{"product_id":"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-paperback","title":"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 - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eHari M. Koduvely\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBecome an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features: \u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eUnderstand the principles of Bayesian Inference with less mathematical equations\u003c\/li\u003e\n\u003cli\u003eLearn state-of-the art Machine Learning methods\u003c\/li\u003e\n\u003cli\u003eFamiliarize yourself with the recent advances in Deep Learning and Big Data frameworks with this step-by-step guide\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBook Description: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eBayesian Inference provides a unified framework to deal with all sorts of uncertainties when learning patterns form data using machine learning models and use it for predicting future observations. However, learning and implementing Bayesian models is not easy for data science practitioners due to the level of mathematical treatment involved. Also, applying Bayesian methods to real-world problems requires high computational resources. With the recent advances in computation and several open sources packages available in R, Bayesian modeling has become more feasible to use for practical applications today. Therefore, it would be advantageous for all data scientists and engineers to understand Bayesian methods and apply them in their projects to achieve better results.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eLearning Bayesian Models with R starts by giving you a comprehensive coverage of the Bayesian Machine Learning models and the R packages that implement them. It begins with an introduction to the fundamentals of probability theory and R programming for those who are new to the subject. Then the book covers some of the important machine learning methods, both supervised and unsupervised learning, implemented using Bayesian Inference and R.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eEvery chapter begins with a theoretical description of the method explained in a very simple manner. Then, relevant R packages are discussed and some illustrations using data sets from the UCI Machine Learning repository are given. Each chapter ends with some simple exercises for you to get hands-on experience of the concepts and R packages discussed in the chapter.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eThe last chapters are devoted to the latest development in the field, specifically Deep Learning, which uses a class of Neural Network models that are currently at the frontier of Artificial Intelligence. The book concludes with the application of Bayesian methods on Big Data using the Hadoop and Spark frameworks.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat You Will Learn: \u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eSet up the R environment\u003c\/li\u003e\n\u003cli\u003eCreate a classification model to predict and explore discrete variables\u003c\/li\u003e\n\u003cli\u003eGet acquainted with Probability Theory to analyze random events\u003c\/li\u003e\n\u003cli\u003eBuild Linear Regression models\u003c\/li\u003e\n\u003cli\u003eUse Bayesian networks to infer the probability distribution of decision variables in a problem\u003c\/li\u003e\n\u003cli\u003eModel a problem using Bayesian Linear Regression approach with the R package BLR\u003c\/li\u003e\n\u003cli\u003eUse Bayesian Logistic Regression model to classify numerical data\u003c\/li\u003e\n\u003cli\u003ePerform Bayesian Inference on massively large data sets using the MapReduce programs in R and Cloud computing\u003c\/li\u003e\n\u003c\/ul\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 168\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.36 x 9.25 x 7.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e October 30, 2015\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":53085809148211,"sku":"9781783987603","price":66.02,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0300\/5595\/6612\/files\/bGsUGbj0WZ9781783987603.webp?v=1772046744","url":"https:\/\/www.vysn.com\/en-ca\/products\/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-paperback","provider":"VYSN","version":"1.0","type":"link"}