Naive Bayes Algorithm in Machine Learning | Naive Bayes Tutorial | Machine Learning | Intellipaat

Welcome to our YouTube channel! In this video, we will dive into the Naive Bayes algorithm, one of the popular and simple machine learning algorithms used for classification tasks. If you're interested in understanding how Naive Bayes works, its assumptions, applications, and examples, this video is for you!

Naive Bayes is a probabilistic algorithm that uses Bayes' theorem to make predictions based on conditional probabilities. In this video, we will provide you with a comprehensive overview of Naive Bayes, explaining the concept in a simple and easy-to-understand manner.

We will start by introducing Bayes' theorem, which forms the foundation of the Naive Bayes algorithm. We will then delve into the Applications of Naive Bayes Algorithms, next, we will explain the Logical Intuition Behind Naive Bayes Algorithms. In addition, we will demonstrate the implementation of the Naive Bayes algorithm in Python using popular machine learning libraries, such as sci-kit-learn, and walk you through a step-by-step example of how to train a Naive Bayes classifier on a real-world dataset.

Whether you're new to machine learning or looking to expand your knowledge on Naive Bayes, this video will provide you with a comprehensive understanding of how this simple yet powerful algorithm works, its assumptions, applications, and examples. Join us in this informative journey into the world of the Naive Bayes algorithm! Hit the play button and let's dive into the fascinating world of machine learning.

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#NaiveBayesAlgorithminMachineLearning #NaiveBayesTutorial #Machine Learning #Intellipaat

🔵 Following topics are covered in this session:
00:00 - Introduction
01:04 - Bayes Theorem in Statistics
04:46 - Applications of Naive Bayes Algorithm
05:58 - Logical Intuition Behind Naive Bayes Algorithm
15:47 - Hands-on Implementation

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[Intellipaat]

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[ccsiri]

4:29 P(B|A) should be 1/2. B- second coin being head. A - first coin being tail. There are two cases B satisfy which is {HH, TH}. Out of these only TH satisfy A. Therefore probability of second coin being head when the first coin being tail is 1/2. You should understand mathematics and concepts inside the bayes theorem.

[exiphykiller]

Great session. Theory was covered brilliantly!

[musiknation]

Kindly solve an example of medical data on prediction in details using confusion matrix, accuracy, f1score in python

[TAUFIQUESEKH]

When running y_pred=clf.predict(X_test), getting error ValueError: dimension mismatch, why? Your code is running but I am getting this error. @intellipaat