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Hyperparameter Tuning in R

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$12

$6

49% OFF

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Course Features

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Duration

4 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

4 hours

Course Description

It is not easy to just run a machine-learning problem from the box and make a prediction. The best model must accurately predict the outcome. You can improve your model by hyperparameter tuning. This is the process of optimizing your model's settings. This course will show you how to use caret, mlr and h2o packages to find the optimal combination of hyperparameters. This course uses grid search and random searching as well as adaptive resampling and automated machine learning (AutoML). You can also tune different supervised models like support vector and gradient boosting machines. Tune up!

Course Overview

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Virtual Labs

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International Faculty

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Post Course Interactions

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Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

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Machine Learning with caret in R

What You Will Learn

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

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Furthermore, you will work with different datasets and tune different supervised learning models, such as random forests, gradient boosting machines, support vector machines, and even neural nets. Get ready to tune!

Course Instructors

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Shirin Elsinghorst (formerly Glander)

Data Scientist @ codecentric

I m Shirin, a biologist turned bioinformatician turned data scientist. During my PhD and Postdoc I worked with Next Generation Sequencing data to analyze diseases like arthritis. However, I then chos...

Course Reviews

Average Rating Based on 3 reviews

5.0

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Course Cover

$12

$6

49% OFF

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