Packt Publishing Unisex Machine Learning For Data Mining: Advanced Predictive Modeling Techniques For Data Analysis - Non Fiction - Paperback
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Title:. Machine Learning for Data Mining: Improve your data mining capabilities with advanced predictive modeling Condition: BRAND NEW. Format: Paperback. ISBN: 9781838828974 Overview:. Machine Learning for Data Mining offers a practical, hands-on pathway to elevate your data mining projects with advanced predictive modeling. Authored by Jesus Salcedo and published by Packt Publishing in 2019 as a first edition, this paperback guide is written for data scientists, analysts, and students who want to blend the strengths of machine learning with traditional data mining techniques. The book demystifies when and how to apply various ML methods within data mining workflows, from initial data preparation to final model deployment. You’ll learn a clear, step-by-step approach to building, evaluating, and refining predictive models, with an emphasis on interpretability and actionable insights. Topics include selecting the right algorithm for different data landscapes, designing robust experiment...
Packt Publishing Unisex Machine Learning For Data Mining: Advanced Predictive Modeling Techniques For Data Analysis - Non Fiction - Paperback
Title:. Machine Learning for Data Mining: Improve your data mining capabilities with advanced predictive modeling Condition: BRAND NEW. Format: Paperback. ISBN: 9781838828974 Overview:. Machine Learning for Data Mining offers a practical, hands-on pathway to elevate your data mining projects with advanced predictive modeling. Authored by Jesus Salcedo and published by Packt Publishing in 2019 as a first edition, this paperback guide is written for data scientists, analysts, and students who want to blend the strengths of machine learning with traditional data mining techniques. The book demystifies when and how to apply various ML methods within data mining workflows, from initial data preparation to final model deployment. You’ll learn a clear, step-by-step approach to building, evaluating, and refining predictive models, with an emphasis on interpretability and actionable insights. Topics include selecting the right algorithm for different data landscapes, designing robust experiment...
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