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Designing Machine Learning Workflows in Python

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$12

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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

It is possible to deploy machine learning models into production with modern tools. However, this can often lead to disappointment as the model does not perform well in production. This course will teach four skills that will make you stand out in data science and help you create pipelines that can withstand the test of time. These superpowers include how you can tune your model in every detail during development, how to use domain expertise and how monitor and address performance problems. Finally, you will learn how to deal with poorly labeled data. This course focuses on the cutting-edge in sklearn, and covers real-life data such as cybersecurity and personalized healthcare.

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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Python Data Science Toolbox (Part 2)

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Supervised Learning with scikit-learn

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Unsupervised Learning in Python

What You Will Learn

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Learn to build pipelines that stand the test of time

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In this chapter, you will be reminded of the basics of a supervised learning workflow

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In this chapter, you will critically examine the ways in which expert knowledge is incorporated in supervised learning

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You will also learn to diagnose dataset shift and mitigate the effect that a changing environment can have on your model's accuracy

Course Instructors

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Christoforos Anagnostopoulos

Honorary Associate Professor

My career has been motivated by a singular and genuine curiosity into what it means to learn from evidence, and in particular from evidence arising from measurements, i.e., data. I have been fortunat...

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Course Cover

$12

$6

49% OFF

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