Practical Machine Learning for Data Analysis Using Python

1 - Introduction
	1.1 - What is machine learning?
		1.1.1 - Why is machine learning needed?
		1.1.2 - Making data-driven decisions
		1.1.3 - Definitions and key terminology
		1.1.4 - Key tasks of machine learning
		1.1.5 - Machine learning techniques
	1.2 - Machine learning framework
		1.2.1 - Data collection
		1.2.2 - Data description
		1.2.3 - Exploratory data analysis
		1.2.4 - Data quality analysis
		1.2.5 - Data preparation
		1.2.6 - Data integration
		1.2.7 - Data wrangling
		1.2.8 - Feature scaling and feature extraction
		1.2.9 - Feature selection and dimension reduction
		1.2.10 - Modeling
		1.2.11 - Selecting modeling techniques
		1.2.12 - Model building
		1.2.13 - Model assessment and tuning
		1.2.14 - Implementation and examining the created model
		1.2.15 - Supervised machine learning framework
		1.2.16 - Unsupervised machine learning framework
	1.3 - Performance evaluation
		1.3.1 - Confusion matrix
		1.3.2 - F-measure analysis
		1.3.3 - ROC analysis
		1.3.4 - Kappa statistic
		1.3.5 - What is measured
		1.3.6 - How they are measured
		1.3.7 - How to interpret estimates
		1.3.8 - k-Fold cross-validation in scikit-learn
		1.3.9 - How to choose the right algorithm
	1.4 - The Python machine learning environment
		1.4.1 - Pitfalls
		1.4.2 - Drawbacks
		1.4.3 - The NumPy library
		1.4.4 - Pandas
	1.5 - Summary
	References
2 - Data preprocessing
	2.1 - Introduction
	2.2 - Feature extraction and transformation
		2.2.1 - Types of features
		2.2.2 - Statistical features
		2.2.3 - Structured features
		2.2.4 - Feature transformations
		2.2.5 - Thresholding and discretization
		2.2.6 - Data manipulation
		2.2.7 - Standardization
	2.2.8 - Normalization and calibration
	2.2.9 - Incomplete features
	2.2.10 - Feature extraction methods
	2.2.11 - Feature extraction using wavelet transform
		2.2.11.1 - The continuous wavelet transform (CWT)
		2.2.11.2 - The discrete wavelet transform (DWT)
		2.2.11.3 - The stationary wavelet transform (SWT)
		2.2.11.4 - The wavelet packet decomposition (WPD)
	2.3 - Dimension reduction
		2.3.1 - Feature construction and selection
		2.3.2 - Univariate feature selection
		2.3.3 - Recursive feature elimination
		2.3.4 - Feature selection from a model
		2.3.5 - Principle component analysis (PCA)
		2.3.6 - Incremental PCA
		2.3.7 - Kernel principal component analysis
		2.3.8 - Neighborhood components analysis
		2.3.9 - Independent component analysis
		2.3.10 - Linear discriminant analysis (LDA)
		2.3.11 - Entropy
	2.4 - Clustering for feature extraction and dimension reduction
	References
3 - Machine learning techniques
	3.1 - Introduction
	3.2 - What is machine learning?
		3.2.1 - Understanding machine learning
		3.2.2 - What makes machines learn?
		3.2.3 - Machine learning is a multidisciplinary field
		3.2.4 - Machine learning problem
		3.2.5 - Goals of learning
		3.2.6 - Challenges in machine learning
	3.3 - Python libraries
		3.3.1 - Scikit-learn
		3.3.2 - TensorFlow
		3.3.3 - Keras
		3.3.4 - Building a model with Keras
		3.3.5 - The natural language tool kit
	3.4 - Learning scenarios
	3.5 - Supervised learning algorithms
		3.5.1 - Classification
		3.5.2 - Forecasting, prediction, and regression
	3.5.3 - Linear models
		3.5.4 - The perceptron
		3.5.5 - Logistic regression
		3.5.6 - Linear discriminant analysis
		3.5.7 - Artificial neural networks
		3.5.8 - k-Nearest neighbors
		3.5.9 - Support vector machines
		3.5.10 - Decision tree classifiers
		3.5.11 - Naive Bayes
		3.5.12 - Ensemble methods
		3.5.13 - Bagging
		3.5.14 - Random forest
		3.5.15 - Boosting
			3.5.15.1 - Adaptive Boosting (AdaBoost)
			3.5.15.2 - Gradient Boosting
		3.5.16 - Other ensemble methods
		3.5.17 - Deep learning
		3.5.18 - Deep neural networks
		3.5.19 - Recurrent neural networks
		3.5.20 - Autoencoders
		3.5.21 - Long short-term memory (LSTM) networks
		3.5.22 - Convolutional neural networks
			3.5.22.1 - Convolution layer
			3.5.22.2 - Pooling layer
	3.6 - Unsupervised learning
		3.6.1 - K-means algorithm
		3.6.2 - Silhouettes
		3.6.3 - Anomaly detection
		3.6.4 - Association rule-mining
	3.7 - Reinforcement learning
	3.8 - Instance-based learning
	3.9 - Summary
	References
4 - Classification examples for healthcare
	4.1 - Introduction
	4.2 - EEG signal analysis
		4.2.1 - Epileptic seizure prediction and detection
		4.2.2 - Emotion recognition
		4.2.3 - Classification of focal and nonfocal epileptic EEG signals
		4.2.4 - Migraine detection
	4.3 - EMG signal analysis
		4.3.1 - Diagnosis of neuromuscular disorders
		4.3.2 - EMG signals in prosthesis control
		4.3.3 - EMG signals in rehabilitation robotics
	4.4 - ECG signal analysis
		4.4.1 - Diagnosis of heart arrhythmia
	4.5 - Human activity recognition
		4.5.1 - Sensor-based human activity recognition
		4.5.2 - Smartphone-based recognition of human activities
	4.6 - Microarray gene expression data classification for cancer detection
	4.7 - Breast cancer detection
	4.8 - Classification of the cardiotocogram data for anticipation of fetal risks
	4.9 - Diabetes detection
	4.10 - Heart disease detection
	4.11 - Diagnosis of chronic kidney disease (CKD)
	4.12 - Summary
	References
Chapter 5 - Other classification examples
	5.1 - Intrusion detection
	5.2 - Phishing website detection
	5.3 - Spam e-mail detection
	5.4 - Credit scoring
	5.5 - credit card fraud detection
	5.6 - Handwritten digit recognition using CNN
	5.7 - Fashion-MNIST image classification with CNN
	5.8 - CIFAR image classification using CNN
	5.9 - Text classification
	5.10 - Summary
	References
Chapter 6 - Regression examples
	6.1 - Introduction
	6.2 - Stock market price index return forecasting
	6.3 - Inflation forecasting
	6.4 - Electrical load forecasting
	6.5 - Wind speed forecasting
	6.6 - Tourism demand forecasting
	6.7 - House prices prediction
	6.8 - Bike usage prediction
	6.9 - Summary
	References
7 - Clustering examples
	7.1 - Introduction
	7.2 - Clustering
		7.2.1 - Evaluating the output of clustering methods
		7.2.2 - Applications of cluster analysis
		7.2.3 - Number of possible clustering
		7.2.4 - Types of clustering algorithms
	7.3 - The k-means clustering algorithm
	7.4 - The k-medoids clustering algorithm
	7.5 - Hierarchical clustering
		7.5.1 - Agglomerative clustering algorithm
		7.5.2 - Divisive clustering algorithm
	7.6 - The fuzzy c-means clustering algorithm
	7.7 - Density-based clustering algorithms
		7.7.1 - The DBSCAN algorithm
		7.7.2 - OPTICS clustering algorithms
	7.8 - The expectation of maximization for Gaussian mixture model clustering
	7.9 - Bayesian clustering
	7.10 - Silhouette analysis
	7.11 - Image segmentation with clustering
	7.12 - Feature extraction with clustering
	7.13 - Clustering for classification
	7.14 - Summary
	References