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Learning Bayesian Models with R: Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems

Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems About This Book;

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Learning Probabilistic Graphical Models in R

real-world problems * Know how to prepare data and feed the models by using the appropriate algorithms from the appropriate R package Who;

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Machine Learning with R

Solve real-world data problems with R and machine learning Key Features Third edition of the bestselling, widely acclaimed R machine;

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Practical Applications of Bayesian Reliability

models along with computer codes for all of the case studies JAGS and R codes are provided on an accompanying website to enable practitioners to;

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R Machine Learning By Example

, you will learn to apply the concepts of machine learning to deal with data-related problems and solve them using the powerful yet simple;

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Applied Analytics through Case Studies Using SAS and R

understanding of statistics, but have limited experience in implementing predictive models and machine learning techniques for analyzing real world data;

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R

R machine learning algorithms from scratch and be amazed to see the algorithms in action * Solve interesting real-world problems using;

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Machine Learning Using R

industry focused real-world use cases Tackle time series modeling in R Apply deep learning using Keras and TensorFlow in R Who This Book is For;

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R Statistics Cookbook

Solve real-world statistical problems using the most popular R packages and techniques Key Features Learn how to apply statistical methods;

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Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan

Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods;

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Bayesian Speech & Language Processing

With this comprehensive guide you will learn how to apply Bayesian machine learning techniques systematically to solve various problems;

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Hands-On Deep Learning with R

Explore and implement deep learning to solve various real-world problems using modern R libraries such as TensorFlow, MXNet, H2O, and;

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Mastering Machine Learning with R

application of Machine Learning methods using an extensive set of R packages * Understand the benefits and potential pitfalls of using machine;

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Practical Machine Learning with R

understand the process of machine learning and apply it to a real dataset using R, this book is just what you need. Data scientists who use Python;

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Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines

Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google;

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Mastering Machine Learning with R -

learning, with R who now want to take their skills to the next level and become an expert in the field. What You Will Learn * Gain deep;

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Bayesian Analysis with Excel and R

of Excel's Bayesian capabilities and move toward R to do even more. Step by step, with real-world examples, Carlberg shows you how to use;

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Hands-On Ensemble Learning with R

machine learning algorithms can be combined to reduce common problems and build simple efficient ensemble models with the help of real-world;

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Bayesian Approaches in Oncology Using R and OpenBUGS

Bayesian Approaches in Oncology Using R and OpenBUGS serves two audiences: those who are familiar with the theory and applications;

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Bayesian Analysis with Python

Students, researchers and data scientists who wish to learn Bayesian data analysis with Python and implement probabilistic models in their day to;

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Bayesian Methods for Management and Business

WinBUGS and R to illustrate the integration of Bayesian statistics within data-rich environments. Computational issues are discussed and;

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Bayesian Analysis for Population Ecology

ecology field, the authors apply the theory to a wide range of actual case studies and illustrate the methods using WinBUGS and R. The computer;

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Bayesian Analysis for Population Ecology

field, the authors apply the theory to a wide range of actual case studies and illustrate the methods using WinBUGS and R. The computer programs;

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Machine Learning and its Applications

that can be solved using Machine Learning Provides methods for directly applying Machine Learning techniques to concrete real world problems;

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Machine Learning and its Applications

problems that can be solved using Machine Learning Provides methods for directly applying Machine Learning techniques to concrete real world;

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Think Bayes

out of the way, the Bayesian fundamentals will become clearer and you'll begin to apply these techniques to real-world problems. Bayesian;

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Graphical Models for Machine Learning and Digital Communication

learning, data compression, and channel coding. Using probabilistic structures such as Bayesian belief networks and Markov random fields, he;

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