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Supervised Machine Learning: Classification

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Certification

Supervised Machine Learning: Classification

This course will teach you how to identify the most important types and families of modeling families in supervised machine learning: classification.

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Description

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Careervira Take

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Course Credibility

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Pedagogy

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Hands-on training

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Career impact

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Pricing

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Assessment

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Skills

Unsupervised LearningSupervised LearningPredictive AnalyticsMachine LearningData Analysis

Learning Goals

Differentiate uses and applications of classification and classification ensembles
Describe and use logistic regression models
Describe and use decision tree and tree-ensemble models
Describe and use other ensemble methods for classification
Use a variety of error metrics to compare and select the classification model that best suits your data
Use oversampling and undersampling as techniques to handle unbalanced classes in a data set

Course Content

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Prerequisites/Requirements

You should have familiarity with programming on a Python development environment
A fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics

Target Audience

This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Classification techniques in a business setting

Instructors

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Mark J Grover

Digital Content Delivery Lead

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Miguel Maldonado

Machine Learning Curriculum Developer

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Course Overview

Instructor-Moderated Discussions

Post course interactions

International faculty

Assessment and Grading

Graded quizzes and assignments

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Course Cover

This course includes

Duration

11 hours
Video Content
11 hours
Level
Intermediate
Instruction Type
Self Paced
Delivery Method
Online
Available on
Desktop, Laptop
Accessibility
Limited Access
Language
English
Subtitles
English
0