
Practical Machine Learning for Data Analysis Using Python
Resources
Description
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
Readership
Table of Contents
1.INTRODUCTION
2. DATA PRE-PROCESSING
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. MACHINE LEARNING TECHNIQUES
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 Statistic4. CLASSIFICATION EXAMPLES
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 scoring5. REAL WORLD REGRESSION EXAMPLES
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 forecasting6. CLUSTERING EXAMPLES
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

Affiliations and Expertise
Ratings and Reviews
There are currently no reviews for "Practical Machine Learning for Data Analysis Using Python"