Naive Bayes 101: Resume Selection with Machine Learning
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
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.
Skills you will develop
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
How Guided Projects work
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
Frequently asked questions
Frequently Asked Questions
What will I get if I purchase a Guided Project?
Are Guided Projects available on desktop and mobile?
Who are the instructors for Guided Projects?
Can I download the work from my Guided Project after I complete it?
What is the refund policy?
Is financial aid available?
Can I audit a Guided Project and watch the video portion for free?
How much experience do I need to do this Guided Project?
Can I complete this Guided Project right through my web browser, instead of installing special software?
What is the learning experience like with Guided Projects?
More questions? Visit the Learner Help Center.
Coursera Footer
