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Naive Bayes: Simple Yet Powerful Classification

Name: Naive Bayes: Simple Yet Powerful Classification
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Naive Bayes is a machine learning algorithm based on probability and the Bayes theorem. It assumes that features are independent and is widely used for tasks like spam detection, text classification, and predictive modeling due to its speed and efficiency.

4.5

|3h 32m

Naive Bayes: Simple Yet Powerful Classification course

What's Included

Lessons

3h 32m

Duration

Certificate

What You'll Master

Skills and outcomes you'll walk away with

Module 1: Mathematical Foundations of Bayesian Probability

Module 2: The Naive Independence Assumption

Module 3: Gaussian Naive Bayes for Continuous Data

Module 4: Multinomial Naive Bayes for Text Analysis

Module 5: Bernoulli Naive Bayes for Binary Features

Module 6: Addressing Data Scarcity with Laplace Smoothing

Course Curriculum

12 lessons • 3h 32m total

Module 1: Mathematical Foundations of Bayesian Probability

19m

Module 2: The Naive Independence Assumption

17m

Module 3: Gaussian Naive Bayes for Continuous Data

16m

Module 4: Multinomial Naive Bayes for Text Analysis

17m

Module 5: Bernoulli Naive Bayes for Binary Features

18m

Module 6: Addressing Data Scarcity with Laplace Smoothing

19m

Module 7: Feature Engineering and Text Vectorization

17m

Module 8: Model Evaluation and Performance Metrics

19m

Module 9: Hyperparameter Tuning and Cross-Validation

17m

Module 10: Real-World Applications and Deployment

17m

Module 11: Advanced Bayesian Networks

17m

Module 12: Scaling Naive Bayes and Out-of-Core Learning

19m

Certification Path

Certification Exam

36 multiple-choice questions • 70% passing score required

Final Project: Building an SMS Spam Classifier using Naive Bayes

In this final assignment, you will apply the concepts learned throughout the course to build a robust SMS spam detection system. You are required to: 1) Load and preprocess the SMS Spam Collection dataset through cleaning text, removing stop words, and tokenization. 2) Implement a Multinomial Naive Bayes classifier. 3) Split the data into training and testing sets. 4) Evaluate the model using Accuracy, Precision, Recall, and an F1-score. 5) Provide a brief report discussing why Naive Bayes is suitable for this specific task despite the naive assumption of feature independence.

Verified Certificate

Earn a verified PDF certificate with unique verification ID upon completion • ₹299