Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework. This book is a quick start guide for data scientists and machine learning engineers looking to implement machine learning models for streaming data with Python to generate real-time insights. You'll start with an introduction to streaming data, the various challenges associated with it, some of its real-world business applications, and various windowing techniques. You'll then examine incremental and online learning algorithms, and the concept of model evaluation with streaming data and get introduced to the Scikit-Multiflow framework in Python. This is followed by a review of the various change detection/concept drift detection algorithms and the implementation of various datasets using Scikit-Multiflow. Introduction to the various supervised and unsupervised algorithms for streaming data, and their implementation on various datasets using Python are also covered. The book concludes by briefly covering other open-source tools available for streaming data such as Spark, MOA (Massive Online Analysis), Kafka, and more. What You'll Learn Understand machine learning with streaming data concepts Review incremental and online learning Develop models for detecting concept drift Explore techniques for classification, regression, and ensemble learning in streaming data contexts Apply best practices for debugging and validating machine learning models in streaming data context Get introduced to other open-source frameworks for handling streaming data. Who This Book Is For Machine learning engineers and data science professionals Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework. This book is a quick start guide for data scientists and machine learning engineers looking to implement machine learning models for streaming data with Python to generate real-time insights. You'll start with an introduction to streaming data, the various challenges associated with it, some of its real-world business applications, and various windowing techniques. You'll then examine incremental and online learning algorithms, and the concept of model evaluation with streaming data and get introduced to the Scikit-Multiflow framework in Python. This is followed by a review of the various change detection/concept drift detection algorithms and the implementation of various datasets using Scikit-Multiflow. Introduction to the various supervised and unsupervised algorithms for streaming data, and their implementation on various datasets using Python are also covered. The book concludes by briefly covering other open-source tools available for streaming data such as Spark, MOA (Massive Online Analysis), Kafka, and more. You will: Understand machine learning with streaming data concepts. Review incremental and online learning. Develop models for detecting concept drift. Explore techniques for classification, regression, and ensemble learning in streaming data contexts. Apply best practices for debugging and validating machine learning models in streaming data context. Get introduced to other open-source frameworks for handling streaming data Table of Contents About the Author About the Technical Reviewer Acknowledgements Introduction Chapter 1: An Introduction to Streaming Data Streaming Data The Need to Process and Analyze Streaming Data The Challenges of Streaming Data Applications of Streaming Data Windowing Techniques Incremental Learning and Online Learning Introduction to the Scikit-Multiflow Framework Streaming Data Generators Create a Data Stream from a CSV file Summary References Chapter 2: Concept Drift Detection in Data Streams Concept Drift Adaptive Windowing Method for Concept Drift Detection Drift Detection Method Early Drift Detection Method Drift Detection Using HDDM_A and HDDM_W Drift Detection Using the Page-Hinkley Method Summary References Chapter 3: Supervised Learning for Streaming Data Evaluation Methods Decision Trees for Streaming Data Hoeffding Tree Classifier Hoeffding Adaptive Tree Classifier Extremely Fast Decision Tree Classifier Hoeffding Tree Regressor Hoeffding Adaptive Tree Regressor Lazy Learning Methods for Streaming Data Ensemble Learning for Streaming Data Adaptive Random Forests Online Bagging Online Boosting Data Stream Preprocessing Summary References Chapter 4: Unsupervised Learning and Other Tools for Data Stream Mining Unsupervised Learning for Streaming Data Clustering Anomaly Detection Other Tools and Technologies for Data Stream Mining Massive Online Analysis (MOA) Apache Spark Apache Flink Apache Storm Apache Kafka Faust Creme River Conclusion and the Path Forward References Index
