Practical Machine Learning for Data Analysis Using Python

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

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-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 Statistic

4. 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 scoring

5. 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 forecasting

6. CLUSTERING EXAMPLES
6.1. K-Means Clustering
6.2. Fuzzy C-Means Clustering