Grey Cover of Machine Learning with Decision Trees and Random Forests. The course notes resource is from 365 Data Science.

Machine Learning with Decision Trees and Random Forests

Decision trees are a popular intuitive supervised machine learning algorithm, that is part of the sklearn library, and has wide areas of applications like- business growth opportunities evaluation, demographic-driven data client targeting, and strategic management planning. Every machine learner worth their salt needs to familiarize themselves with the decision trees machine learning model. These free machine learning with random forests and decision trees pdf course notes will teach you how do decision trees work, how they ensemble into the random forest algorithm, what are their pros and cons, which are the most commonly used performance metrics and much more.

Nikola Pulev
Course Author

Who is it for

These free machine learning with random forests and decision trees pdf course notes are designed for data scientists and machine learning engineers who want to utilize the classification and regression capabilities of this intuitive machine learning algorithm.

How it can help you

Learning how to use decision trees and random forests will simplify your workflow, as you spend less time preprocessing and cleaning data, you can intuitively communicate your findings, and are able to train your model moderately fast.

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Courses where you'll use this resource

Machine Learning with Decision Trees and Random Forests