Скачать или смотреть New Course Available Now: Machine Learning with Tidymodels

The ever increasing application of machine learning models in industry and academia requires tools which are easy to use and ensure a reliable model fitting process. The R package universe covers practically all statistical models on the planet including all relevant machine learning models like neural nets, support vector machines, decision trees, and random forests. However, most of these packages do not provide a consistent interface, which makes it hard to fit and compare models from different families. Even worse, it is hard to create standardized workflows for typical machine learning projects which ensure that

no information has been leaked from the training data, leading to higher performance numbers.
models are compared on the same re-sampling procedures.
performance metrics are calculated correctly.

The tidymodels framework is a new package ecosystem, in which all steps of the machine learning workflow are implemented through dedicated R packages. The consistency of these packages ensures their interoperability and ease of use. Most importantly, the framework makes your machine learning workflow *easier to understand* and *faster to implement**. *tidymodels should definitely be part of every R data scientist's tool box. Additionally, it fits perfectly into the tidyverse package ecosystem and provides excellent compatibility with packages like dplyr or ggplot2.

Each lesson in the Machine Learning with Tidymodels (https://www.quantargo.com/courses/cou...) course module covers one essential skill which together completes the entire machine learning workflow:

**The tidymodels Machine Learning Workflow**: Start your machine learning journey and learn the most fundamental building blocks of the tidymodels framework.
**Data Preprocessing with recipes**: Learn why data preprocessing is crucial in your machine learning workflow and create your first data transformations with the recipes package.
**Model Fitting with parsnip**: Fit machine learning models using the parsnip package including linear regression, decision trees and boosting trees.
**Model Evaluation and Performance Metrics with yardstick**: Estimate model quality based on different performance metrics using the yardstick package.
**Resampling techniques using rsample**: Avoid overfitting by using resampling techniques including cross-validation and bootstrap using the rsample package.
**Model optimization using tune**: Optimize your model parameters using the tune package to find models which predict new data well..