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Machine Learning Fundamentals in R

Learn the basics of prediction using machine learning. This track covers predicting categorical and numeric responses via classification and regression, and discovering the hidden structure of datasets (unsupervised learning). Learn how to process data for modeling, how to train your models, how to visualize your models and assess their performance, and how to tune their parameters for better performance.

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1
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Supervised Learning in R: Classification

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

4 hours

Brett Lantz Headshot

Brett Lantz

Senior Data Scientist at Sony PlayStation

2
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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

Nina Zumel Headshot

Nina Zumel

Co-founder, Principal Consultant at Win-Vector, LLC

3
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Unsupervised Learning in R

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

4 hours

Hank Roark Headshot

Hank Roark

Senior Data Scientist, Boeing

4
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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

Zachary Deane-Mayer Headshot

Zachary Deane-Mayer

VP, Data Science at DataRobot

5
R
Modeling with tidymodels in R

Learn to streamline your machine learning workflows with tidymodels.

4 hours

David Svancer Headshot

David Svancer

Data Scientist

6
R
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

Sandro Raabe Headshot

Sandro Raabe

Data Scientist

7
R
Machine Learning Fundamentals in R
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