.2. Feature Extraction Methods
    2.3. Dimension Reduction/ Feature selection Methods
    2.4. Statistical Features
    2.5. Dimension Reduction using Principle Component Analysis (PCA)

    3.1. Introduction
    3.2. Linear Regression
    3.3. Linear Discriminant Analysis
    3.4. K-Nearest Neighborhood
    3.5. Artificial Neural Networks
    3.6. Naïve Bayes
    3.7. Support Vector Machines
    3.8. Decision Tree Classifiers
    3.9. Random Forest
    3.10. Bagging
    3.11. Boosting
    3.12. Deep Learning
    3.13. Theano
    3.14. Tensorflow
    3.15. Keras
    3.16. K-means Clustering
    3.17. Fuzzy C-Means Clustering
    3.18. Performance Evaluation
    Confusion Matrix
    F-Measure Analysis
    ROC Analysis
    Kappa Statistic

    Healthcare-related Examples
    4.1. EEG Signal Analysis
    4.1.1. Introduction
    4.1.2. Epileptic Seizure Prediction and Detection
    4.1.3. Emotion Recognition
    4.1.4. Automated Classification of Focal and Non-focal Epileptic EEG Signals
    4.2. EMG Signal Analysis
    4.2.1. Introduction
    4.2.2. Diagnosis of Neuromuscular Disorders
    4.2.3. EMG Signals in Prosthesis Control
    4.2.4. EMG Signals in Rehabilitation Robotics
    4.3. ECG Signal Analysis
    4.3.1. Introduction
    4.3.2. Diagnosis of Heart Arrhythmia
    4.4. Microarray Gene Expression Data Classification for cancer detection
    4.5. Breast Cancer Detection
    4.6. Classification of the Cardiotocogram Data for Anticipation of Fetal Risks
    4.7. Diabetes detection
    4.8. Heart Disease detection Non-Healthcare Classification Examples
    4.9. Sensor Based Human Activity Recognition
    4.10. Smartphone-Based Recognition of Human Activities
    4.11. Intrusion Detection
    4.12. Phishing Website Detection
    4.13. Spam E-mail Detection
    4.14. Credit scoring

    5.1. Introduction 
    5.2. Stock market price index return forecasting
    5.3. Inflation Forecasting
    5.4. Wind Speed Forecasting
    5.5. Electrical Load Forecasting
    5.6. Tourism demand forecasting

    6.1. K-Means Clustering
    6.2. Fuzzy C-Means Clustering

Product details

  • No. of pages: 534
  • Language: English
  • Copyright: © Academic Press 2020
  • Published: June 5, 2020
  • Imprint: Academic Press
  • eBook ISBN: 9780128213803
  • Paperback ISBN: 9780128213797

About the Author

Abdulhamit Subasi

Abdulhamit Subasi
Prof. Dr. Abdulhamit Subasi is specialized in Machine Learning, Data mining and Biomedical Signal Processing. Concerning application of machine learning to different fields, he wrote seven book chapters and more than 150 published journal and conference papers. He is also author of the book, “Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques”. He worked at many institutions as an academician and Georgia Institute of Technology, Georgia, USA, as a researcher. He has been awarded with the Queen Effat Award for Excellence in Research, May 2018. Since 2015, he has been working as a Professor of Information Systems at Effat University, Jeddah, Saudi Arabia. He has worked on several projects related to biomedical signal processing and data analysis.

Affiliations and Expertise

Professor of Information Systems at Effat University, Jeddah, Saudi Arabia

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