Practical Machine Learning for Streaming Data with Python

Design, Develop, and Validate Online Learning Models

By: Sayan Putatunda

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Paperback | 9 April 2021

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136 Pages

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23.5 x 15.5

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Chapter 1: An Introduction to Streaming DataChapter Goal: Introduce the readers to the concept of streaming data, the various challenges associated with it, some of its real-world business applications, various windowing techniques along with the concepts of incremental and online learning algorithms. This chapter will also help in understanding the concept of model evaluation in case of streaming data and provide and introduction to the Scikit-Multiflow framework in Python.No of pages- 35Sub -Topics1. Streaming data2. Challenges of streaming data3. Concept drift4. Applications of streaming data5. Windowing techniques6. Incremental learning and online learning7. Illustration: Adopting batch learners into incremental learners8. Introduction to Scikit-Multiflow framework9. Evaluation of streaming algorithms

Chapter 2: Change DetectionChapter Goal: Help the readers to understand the various change detection/concept drift detection algorithms and its implementation on various datasets using Scikit-Multiflow.No of pages: 35Sub - Topics: 1. Change detection problem2. Concept drift detection algorithms3. ADWIN4. DDM5. EDDM6. Page Hinkley
Chapter 3: Supervised and Unsupervised Learning for Streaming DataChapter Goal: Help the readers to understand the various regression and classification (including Ensemble Learning) algorithms for streaming data and its implementation on various datasets using Scikit-Multiflow. Also, discuss some approaches for clustering with streaming data and its implementation using Python.No of pages: 35Sub - Topics: 1. Regression with streaming data2. Classification with streaming data3. Ensemble Learning with streaming data4. Clustering with streaming data
Chapter 4: Other Tools and the Path ForwardChapter Goal: Introduce the readers to the other open source tools for handling streaming data such as Spark streaming, MOA and more. Also, educate the reader about additional reading for advanced topics within streaming data analysis.No of pages: 35Sub - Topics: 1. Other tools for handling streaming data1.1.1. Apache Spark1.1.2. Massive Online Analysis (MOA)1.1.3. Apache Kafka2. Active research areas and breakthroughs in streaming data analysis3. Conclusion

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