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Machine Learning Engineering with MLflow

Get up and running, and productive in no time with MLflow using the most effective machine learning engineering approach Key Features;

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Beginning MLOps with MLFlow

of hyperparameter tuning with a validation data set. You will explore three different deployment setups of machine learning models with varying levels;

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Learning Spark

Build reliable data pipelines with open source Delta Lake and Spark Develop machine learning pipelines with MLlib and productionize models;

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Machine Learning Engineering with Python

Machine Learn (ETML) pattern with use cases Book DescriptionMachine learning engineering is a thriving discipline at the interface of software;

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Beginning Apache Spark 3

learning applications using Spark MLlib and how to manage the machine learning development lifecycle. This book is packed with practical examples;

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A Primer on Machine Learning Applications in Civil Engineering

learning and data analytics applications with respect to civil engineering Includes many machine learning techniques in numerous civil;

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Machine Learning and Systems Engineering

A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October;

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Machine Learning Technologies and Applications

This book comprises the best deliberations with the theme Machine Learning Technologies and Applications in the International Conference;

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Advances in Machine Learning Applications in Software Engineering

Machine learning is the study of building computer programs that improve their performance through experience. To meet the challenge;

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Empowering Artificial Intelligence Through Machine Learning

learning for credit card fraud detection, using artificial intelligence in education using gaming and automatization with courses and outcomes;

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Machine Learning with PySpark

Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business;

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Knowledge Engineering, Machine Learning and Lattice Computing with Applications

selected from 130 submissions. The papers are organized in topical sections on bioinspired and machine learning methods, machine learning;

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Welding and Cutting Case Studies with Supervised Machine Learning

includes case studies that employ machine learning for manufacturing processes in the engineering domain. The book not only provides beginners with;

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Welding and Cutting Case Studies with Supervised Machine Learning

includes case studies that employ machine learning for manufacturing processes in the engineering domain. The book not only provides beginners with;

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Practical Machine Learning with Python

Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world;

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Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces

Pascal Laube presents machine learning approaches for three key problems of reverse engineering of defective structured surfaces;

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Python Feature Engineering Cookbook

who want to optimize and enrich their machine learning models with the best features. Knowledge of machine learning and Python coding will;

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Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering

optimum values and solutions for models such as artificial intelligence and machine learning. Research is a necessity for determining how these;

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Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering

optimum values and solutions for models such as artificial intelligence and machine learning. Research is a necessity for determining how these;

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Machine Learning Engineering

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Machine Learning Engineering

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Data-Driven Science and Engineering

. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods;

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Advanced Machine Learning Technologies and Applications

This book presents the refereed proceedings of the 5th International Conference on Advanced Machine Learning Technologies and Applications;

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Automated Machine Learning

Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologies Key;

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Machine Component Analysis with MATLAB

thorough, rigorous presentation of machine design, augmented with proven learning techniques which can be used by students and practicing engineers;

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Hands-On Machine Learning with R

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today's most popular;

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Machine Learning in Cognitive IoT

of machine learning (ML) in data mining. Further, it explains various ML techniques and paradigms with different phases of data pre-processing and;

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