SkillShare – Decision Trees Random Forests and Gradient Boosting in R

SkillShare – Decision Trees Random Forests and Gradient Boosting in R-SkilledHares
English | Size: 1.78 GB
Category: Tutorial

Welcome to the Decision Trees, Random Forests and Gradient Boosting course in R. At the end of this course, you will be able to develop supervised learning models (machine learning) using different R packages such as ROCR, CARET and XGBoost.

This new knowledge will be useful if you are interested in making a career as a business intelligence analyst or to gain insights that allow you to make better decisions based on data if you are an entrepreneur.

This is a very complete course with more than 40 sessions divided into 9 sections. In it, we approach data mining and machine learning tools with great depth and with a 100% practical teaching case approach. Unlike other courses, this goes beyond simply being an R programming course as it addresses both pre-cleansing data and comparing models and analyzing results.

We’ll start the course by crafting a step-by-step sort tree with manual calculations so you understand the black box behind the algorithm. Next, we will use a teaching case with real data,in which we will assume the role of a data analyst newly hired by a travel agency that will have to predict the clients most likely to purchase a program based on historical data.

I designed this course for university students and professionals interested in data science and machine learning but who for their training have very little experience in programming languages. This is an open course for everyone that includes an introductory section to R, so it has no prerequisites.

I invite you to review the model class and sign up. I hope to see you soon!
Author: Carlos Martinez, PhD
Duration: 3h 24m

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