Isbn: 9781838556334 - applied supervised learning with r: use machine learning libraries of r to build models that solve business problems and predict future trends (22 resultados)

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Paperback or Softback. Condición: New. Applied Supervised Learning with R. Book.

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Paperback. Condición: New. Learn the ropes of supervised machine learning with R by studying popular real-world use cases, and understand how it drives object detection in driverless cars, customer churn, and loan default prediction.Key FeaturesStudy supervised learning algorithms by using real-world datasetsFine-tune optimal parameters with hyperparameter optimizationSelect the best algorithm using the model evaluation frameworkBook DescriptionR provides excellent visualization features that are essential for exploring data before using it in automated learning.Applied Supervised Learning with R helps you cover the complete process of employing R to develop applications using supervised machine learning algorithms for your business needs. The book starts by helping you develop your analytical thinking to create a problem statement using business inputs and domain research. You will then learn different evaluation metrics that compare various algorithms, and later progress to using these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine-tune your set of optimal parameters. The book demonstrates how you can add different regularization terms to avoid overfitting your model.By the end of this book, you will have gained the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs.What you will learnDevelop analytical thinking to precisely identify a business problemWrangle data with dplyr, tidyr, and reshape2Visualize data with ggplot2Validate your supervised machine learning model using k-foldOptimize hyperparameters with grid and random search, and Bayesian optimizationDeploy your model on Amazon Web Services (AWS) Lambda with plumberImprove your model's performance with feature selection and dimensionality reductionWho this book is forThis book is specially designed for beginner and intermediate-level data analysts, data scientists, and data engineers who want to explore different methods of supervised machine learning and its use cases. Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the concepts covered in this book.…

Paperback. Condición: New. Learn the ropes of supervised machine learning with R by studying popular real-world use cases, and understand how it drives object detection in driverless cars, customer churn, and loan default prediction.Key FeaturesStudy supervised learning algorithms by using real-world datasetsFine-tune optimal parameters with hyperparameter optimizationSelect the best algorithm using the model evaluation frameworkBook DescriptionR provides excellent visualization features that are essential for exploring data before using it in automated learning.Applied Supervised Learning with R helps you cover the complete process of employing R to develop applications using supervised machine learning algorithms for your business needs. The book starts by helping you develop your analytical thinking to create a problem statement using business inputs and domain research. You will then learn different evaluation metrics that compare various algorithms, and later progress to using these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine-tune your set of optimal parameters. The book demonstrates how you can add different regularization terms to avoid overfitting your model.By the end of this book, you will have gained the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs.What you will learnDevelop analytical thinking to precisely identify a business problemWrangle data with dplyr, tidyr, and reshape2Visualize data with ggplot2Validate your supervised machine learning model using k-foldOptimize hyperparameters with grid and random search, and Bayesian optimizationDeploy your model on Amazon Web Services (AWS) Lambda with plumberImprove your model's performance with feature selection and dimensionality reductionWho this book is forThis book is specially designed for beginner and intermediate-level data analysts, data scientists, and data engineers who want to explore different methods of supervised machine learning and its use cases. Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the concepts covered in this book.…

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Paperback. Condición: New. Learn the ropes of supervised machine learning with R by studying popular real-world use cases, and understand how it drives object detection in driverless cars, customer churn, and loan default prediction.Key FeaturesStudy supervised learning algorithms by using real-world datasetsFine-tune optimal parameters with hyperparameter optimizationSelect the best algorithm using the model evaluation frameworkBook DescriptionR provides excellent visualization features that are essential for exploring data before using it in automated learning.Applied Supervised Learning with R helps you cover the complete process of employing R to develop applications using supervised machine learning algorithms for your business needs. The book starts by helping you develop your analytical thinking to create a problem statement using business inputs and domain research. You will then learn different evaluation metrics that compare various algorithms, and later progress to using these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine-tune your set of optimal parameters. The book demonstrates how you can add different regularization terms to avoid overfitting your model.By the end of this book, you will have gained the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs.What you will learnDevelop analytical thinking to precisely identify a business problemWrangle data with dplyr, tidyr, and reshape2Visualize data with ggplot2Validate your supervised machine learning model using k-foldOptimize hyperparameters with grid and random search, and Bayesian optimizationDeploy your model on Amazon Web Services (AWS) Lambda with plumberImprove your model's performance with feature selection and dimensionality reductionWho this book is forThis book is specially designed for beginner and intermediate-level data analysts, data scientists, and data engineers who want to explore different methods of supervised machine learning and its use cases. Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the concepts covered in this book.…

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Paperback. Condición: New. Learn the ropes of supervised machine learning with R by studying popular real-world use cases, and understand how it drives object detection in driverless cars, customer churn, and loan default prediction.Key FeaturesStudy supervised learning algorithms by using real-world datasetsFine-tune optimal parameters with hyperparameter optimizationSelect the best algorithm using the model evaluation frameworkBook DescriptionR provides excellent visualization features that are essential for exploring data before using it in automated learning.Applied Supervised Learning with R helps you cover the complete process of employing R to develop applications using supervised machine learning algorithms for your business needs. The book starts by helping you develop your analytical thinking to create a problem statement using business inputs and domain research. You will then learn different evaluation metrics that compare various algorithms, and later progress to using these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine-tune your set of optimal parameters. The book demonstrates how you can add different regularization terms to avoid overfitting your model.By the end of this book, you will have gained the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs.What you will learnDevelop analytical thinking to precisely identify a business problemWrangle data with dplyr, tidyr, and reshape2Visualize data with ggplot2Validate your supervised machine learning model using k-foldOptimize hyperparameters with grid and random search, and Bayesian optimizationDeploy your model on Amazon Web Services (AWS) Lambda with plumberImprove your model's performance with feature selection and dimensionality reductionWho this book is forThis book is specially designed for beginner and intermediate-level data analysts, data scientists, and data engineers who want to explore different methods of supervised machine learning and its use cases. Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the concepts covered in this book.…

Condición: gut. Applied Supervised Learning with R: Use machine learning libraries of R to build models that solve business problems and predict future trends In englischer Sprache. pages.

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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Applied Supervised Learning with R will make you a pro at identifying your business problem, selecting the best supervised machine learning algorithm to solve it, and fine-tuning your model to exactly deliver your needs without overfitting itself.…

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Taschenbuch. Condición: Neu. Applied Supervised Learning with R | Karthik Ramasubramanian (u. a.) | Taschenbuch | Kartoniert / Broschiert | Englisch | 2019 | Packt Publishing | EAN 9781838556334 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…