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Linear Classifiers in Python

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Course Report - Linear Classifiers in Python

Course Report

Find detailed report of this course which helps you make an informed decision on its relevance to your learning needs. Find out the course's popularity among Careervira users and the job roles that would find the course relevant for their upskilling here. You can also find how this course compares against similar courses and much more in the course report.

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

This course will show you how scikit-learn can be used to teach linear classifiers. Once you've learned how to use these methods you can dive into their ideas to discover what makes them tick. This course will show you how to tune and train these Python linear classifiers. This course will also help you understand other machine learning algorithms.

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

Supervised Learning with scikit-learn

What You Will Learn

In this course you will learn the details of linear classifiers like logistic regression and SVM

In this course you'll learn all about using linear classifiers, specifically logistic regression and support vector machines, with scikit-learn

At the end of this course you'll know how to train, test, and tune these linear classifiers in Python

You'll also have a conceptual foundation for understanding many other machine learning algorithms

Course Instructors

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Mike Gelbart

Instructor, the University of British Columbia

Mike Gelbart is an Instructor in the Department of Computer Science at the University of British Columbia (UBC) in Vancouver, Canada. He also teaches in, and co-designed, the Master of Data Science p...

Course Reviews

Average Rating Based on 3 reviews

5.0

100%

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