Applied Supervised Learning with R: Use machine learning libraries of R to build models that solve business problems and predict future trends
bLearn the ropes of supervised machine learning with R by studying popular real-world use-cases, and understand how it drives object detection in driver less cars, customer churn, and loan default prediction. /b h4Key Features/h4 ulliStudy supervised learning algorithms by using real-world datasets...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing Limited
2019
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| Ausgabe: | 1 |
| Schlagworte: | |
| Zusammenfassung: | bLearn the ropes of supervised machine learning with R by studying popular real-world use-cases, and understand how it drives object detection in driver less cars, customer churn, and loan default prediction. /b h4Key Features/h4 ulliStudy supervised learning algorithms by using real-world datasets /li liFine tune optimal parameters with hyperparameter optimization /li liSelect the best algorithm using the model evaluation framework /li /ul h4Book Description/h4 R 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. To prevent you from overfitting your model, a dedicated section will even demonstrate how you can add various regularization terms. By the end of this book, you will have the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs. h4What you will learn/h4 ulliDevelop analytical thinking to precisely identify a business problem /li liWrangle data with dplyr, tidyr, and reshape2 /li liVisualize data with ggplot2 /li liValidate your supervised machine learning model using k-fold /li liOptimize hyperparameters with grid and random search, and Bayesian optimization /li liDeploy your model on Amazon Web Services (AWS) Lambda with plumber /li liImprove your model's performance with feature selection and dimensionality reduction /li /ul h4Who this book is for/h4 This book is specially designed for novice and intermediate-level data analysts, data scientists, and data engineers who want to explore different methods of supervised machine learning and its various use cases. Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the content of this book |
| Beschreibung: | 1 Online-Ressource (502 Seiten) |
| ISBN: | 9781838557164 |
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| 520 | |a bLearn the ropes of supervised machine learning with R by studying popular real-world use-cases, and understand how it drives object detection in driver less cars, customer churn, and loan default prediction. /b h4Key Features/h4 ulliStudy supervised learning algorithms by using real-world datasets /li liFine tune optimal parameters with hyperparameter optimization /li liSelect the best algorithm using the model evaluation framework /li /ul h4Book Description/h4 R 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. | ||
| 520 | |a 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. To prevent you from overfitting your model, a dedicated section will even demonstrate how you can add various regularization terms. By the end of this book, you will have the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs. | ||
| 520 | |a h4What you will learn/h4 ulliDevelop analytical thinking to precisely identify a business problem /li liWrangle data with dplyr, tidyr, and reshape2 /li liVisualize data with ggplot2 /li liValidate your supervised machine learning model using k-fold /li liOptimize hyperparameters with grid and random search, and Bayesian optimization /li liDeploy your model on Amazon Web Services (AWS) Lambda with plumber /li liImprove your model's performance with feature selection and dimensionality reduction /li /ul h4Who this book is for/h4 This book is specially designed for novice and intermediate-level data analysts, data scientists, and data engineers who want to explore different methods of supervised machine learning and its various use cases. Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the content of this book | ||
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| spelling | Ramasubramanian, Karthik Verfasser aut Applied Supervised Learning with R Use machine learning libraries of R to build models that solve business problems and predict future trends Ramasubramanian, Karthik 1 Birmingham Packt Publishing Limited 2019 1 Online-Ressource (502 Seiten) txt rdacontent c rdamedia cr rdacarrier bLearn the ropes of supervised machine learning with R by studying popular real-world use-cases, and understand how it drives object detection in driver less cars, customer churn, and loan default prediction. /b h4Key Features/h4 ulliStudy supervised learning algorithms by using real-world datasets /li liFine tune optimal parameters with hyperparameter optimization /li liSelect the best algorithm using the model evaluation framework /li /ul h4Book Description/h4 R 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. To prevent you from overfitting your model, a dedicated section will even demonstrate how you can add various regularization terms. By the end of this book, you will have the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs. h4What you will learn/h4 ulliDevelop analytical thinking to precisely identify a business problem /li liWrangle data with dplyr, tidyr, and reshape2 /li liVisualize data with ggplot2 /li liValidate your supervised machine learning model using k-fold /li liOptimize hyperparameters with grid and random search, and Bayesian optimization /li liDeploy your model on Amazon Web Services (AWS) Lambda with plumber /li liImprove your model's performance with feature selection and dimensionality reduction /li /ul h4Who this book is for/h4 This book is specially designed for novice and intermediate-level data analysts, data scientists, and data engineers who want to explore different methods of supervised machine learning and its various use cases. Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the content of this book COMPUTERS / Intelligence (AI) & Semantics COMPUTERS / Neural Networks Moolayil, Jojo Sonstige oth |
| spellingShingle | Ramasubramanian, Karthik Applied Supervised Learning with R Use machine learning libraries of R to build models that solve business problems and predict future trends COMPUTERS / Intelligence (AI) & Semantics COMPUTERS / Neural Networks |
| title | Applied Supervised Learning with R Use machine learning libraries of R to build models that solve business problems and predict future trends |
| title_auth | Applied Supervised Learning with R Use machine learning libraries of R to build models that solve business problems and predict future trends |
| title_exact_search | Applied Supervised Learning with R Use machine learning libraries of R to build models that solve business problems and predict future trends |
| title_exact_search_txtP | Applied Supervised Learning with R Use machine learning libraries of R to build models that solve business problems and predict future trends |
| title_full | Applied Supervised Learning with R Use machine learning libraries of R to build models that solve business problems and predict future trends Ramasubramanian, Karthik |
| title_fullStr | Applied Supervised Learning with R Use machine learning libraries of R to build models that solve business problems and predict future trends Ramasubramanian, Karthik |
| title_full_unstemmed | Applied Supervised Learning with R Use machine learning libraries of R to build models that solve business problems and predict future trends Ramasubramanian, Karthik |
| title_short | Applied Supervised Learning with R |
| title_sort | applied supervised learning with r use machine learning libraries of r to build models that solve business problems and predict future trends |
| title_sub | Use machine learning libraries of R to build models that solve business problems and predict future trends |
| topic | COMPUTERS / Intelligence (AI) & Semantics COMPUTERS / Neural Networks |
| topic_facet | COMPUTERS / Intelligence (AI) & Semantics COMPUTERS / Neural Networks |
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