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Supervised Learning with scikit-learn

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Certification

Supervised Learning with scikit-learn

How to build and tune predictive models and how they perform with unseen data.

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Description

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Features

This course includes

Duration

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

Skills

Predictive ModellingSupervised LearningMachine LearningData Analysis

Learning Goals

In this course, you'll learn how to use Python to perform supervised learning, an essential component of machine learning
You'll learn how to build predictive models, tune their parameters, and determine how well they will perform with unseen data—all while using real world datasets
You'll be using scikit-learn, one of the most popular and user-friendly machine learning libraries for Python

Course Content

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Prerequisites/Requirements

Statistical Thinking in Python (Part 1)

Instructors

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Hugo Bowne-Anderson

Data Scientist at DataCamp

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Course Overview

Hands-On Training, Instructor-Moderated Discussions

Post course interactions

Virtual labs

International faculty

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Course Cover

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