In this project, we will build a Naïve Bayes Classifier to predict whether a given resume text is flagged or not. Our training data consist of 125 resumes with 33 flagged resumes and 92 non flagged resumes. This project could be practically used to screen resumes in companies.
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Recommended experience
What you'll learn
Create a pipeline to remove stop-words, punctuation, and perform tokenization
Understand the theory and intuition behind Naive Bayes classifiers
Train a Naive Bayes Classifier and assess its performance
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:
Task 1: Understand the Problem Statement and Business Case
Task 2: Import libraries and datasets
Task 3: Perform exploratory data analysis
Task 4: Perform data cleaning
Task 5: Visualize cleaned datasets
Task 6: Prepare the data by applying count vectorization
Task 7: Understand the intuition behind Naive Bayes Classifier - Part #1
Task 8: Understand the intuition behind Naive Bayes Classifier - Part #2
Task 9: Train a Naive Bayes classifier model
Task 10: Assess trained model performance
Recommended experience
Basic python programming knowledge
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Instructor
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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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