Impara Microsoft Excel: dalle fondamenta a professionista!
Build a dynamic Machine Learning Pipeline, which you can use in Data Science and Data Analysis Projects
Office Productivity,Microsoft,Excel
Lectures -4
Duration -3.5 hours
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Course Description
Hi,
In this course, you are going to get a quick introduction to the fascinating world of building predictive models in R using tidymodels. This course guides you through important packages of tidy models to empower you to build an automatic regression pipeline, which you can use to tune a model for your own data set. I will introduce you to tidy models and show you how you can build a simple API using a plumber.
We discover the important packages together. After each video in the first chapter, you will solve short quizzes to deepen your knowledge. Step by step, you will learn the important parts to build a regression pipeline.
In the second chapter, we will finally build the pipeline, which you can customize to your specific needs. We will make the pipeline available through a plumber.
This course is pretty dense in nature, but I believe that it is a very good starting point for you, since you will be able to build upon the material provided to you.
I assume that you are familiar with R, tidyverse principles and the basics of machine learning. But even If you are a complete beginner, I think that this course can be valuable to you.
I am looking forward to seeing you in the course.
Sincerely Moritz
Goals
Build a regression pipeline with R using tidymodels
Make your analysis available through plumber
Make predictions on a new data set using your trained model
Visualize cross-validation results
Learn how to render a markdown document as HTML with parameters
Learn how to tune hyperparameters using tidymodels
Prerequisites
R programming
Machine Learning
tidyverse

Curriculum
Check out the detailed breakdown of what’s inside the course
Corso di Excel per Principianti
4 Lectures
-
Cenni preliminari, Serie, Somme, Formattazione Tabelle 54:51 54:51
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Introduzione alle formule, Somme automatiche, Medie, Formattazione numerica 51:57 51:57
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Funzionalità di archivio dati, Maschere preimpostate condizionate, Filtri 51:09 51:09
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Formattazione condizionale con formule, Scadenziari, Rappresentazione grafica 54:59 54:59
Instructor Details

STEFANO AMATO
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