Su kodu ENG10 kaina tik 101,78 €
113,09 €
Applied Supervised Learning with R
Applied Supervised Learning with R
101,78 €
113,09 €
  • Išsiųsime per 12–16 d.d.
About the BookSupervised Learning with R covers the complete process of developing applications with supervised machine learning algorithms that cater to your business needs. Your learning curve starts with developing your analytical thinking towards creating a problem statement using business inputs or domain research. You will learn many evaluation metrics that compare various algorithms, and you can then use these metrics to select the best algorithm for your problem. After finalizing the al…
101.78 2024-04-21 23:59:00
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Applied Supervised Learning with R + nemokamas atvežimas! | knygos.lt

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About the Book
Supervised Learning with R covers the complete process of developing applications with supervised machine learning algorithms that cater to your business needs. Your learning curve starts with developing your analytical thinking towards creating a problem statement using business inputs or domain research. You will learn many evaluation metrics that compare various algorithms, and you can then use 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 avoid overfitting your model, you will also be shown how to add various regularization terms.

When you complete the book, you will find yourself an expert at modelling a supervised machine learning algorithm that precisely fulfills your business need.


Features

Study the supervised learning algorithms by using them on real world datasets
Fine tune optimal parameters with hyperparameter optimization
Select the best algorithm using the model evaluation framework


What will you will learn

Develop analytical thinking to precisely identify a business problem
Wrangle data with dplyr, tidyr, and reshape2
Visualize data with ggplot2
Validate your supervised machine learning model using k-fold
Optimize hyperparameters with grid and random search and bayesian optimization
Deploy your model on AWS Lambda with plumber, an R package
Improve the model performance with feature selection and dimensionality reduction
Use a research paper with a real-world problem, frame a problem statement, and replicate its results with a supervised machine learning model

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101,78 €
113,09 €
Išsiųsime per 12–16 d.d.

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Formatai:

113,09 € Nauja knyga
minkšti viršeliai

About the Book
Supervised Learning with R covers the complete process of developing applications with supervised machine learning algorithms that cater to your business needs. Your learning curve starts with developing your analytical thinking towards creating a problem statement using business inputs or domain research. You will learn many evaluation metrics that compare various algorithms, and you can then use 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 avoid overfitting your model, you will also be shown how to add various regularization terms.

When you complete the book, you will find yourself an expert at modelling a supervised machine learning algorithm that precisely fulfills your business need.


Features

Study the supervised learning algorithms by using them on real world datasets
Fine tune optimal parameters with hyperparameter optimization
Select the best algorithm using the model evaluation framework


What will you will learn

Develop analytical thinking to precisely identify a business problem
Wrangle data with dplyr, tidyr, and reshape2
Visualize data with ggplot2
Validate your supervised machine learning model using k-fold
Optimize hyperparameters with grid and random search and bayesian optimization
Deploy your model on AWS Lambda with plumber, an R package
Improve the model performance with feature selection and dimensionality reduction
Use a research paper with a real-world problem, frame a problem statement, and replicate its results with a supervised machine learning model

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