After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Описание курса
Tidymodels is a powerful suite of R packages designed to streamline machine learning workflows. Learn to split datasets for cross-validation, preprocess data with tidymodels' recipe package, and fine-tune machine learning algorithms. You'll learn key concepts such as defining model objects and creating modeling workflows. Then, you'll apply your skills to predict home prices and classify employees by their risk of leaving a company.
Предварительные требования
Программа
Структура курса
1
Machine Learning with tidymodels
In this chapter, you’ll explore the rich ecosystem of R packages that power tidymodels and learn how they can streamline your machine learning workflows. You’ll then put your tidymodels skills to the test by predicting house sale prices in Seattle, Washington.
- The tidymodels ecosystem50 XP
- Tidymodels packages100 XP
- Creating training and test datasets100 XP
- Distribution of outcome variable values100 XP
- Linear regression with tidymodels50 XP
- Fitting a linear regression model100 XP
- Exploring estimated model parameters50 XP
- Predicting home selling prices100 XP
- Evaluating model performance50 XP
- Model performance metrics100 XP
- R squared plot100 XP
- Complete model fitting process with last_fit()100 XP
2
Classification Models
Learn how to predict categorical outcomes by training classification models. Using the skills you’ve gained so far, you’ll predict the likelihood of customers canceling their service with a telecommunications company.
3
Feature Engineering
Find out how to bake feature engineering pipelines with the recipes package. You’ll prepare numeric and categorical data to help machine learning algorithms optimize your predictions.
4
Workflows and Hyperparameter Tuning
Now it’s time to streamline the modeling process using workflows and fine-tune models with cross-validation and hyperparameter tuning. You’ll learn how to tune a decision tree classification model to predict whether a bank's customers are likely to default on their loan.
R
Modeling with tidymodels in R
Курс
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