Machine Learning for Streaming Data with Python: Rapidly build practical online machine learning solutions

Cover
Title Page
Copyright and Credits
Contributors
Table of Contents
Preface
Part 1: Introduction and Core Concepts of Streaming Data
Chapter 1: An Introduction to Streaming Data
	Technical requirements
		Setting up a Python environment
	A short history of data science
	Working with streaming data
		Streaming data versus batch data
		Advantages of streaming data
		Examples of successful implementation of streaming analytics
		Challenges of streaming data
		How to get started with streaming data
		Common use cases for streaming data
		Streaming versus big data
	Real-time data formats and importing an example dataset in Python
	Summary
	Further reading
Chapter 2: Architectures for Streaming and Real-Time Machine Learning
	Technical requirements
		Python environment
	Defining your analytics as a function
	Understanding microservices architecture
	Communicating between services through APIs
	Demystifying the HTTP protocol
		The GET request
		The POST request
		JSON format for communication between systems
		RESTful APIs
	Building a simple API on AWS
		API Gateway in AWS
		Lambda in AWS
		Data-generating process on a local machine
		Implementing the example
		More architectural considerations
		Other AWS services and other services in general that have the same functionality
	Big data tools for real time streaming
		Calling a big data environment in real time
	Summary
	Further reading
Chapter 3: Data Analysis on Streaming Data
	Technical requirements
		Python environment
	Descriptive statistics on streaming data
		Why are descriptive statistics different on streaming data?
	Introduction to sampling theory
		Comparing population and sample
		Population parameters and sample statistics
		Sampling distribution
		Sample size calculations and confidence level
		Rolling descriptive statistics from streaming
		Exponential weight
		Tracking convergence as an additional KPI
	Overview of the main descriptive statistics
		The mean
		The median
		The mode
		Standard deviation
		Variance
		Quartiles and interquartile range
		Correlations
	Real-time visualizations
		Opening the dashboard
		Comparing Plotly's Dash and other real-time visualization tools
	Building basic alerting systems
		Alerting systems on extreme values
		Alerting systems on process stability (mean and median)
		Alerting systems on constant variability (std and variance)
		Basic alerting systems using statistical process control
	Summary
	Further reading
Part 2: Exploring Use Cases for Data Streaming
Chapter 4: Online Learning with River
	Technical requirements
		Python environment
	What is online machine learning?
		How is online learning different from regular learning?
		Advantages of online learning
		Challenges of online learning
		Types of online learning
	Using River for online learning
		Training an online model with River
		Improving the model evaluation
		Building a multiclass classifier using one-vs-rest
	Summary
	Further reading
Chapter 5: Online Anomaly Detection
	Technical requirements
		Python environment
	Defining anomaly detection
		Are outliers a problem?
	Exploring use cases of anomaly detection
		Fraud detection in financial institutions
		Anomaly detection on your log data
		Fault detection in manufacturing and production lines
		Hacking detection in computer networks (cyber security)
		Medical risks in health data
		Predictive maintenance and sensor data
	Comparing anomaly detection and imbalanced classification
		The problem of imbalanced data
		The F1 score
		SMOTE oversampling
		Anomaly detection versus classification
	Algorithms for detecting anomalies in River
		The use of thresholders in River anomaly detection
		Anomaly detection algorithm 1 – One-Class SVM
		Anomaly detection algorithm 2 – Half-Space-Trees
	Going further with anomaly detection
	Summary
	Further reading
Chapter 6: Online Classification
	Technical requirements
		Python environment
	Defining classification
	Identifying use cases of classification
		Use case 1 – email spam classification
		Use case 2 – face detection in phone camera
		Use case 3 – online marketing ad selection
	Overview of classification algorithms in River
		Classification algorithm 1 – LogisticRegression
		Classification algorithm 2 – Perceptron
		Classification algorithm 3 – AdaptiveRandomForestClassifier
		Classification algorithm 4 – ALMAClassifier
		Classification algorithm 5 – PAClassifier
		Evaluating benchmark results
	Summary
	Further reading
Chapter 7: Online Regression
	Technical requirements
		Python environment
	Defining regression
	Use cases of regression
		Use case 1 – Forecasting
		Use case 2 – Predicting the number of faulty products in manufacturing
	Overview of regression algorithms in River
		Regression algorithm 1 – LinearRegression
		Regression algorithm 2 – HoeffdingAdaptiveTreeRegressor
		Regression algorithm 3 – SGTRegressor
		Regression algorithm 4 – SRPRegressor
	Summary
	Further reading
Chapter 8: Reinforcement Learning
	Technical requirements
		Python environment
	Defining reinforcement learning
		Comparing online and offline reinforcement learning
		A more detailed overview of feedback loops in reinforcement learning
	The main steps of a reinforcement learning model
		Making the decisions
		Updating the decision rules
	Exploring Q-learning
		The goal of Q-learning
		Parameters of the Q-learning algorithm
	Deep Q-learning
	Using reinforcement learning for streaming data
	Use cases of reinforcement learning
		Use case one – trading system
		Use case two – social network ranking system
		Use case three – a self-driving car
		Use case four – chatbots
		Use case five – learning games
	Implementing reinforcement learning in Python
	Summary
	Further reading
Part 3: Advanced Concepts and Best Practices around Streaming Data
Chapter 9: Drift and Drift Detection
	Technical requirements
		Python environment
	Defining drift
		Three types of drift
	Introducing model explicability
	Measuring drift
		Measuring data drift
		Measuring concept drift
	Measuring drift in Python
		A basic intuitive approach to measuring drift
		Measuring drift with robust tools
	Counteracting drift
		Offline learning with retraining strategies against drift
		Online learning against drift
	Summary
	Further reading
Chapter 10: Feature Transformation and Scaling
	Technical requirements
		Python environment
	Challenges of data preparation with streaming data
	Scaling data for streaming
		Introducing scaling
		Adapting scaling to a streaming context
	Transforming features in a streaming context
		Introducing PCA
		Mathematical definition of PCA
		Regular PCA in Python
		Incremental PCA for streaming
	Summary
	Further reading
Chapter 11: Catastrophic Forgetting
	Technical requirements
		Python environment
	Introducing catastrophic forgetting
	Catastrophic forgetting in online models
	Detecting catastrophic forgetting
		Using Python to detect catastrophic forgetting
	Model explicability versus catastrophic forgetting
		Explaining models using linear coefficients
		Explaining models using dendrograms
		Explaining models using variable importance
	Summary
	Further reading
Chapter 12: Conclusion and Best Practices
	Going further
	Summary
Index
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