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.
Naive Bayes 101: Resume Selection with Machine Learning
Naive Bayes 101: Resume Selection with Machine Learning
Instructor: Ryan Ahmed
Access provided by Somaiya Vidyavihar University
Learn, practice, and apply job-ready skills with expert guidance
4.5
(17 reviews)
Intermediate level
Recommended experience
2 hours
Learn at your own pace
Hands-on learning
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
- Unstructured Data
- Data Analysis
- Computer Science
- Python Programming
- Data Visualization
- Machine Learning
- Machine Learning Algorithms
- Predictive Modeling
- Natural Language Processing
- Plot (Graphics)
- Data Manipulation
- Data Cleansing
- Data Processing
- Scikit Learn (Machine Learning Library)
- Text Mining
- Matplotlib
- Applied Machine Learning
- Exploratory Data Analysis
- Pandas (Python Package)
Details to know
Shareable certificate
Add to your LinkedIn profile
Taught in English
No downloads or installation required
Only available on desktop
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Learn, practice, and apply job-ready skills in less than 2 hours
- Receive training from industry experts
- Gain hands-on experience solving real-world job tasks
- Build confidence using the latest tools and technologies
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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