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Control your machine learning algorithms using test-driven development to achieve quantifiable milestones About This Book * Build;
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Learn how to apply test-driven development (TDD) to machine-learning algorithms - and catch mistakes that could sink your analysis. In this;
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This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on;
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, and HD Insight to perform machine learning Set up a data science virtual machine and test-drive installed tools, such as Azure ML Workbench;
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, Stream Analytics, Azure Databricks, and HD Insight to perform machine learning Set up a data science virtual machine and test-drive installed;
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Streams 2020, and First International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020;
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Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems;
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intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics;
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The book focuses on how machine learning and the Internet of Things (IoT) has empowered the advancement of information driven arrangements;
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If you are an active user of statistics to examine trends in your business, then using machine learning is your next best step. It uses;
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The Machine Trades Passbook® in the Test Your Knowledge Series features general test questions in this field of study. It provides hundreds;
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Machine Learning and Knowledge Discovery for Engineering Systems Health Management presents state-of-the-art tools and techniques for;
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Learning, Deep Learning and Intelligent Optimization. Physical devices powered with Artificial Intelligence are gaining importance in diagnosis and;
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Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning;
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Create scalable machine learning applications to power a modern data-driven business using Spark 2.x About This Book * Get to the;
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Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business;
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implementing various optimization techniques within their machining processes. The application of methods including machine learning and genetic;
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implementing various optimization techniques within their machining processes. The application of methods including machine learning and genetic;
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machine learning problems Key Features Delve into machine learning with this comprehensive guide to scikit-learn and scientific Python Master the;
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This book offers a clear understanding of the concept of context-aware machine learning including an automated rule-based framework within;
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