Hello everyone and welcome to this new hands-on project on Scikit-Learn Library for solving machine learning classification problems. In this project, we will learn how to build and train classifier models using Scikit-Learn library. Scikit-learn is a free machine learning library developed for python. Scikit-learn offers several algorithms for classification, regression, and clustering. Several famous machine learning models are included such as support vector machines, random forests, gradient boosting, and k-means.
What you'll learn
Evaluate several trained classification models performance using various Key Performance Indicators (KPIs)
Understand the theory and intuition behind XG-Boost classifier model
Train machine learning classifier models using Scikit-Learn library
Skills you'll practice
Details to know
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About this Guided Project
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Understand the Problem Statement
Import Libraries and Datasets
Practice Opportunity #1 [Optional]
Perform Exploratory Data Analysis
Create Training and Testing Datasets
XG-Boost Algorithm Overview
Train an XG-Boost Algorithm Using Scikit-Learn
Evaluate Trained Model Performance
Practice Opportunity #2 [Optional]
Final Capstone Project
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How you'll learn
Skill-based, hands-on learning
Practice new skills by completing job-related tasks.
Expert guidance
Follow along with pre-recorded videos from experts using a unique side-by-side interface.
No downloads or installation required
Access the tools and resources you need in a pre-configured cloud workspace.
Available only on desktop
This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
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By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.
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