Machine Learning Scientist with R

Master the essential skills to land a job as a machine learning scientist! You'll augment your R programming skillset with the toolbox to perform supervised and unsupervised learning. You'll learn how to process data for modeling, train your models, visualize your models and assess their performance, and tune their parameters for better performance. In the process, you'll get an introduction to Bayesian statistics, natural language processing, and Spark.

R65 hours16 coursesStatement of Accomplishment

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Supervised Learning in R: Classification

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

4 hours

Supervised Learning in R: Regression

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

4 hours

Feature Engineering in R

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

4 hours

Unsupervised Learning in R

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

4 hours

Machine Learning in the Tidyverse

Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.

5 hours

Intermediate Regression in R

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

4 hours

Cluster Analysis in R

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

4 hours

Machine Learning with caret in R

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

4 hours

Modeling with tidymodels in R

Learn to streamline your machine learning workflows with tidymodels.

4 hours

Machine Learning with Tree-Based Models in R

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

4 hours

Machine Learning Fundamentals in R

Dimensionality Reduction in R

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

4 hours

Support Vector Machines in R

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

4 hours

Fundamentals of Bayesian Data Analysis in R

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

4 hours

Hyperparameter Tuning in R

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

4 hours

Bayesian Regression Modeling with rstanarm

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

4 hours

Introduction to Spark with sparklyr in R

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

4 hours

Machine Learning Scientist with R

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Join over 12,820,000 learners and start Machine Learning Scientist with R today!