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
1.INTRODUCTION
2. DATA PRE-PROCESSING2.1. Data manipulation, Cross validation and Data over fitting2.2. Feature Extraction Methods2.3. Dimension Reduction/ Feature selection Methods2.4. Statistical Features2.5. Dimension Reduction using Principle Component Analysis (PCA)
3. MACHINE LEARNING TECHNIQUES3.1. Introduction3.2. Linear Regression3.3. Linear Discriminant Analysis3.4. K-Nearest Neighborhood3.5. Artificial Neural Networks3.6. Naïve Bayes3.7. Support Vector Machines3.8. Decision Tree Classifiers3.9. Random Forest3.10. Bagging3.11. Boosting3.12. Deep Learning3.13. Theano3.14. Tensorflow3.15. Keras3.16. K-means Clustering3.17. Fuzzy C-Means Clustering3.18. Performance EvaluationConfusion MatrixF-Measure AnalysisROC AnalysisKappa Statistic
4. CLASSIFICATION EXAMPLESHealthcare-related Examples4.1. EEG Signal Analysis4.1.1. Introduction4.1.2. Epileptic Seizure Prediction and Detection4.1.3. Emotion Recognition4.1.4. Automated Classification of Focal and Non-focal Epileptic EEG Signals4.2. EMG Signal Analysis4.2.1. Introduction4.2.2. Diagnosis of Neuromuscular Disorders4.2.3. EMG Signals in Prosthesis Control4.2.4. EMG Signals in Rehabilitation Robotics4.3. ECG Signal Analysis4.3.1. Introduction4.3.2. Diagnosis of Heart Arrhythmia4.4. Microarray Gene Expression Data Classification for cancer detection4.5. Breast Cancer Detection4.6. Classification of the Cardiotocogram Data for Anticipation of Fetal Risks4.7. Diabetes detection4.8. Heart Disease detection Non-Healthcare Classification Examples4.9. Sensor Based Human Activity Recognition4.10. Smartphone-Based Recognition of Human Activities4.11. Intrusion Detection4.12. Phishing Website Detection4.13. Spam E-mail Detection4.14. Credit scoring
5. REAL WORLD REGRESSION EXAMPLES 5.1. Introduction 5.2. Stock market price index return forecasting5.3. Inflation Forecasting5.4. Wind Speed Forecasting5.5. Electrical Load Forecasting 5.6. Tourism demand forecasting
6. CLUSTERING EXAMPLES6.1. K-Means Clustering6.2. Fuzzy C-Means Clustering
9780128040768; 9780128155530; 9780128136591; 9780128174449
