Naive Bayes 101: Resume Selection with Machine Learning

Offered By
Coursera Project Network
In this Guided Project, you will:

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

Clock2 hours
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

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

Data CleansingMachine LearningNLPArtificial Intelligence(AI)Computer Science

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:

  1. Task 1: Understand the Problem Statement and Business Case

  2. Task 2: Import libraries and datasets

  3. Task 3: Perform exploratory data analysis

  4. Task 4: Perform data cleaning

  5. Task 5: Visualize cleaned datasets

  6. Task 6: Prepare the data by applying count vectorization

  7. Task 7: Understand the intuition behind Naive Bayes Classifier - Part #1

  8. Task 8: Understand the intuition behind Naive Bayes Classifier - Part #2

  9. Task 9: Train a Naive Bayes classifier model

  10. 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

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