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A First Course in Machine Learning

A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines;

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First Course In Machine Learning 2nd Ed

A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines;

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Deep Learning

Build and run intelligent applications by leveraging key Java machine learning libraries About This Book * Develop a sound strategy to;

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Python

. In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety;

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Python

in this field, one must be able to understand and design a machine learning system that serves the needs of a project. The idea is to prepare a;

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Machine Learning with scikit-learn Quick Start Guide

. Key Features Build your first machine learning model using scikit-learn Train supervised and unsupervised models using popular techniques such;

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Machine Learning Using R

Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition;

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R

Find out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning;

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Hands-On Data Science and Python Machine Learning

This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale;

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Machine Learning

introduction to the area, suitable either for a first course, or as a component in general machine learning and advanced AI courses. Also an important;

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Machine Learning Modeling for IoUT Networks

This book discusses how machine learning and the Internet of Things (IoT) are playing a part in smart control of underwater environments;

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Accelerated Optimization for Machine Learning

provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It;

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Accelerated Optimization for Machine Learning

provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It;

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Machine Learning

A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Since the best-selling first edition was published;

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Deep Learning in Computational Mechanics

This book provides a first course on deep learning in computational mechanics. The book starts with a short introduction to machine;

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Deep Learning in Computational Mechanics

This book provides a first course on deep learning in computational mechanics. The book starts with a short introduction to machine;

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Thinking Machines

computing and neural networks. This is the first book that focuses on machine learning accelerators and hardware development for machine learning;

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Introduction to Machine Learning with R

guide will help you gain a solid foundation in machine learning principles. Using the R programming language, you'll first start to learn with;

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Machine Learning for Networking: First International Conference, Mln 2018, Paris, France, November 27-29, 2018, Revised Selected Papers

This book constitutes the thoroughly refereed proceedings of the First International Conference on Machine Learning for Networking, MLN;

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Hands-On Machine Learning with C++

get started with machine learning algorithms and techniques using the popular C++ language. As well as being a useful first course in machine;

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Machine Learning Foundations

techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides;

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Official Google Cloud Certified Professional Data Engineer Study Guide

Dan Sullivan, a popular and experienced online course author for machine learning, big data, and Cloud topics, Google Cloud Certified Professional Data;

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Machine Learning

Every day, someone is putting down a book on machine learning and giving up on learning about this revolutionary topic. How many of them;

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First-order and Stochastic Optimization Methods for Machine Learning

learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the;

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First-order and Stochastic Optimization Methods for Machine Learning

learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the;

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Big Data, Machine Learning, and Applications

This book constitutes refereed proceedings of the First International First International Conference on Big Data, Machine Learning, and;

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Machine Learning for Email

This compact book explores standard tools for text classification, and teaches the reader how to use machine learning to decide whether a e;

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