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Machine Learning with Tree-Based Models in Python

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5

(3)

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Course Features

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Duration

5 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

5 hours

Course Description

Decision trees can be used to solve problems that relate to regression and classification. Tree models have a high degree flexibility but come at a price: They can capture complex nonlinear relations while being more likely to recall the noise in a dataset. Ensemble methods combine predictions from different trees to increase flexibility and decrease their tendency to forget about noise. These methods are versatile and have been awarded numerous machine learning awards.

This course will show you how to use Python and scikit-learn to create decision trees. You'll be working with real data and learn about the advantages and disadvantages of trees, as well as how ensembling may help to mitigate them. To make your models more efficient, you'll also learn how important hyperparameters can be tuned.

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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Supervised Learning with scikit-learn

What You Will Learn

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In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn

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In this course, you'll learn how to use Python to train decision trees and tree-based models with the user-friendly scikit-learn machine learning library

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You'll understand the advantages and shortcomings of trees and demonstrate how ensembling can alleviate these shortcomings, all while practicing on real-world datasets

Course Instructors

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Elie Kawerk

Data Scientist at Mirum Agency

Elie is a Data Scientist with a love for Python and open source. He currently works at Majid Al Futtaim Holding and has previously worked at Mirum agency. Elie holds a PhD in computational physic...

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