机器学习科学家 在 R 中

In this course you will learn the basics of machine learning for classification.

In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.

Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.

This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.

Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.

Learn to perform linear and logistic regression with multiple explanatory variables.

Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.

This course teaches the big ideas in machine learning like how to build and evaluate predictive models.

Learn to streamline your machine learning workflows with tidymodels.

Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.

Learn dimensionality reduction techniques in R and master feature selection and extraction for your own data and models.

This course will introduce the support vector machine (SVM) using an intuitive, visual approach.

Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.

Learn how to tune your model's hyperparameters to get the best predictive results.

Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.

Build a regression model for a DVD rental firm to predict rental duration. Evaluate models to recommend the best one.

Learn how to run big data analysis using Spark and the sparklyr package in R, and explore Spark MLIb in just 4 hours.