Practical Machine Learning for Data Analysis Using Python

Practical Machine Learning for Data Analysis Using Python

by Abdulhamit Subasi
Released June 2020
Publisher(s): Academic Press
ISBN: 9780128213803

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Book description

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.

  • 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

Table of contents

  1. Cover
  2. Title page
  3. Contents
  4. Copyright
  5. Dedication
  6. Preface
  7. Acknowledgments
  8. Chapter 1: Introduction
    1. Abstract
    2. 1.1. What is machine learning?
    3. 1.2. Machine learning framework
    4. 1.3. Performance evaluation
    5. 1.4. The Python machine learning environment
    6. 1.5. Summary
  9. Chapter 2: Data preprocessing
    1. Abstract
    2. 2.1. Introduction
    3. 2.2. Feature extraction and transformation
    4. 2.3. Dimension reduction
    5. 2.4. Clustering for feature extraction and dimension reduction
  10. Chapter 3: Machine learning techniques
    1. Abstract
    2. 3.1. Introduction
    3. 3.2. What is machine learning?
    4. 3.3. Python libraries
    5. 3.4. Learning scenarios
    6. 3.5. Supervised learning algorithms
    7. 3.6. Unsupervised learning
    8. 3.7. Reinforcement learning
    9. 3.8. Instance-based learning
    10. 3.9. Summary
  11. Chapter 4: Classification examples for healthcare
    1. Abstract
    2. 4.1. Introduction
    3. 4.2. EEG signal analysis
    4. 4.3. EMG signal analysis
    5. 4.4. ECG signal analysis
    6. 4.5. Human activity recognition
    7. 4.6. Microarray gene expression data classification for cancer detection
    8. 4.7. Breast cancer detection
    9. 4.8. Classification of the cardiotocogram data for anticipation of fetal risks
    10. 4.9. Diabetes detection
    11. 4.10. Heart disease detection
    12. 4.11. Diagnosis of chronic kidney disease (CKD)
    13. 4.12. Summary
  12. Chapter 5: Other classification examples
    1. Abstract
    2. 5.1. Intrusion detection
    3. 5.2. Phishing website detection
    4. 5.3. Spam e-mail detection
    5. 5.4. Credit scoring
    6. 5.5. credit card fraud detection
    7. 5.6. Handwritten digit recognition using CNN
    8. 5.7. Fashion-MNIST image classification with CNN
    9. 5.8. CIFAR image classification using CNN
    10. 5.9. Text classification
    11. 5.10. Summary
  13. Chapter 6: Regression examples
    1. Abstract
    2. 6.1. Introduction
    3. 6.2. Stock market price index return forecasting
    4. 6.3. Inflation forecasting
    5. 6.4. Electrical load forecasting
    6. 6.5. Wind speed forecasting
    7. 6.6. Tourism demand forecasting
    8. 6.7. House prices prediction
    9. 6.8. Bike usage prediction
    10. 6.9. Summary
  14. Chapter 7: Clustering examples
    1. Abstract
    2. 7.1. Introduction
    3. 7.2. Clustering
    4. 7.3. The k-means clustering algorithm
    5. 7.4. The k-medoids clustering algorithm
    6. 7.5. Hierarchical clustering
    7. 7.6. The fuzzy c-means clustering algorithm
    8. 7.7. Density-based clustering algorithms
    9. 7.8. The expectation of maximization for Gaussian mixture model clustering
    10. 7.9. Bayesian clustering
    11. 7.10. Silhouette analysis
    12. 7.11. Image segmentation with clustering
    13. 7.12. Feature extraction with clustering
    14. 7.13. Clustering for classification
    15. 7.14. Summary
  15. Index

Product information

  • Title: Practical Machine Learning for Data Analysis Using Python
  • Author(s): Abdulhamit Subasi
  • Release date: June 2020
  • Publisher(s): Academic Press
  • ISBN: 9780128213803