Multinomial Naive Bayes with Laplace (Add 1) smoothing | solved Example | Applied Machine learning
Автор: NextGen Learners
Загружено: 2026-02-19
Просмотров: 129
Описание:
📘 Applied Machine Learning Playlist:
• CS4014 - Applied Machine Learning
Multinomial Naive Bayes is a widely used Machine Learning classification algorithm, especially for text classification, spam detection, and sentiment analysis, where features represent word frequencies or counts.
In this video, we solve a Multinomial Naive Bayes example step by step using Laplace (Add-1) smoothing, which helps handle the zero probability problem and improves model reliability.
You will learn:
What is Multinomial Naive Bayes
When to use Multinomial Naive Bayes
Difference between Bernoulli and Multinomial Naive Bayes
What is Laplace (Add-1) smoothing
Why smoothing is needed
Step-by-step solved example with smoothing
Prior probability and likelihood calculation
How prediction is made using Multinomial Naive Bayes
Real-world applications in NLP and Machine Learning
This lecture is part of the Applied Machine Learning playlist and is ideal for:
Machine Learning beginners
Data Science students
AI students
University exam preparation
Interview preparation
#machinelearning #naivebayes #multinomialnaivebayes #laplacesmoothing #appliedmachinelearning #datascience #artificialintelligence #mlalgorithms #mlforbeginners #datasciencestudents
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