Information Technology
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Model Validation in Python

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5

(3)

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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

Machine learning models are now more accessible than ever. You might not get the results you expect when running new data through a machine-learning model that hasn't been validated. Analysts can confidently answer "How good are your models?" Validating their models is the best way to answer this question. This question can be answered both for classification models that use all tic-tac–'toe scenarios as well as regression models that use fivethirtyeight's ultimate Halloween candy power ranking dataset. This course will give an overview of validation, and talk about different validation methods. Also, we'll be discussing tools that can help you create high-performing, validated models.

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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Learn the basics of model validation, validation techniques, and begin creating validated and high performing models

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In this course, we will cover the basics of model validation, discuss various validation techniques, and begin to develop tools for creating validated and high performing models

Course Instructors

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Kasey Jones

Research Data Scientist

Kasey Jones is a research data scientist at RTI International. His work focuses primarily on agent-based model simulations and natural language processing analysis. He also enjoys creating unique vis...

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