Linear Classifiers in Python

via

DataCamp

Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

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

Virtual Labs

International Faculty

Post Course Interactions

Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Supervised Learning with scikit-learn

What You Will Learn

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

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

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

Course Content

Expand all sections

Module 1: Applying logistic regression and SVM

Module 2: Loss functions

Module 3: Logistic regression

Module 4: Support Vector Machines

Course Instructors

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...