{"product_id":"9781784395216","title":"Mastering Probabilistic Graphical Models Using Python","description":"\u003cp\u003e\u003cb\u003eMaster probabilistic graphical models by learning through real-world problems and illustrative code examples in Python\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAbout This Book\u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eGain in-depth knowledge of Probabilistic Graphical Models\u003c\/li\u003e\n\u003cli\u003eModel time-series problems using Dynamic Bayesian Networks\u003c\/li\u003e\n\u003cli\u003eA practical guide to help you apply PGMs to real-world problems\u003c\/li\u003e\n\u003c\/ul\u003e\u003cb\u003eWho This Book Is For\u003c\/b\u003e\u003cp\u003eIf you are a researcher or a machine learning enthusiast, or are working in the data science field and have a basic idea of Bayesian Learning or Probabilistic Graphical Models, this book will help you to understand the details of Graphical Models and use it in your data science problems. This book will also help you select the appropriate model as well as the appropriate algorithm for your problem.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eWhat You Will Learn\u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eGet to know the basics of Probability theory and Graph Theory\u003c\/li\u003e\n\u003cli\u003eWork with Markov Networks\u003c\/li\u003e\n\u003cli\u003eImplement Bayesian Networks\u003c\/li\u003e\n\u003cli\u003eExact Inference Techniques in Graphical Models such as the Variable Elimination Algorithm\u003c\/li\u003e\n\u003cli\u003eUnderstand approximate Inference Techniques in Graphical Models such as Message Passing Algorithms\u003c\/li\u003e\n\u003cli\u003eSample algorithms in Graphical Models\u003c\/li\u003e\n\u003cli\u003eGrasp details of Naive Bayes with real-world examples\u003c\/li\u003e\n\u003cli\u003eDeploy PGMs using various libraries in Python\u003c\/li\u003e\n\u003cli\u003eGain working details of Hidden Markov Models with real-world examples\u003c\/li\u003e\n\u003c\/ul\u003e\u003cb\u003eIn Detail\u003c\/b\u003e\u003cp\u003eProbabilistic Graphical Models is a technique in machine learning that uses the concepts of graph theory to compactly represent and optimally predict values in our data problems. In real world problems, it's often difficult to select the appropriate graphical model as well as the appropriate inference algorithm, which can make a huge difference in computation time and accuracy. Thus, it is crucial to know the working details of these algorithms.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis book starts with the basics of probability theory and graph theory, then goes on to discuss various models and inference algorithms. All the different types of models are discussed along with code examples to create and modify them, and also to run different inference algorithms on them. There is a complete chapter devoted to the most widely used networks Naive Bayes Model and Hidden Markov Models (HMMs). These models have been thoroughly discussed using real-world examples.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eStyle and approach\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eAn easy-to-follow guide to help you understand Probabilistic Graphical Models using simple examples and numerous code examples, with an emphasis on more widely used models.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e","brand":"Packt Publishing","offers":[{"title":"Default Title","offer_id":47135510954224,"sku":"9781784395216","price":35.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/7593\/9824\/files\/9781784395216_p0.jpg?v=1763726521","url":"https:\/\/shop-qa.barnesandnoble.com\/products\/9781784395216","provider":"Barnes \u0026 Noble (DEV)","version":"1.0","type":"link"}