Practical Machine Learning for Streaming Data with Python
Design, Develop, and Validate Online Learning Models
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Practical Machine Learning for Streaming Data with Python
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Delivery estimate
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Practical Machine Learning for Streaming Data with Python
Description
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.
Machine learning engineers and data science professionals
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