Multinomial Naive Bayes with Laplace ( ADD 1 ) Smoothing | How we make dictionary? | Past Paper Q скачать в хорошем качестве

Multinomial Naive Bayes with Laplace ( ADD 1 ) Smoothing | How we make dictionary? | Past Paper Q

📘 Applied Machine Learning Playlist:    • CS4014 - Applied Machine Learning   Multinomial Naive Bayes is a powerful Machine Learning algorithm commonly used for text classification, spam detection, and NLP tasks. A key step in this algorithm is creating the dictionary (vocabulary) and applying Laplace (Add-1) smoothing to handle zero probabilities. In this video, we solve a past paper question step by step, explaining how to create the dictionary, calculate probabilities, and apply Laplace smoothing in Multinomial Naive Bayes. You will learn: What is Multinomial Naive Bayes How to create the dictionary (vocabulary) Why dictionary size is important What is Laplace (Add-1) smoothing How smoothing prevents zero probability Step-by-step solved past paper example Prior probability and likelihood calculation How prediction is made in Multinomial Naive Bayes Applications in NLP, spam filtering, and text classification This lecture is part of the Applied Machine Learning playlist and is ideal for: Machine Learning beginners Data Science students AI students University exam and past paper preparation Interview preparation #machinelearning #naivebayes #multinomialnaivebayes #laplacesmoothing #appliedmachinelearning #datascience #artificialintelligence #mlalgorithms #mlforbeginners #datasciencestudents