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.

• 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.