Multinomial Naive Bayes with Laplace (Add 1) smoothing | solved Example | Applied Machine learning скачать в хорошем качестве

Multinomial Naive Bayes with Laplace (Add 1) smoothing | solved Example | Applied Machine learning

📘 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