Description
Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook is an authoritative resource by leading experts Lior Rokach, Oded Maimon, and Erez Shmueli. This comprehensive guide bridges the gap between theoretical machine learning concepts and practical data science applications.
The handbook provides in-depth coverage of data mining techniques, knowledge discovery processes, and machine learning algorithms used in real-world scenarios. It addresses key challenges in extracting meaningful patterns from large datasets and transforming raw data into actionable insights.
Ideal for data scientists, machine learning engineers, and researchers, this Springer-Nature publication offers both foundational knowledge and advanced methodologies. Topics include supervised and unsupervised learning, feature engineering, model evaluation, and deployment strategies that enable professionals to build robust predictive models and drive data-informed decision-making.
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