In the bayesian classification
The final ans doesn't matter in the calculation
Because there is no need of value for the decision you have to simply identify which one is greater and therefore you can find the final result.
-~-~~-~~~-~~-~-
Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3"
https://www.youtube.com/watch?v=GS3HKR6CV8E
-~-~~-~~~-~~-~-

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Well Academy

Naive Bayes Classification Algorithm – Solved Numerical Question 1 in Hindi
Data Warehouse and Data Mining Lectures in Hindi

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Naive Bayes Classifier- Fun and Easy Machine Learning
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Now Naïve Bayes is based on Bayes Theorem also known as conditional Theorem, which you can think of it as an evidence theorem or trust theorem. So basically how much can you trust the evidence that is coming in, and it’s a formula that describes how much you should believe the evidence that you are being presented with. An example would be a dog barking in the middle of the night. If the dog always barks for no good reason, you would become desensitized to it and not go check if anything is wrong, this is known as false positives. However if the dog barks only whenever someone enters your premises, you’d be more likely to act on the alert and trust or rely on the evidence from the dog. So Bayes theorem is a mathematic formula for how much you should trust evidence.
So lets take a look deeper at the formula,
• We can start of with the Prior Probability which describes the degree to which we believe the model accurately describes reality based on all of our prior information, So how probable was our hypothesis before observing the evidence.
• Here we have the likelihood which describes how well the model predicts the data. This is term over here is the normalizing constant, the constant that makes the posterior density integrate to one. Like we seen over here.
• And finally the output that we want is the posterior probability which represents the degree to which we believe a given model accurately describes the situation given the available data and all of our prior information. So how probable is our hypothesis given the observed evidence.
So with our example above. We can view the probability that we play golf given it is sunny = the probability that we play golf given a yes times the probability it being sunny divided by probability of a yes. This uses the golf example to explain Naive Bayes.
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5 Minutes Engineering

Introduction to Bayesian theory and Bayes classification with an easy example.

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Saurabh Singh

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This Naive Bayes Tutorial video from Edureka will help you understand all the concepts of Naive Bayes classifier, use cases and how it can be used in the industry. This video is ideal for both beginners as well as professionals who want to learn or brush up their concepts in Data Science and Machine Learning through Naive Bayes. Below are the topics covered in this tutorial:
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edureka!

Naive Bayes is a machine learning algorithm for classification problems. It is based on Bayes’ probability theorem. It is primarily used for text classification which involves high dimensional training data sets. A few examples are spam filtration, sentimental analysis, and classifying news articles. It is not only known for its simplicity, but also for its effectiveness. It is fast to build models and make predictions with Naive Bayes algorithm. Naive Bayes is the first algorithm that should be considered for solving text classification problem. Hence, you should learn this algorithm thoroughly.
This video will talk about below:
1. Machine Learning Classification
2. Naive Bayes Theorem
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Watch this video to understand how a problem in Naive Bayes is solved in data mining for classification on the given data set. Watch Now!
شاهد هذا الفيديو لفهم كيفية حل مشكلة في Naive Bayes في التنقيب عن البيانات للتصنيف على مجموعة البيانات المحددة. شاهد الآن!
Assista a este vídeo para entender como um problema em Naive Bayes é resolvido na mineração de dados para classificação no conjunto de dados fornecido. Assista agora!
Regardez cette vidéo pour comprendre comment un problème dans Naive Bayes est résolu dans l'exploration de données pour la classification sur l'ensemble de données donné. Regarde maintenant!
Sehen Sie sich dieses Video an, um zu verstehen, wie ein Problem in Naive Bayes im Data Mining zur Klassifizierung auf dem gegebenen Datensatz gelöst wird. Schau jetzt!
Mire este video para comprender cómo se resuelve un problema en Naive Bayes en la extracción de datos para su clasificación en un conjunto de datos determinado. ¡Ver ahora!
Посмотрите это видео, чтобы понять, как проблема в Naive Bayes решена в области интеллектуального анализа данных для классификации по данному набору данных. Смотри!
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Naive Bayes Classification Algorithm – Solved Numerical Question 2 in Hindi
Data Warehouse and Data Mining Lectures in Hindi

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This Edureka video will provide you with a detailed and comprehensive knowledge of Naive Bayes Classifier Algorithm in python. At the end of the video, you will learn from a demo example on Naive Bayes. Below are the topics covered in this tutorial:
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edureka!

My web page:
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Noureddin Sadawi

Data Warehouse and Mining
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Anuradha Bhatia

simple and easy explanation of Naive Bayes Algorithm in Hindi

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Red Apple Tutorials

naive Bayes classifiers in data mining or machine learning are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features.
Naive Bayes has been studied extensively since the 1950s. It was introduced under a different name into the text retrieval community in the early 1960s,and remains a popular (baseline) method for text categorization, the problem of judging documents as belonging to one category or the other (such as spam or legitimate, sports or politics, etc.) with word frequencies as the features. With appropriate pre-processing, it is competitive in this domain with more advanced methods including support vector machines. It also finds application in automatic medical diagnosis.
for more refer to
https://en.wikipedia.org/wiki/Naive_Bayes_classifier
naive bayes classifier example for play-tennis
Download PDF of the sum on below link
https://britsol.blogspot.in/2017/11/naive-bayes-classifier-example-pdf.html
*****************************************************NOTE*********************************************************************************
The steps explained in this video is correct but
please don't refer the given sum from the book mentioned in this video coz the solution for this problem might be wrong due to printing mistake.
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fun 2 code

Using SQL Server and Visual Studio for data mining with Bayes Theorem

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Ben KIM

Simple example of the Naive Bayes classification algorithm

Views: 130211
Francisco Iacobelli

This Naive Bayes Classifier tutorial video will introduce you to the basic concepts of Naive Bayes classifier, what is Naive Bayes and Bayes theorem, conditional probability concepts used in Bayes theorem, where is Naive Bayes classifier used, how Naive Bayes algorithm works with solved examples, advantages of Naive Bayes. By the end of this video, you will also implement Naive Bayes algorithm for text classification in Python.
The topics covered in this Naive Bayes video are as follows:
1. What is Naive Bayes? ( 01:06 )
2. Naive Bayes and Machine Learning ( 05:45 )
3. Why do we need Naive Bayes? ( 05:46 )
4. Understanding Naive Bayes Classifier ( 06:30 )
5. Advantages of Naive Bayes Classifier ( 20:17 )
6. Demo - Text Classification using Naive Bayes ( 22:36 )
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Simplilearn

Bayes Classifiers; Bayes rule; discrete and Gaussian class-conditional distributions

Views: 32317
Alexander Ihler

#Naivebayesclassifier #MachineLearning #CodeWrestling
This video explains the concept of classification of text from a set of documents using a Naive Bayes Classifier approach.
This video also deals with the concept of Bayes Theorem.
We have explained the topic using a sample dataset of text which is classified as of whether it belongs to "sports" category or not.
We train the model and then classify a new sentence 'A very close game' by finding its probability for belonging to "sports" category or not. The most likely probability is the final category, that sentence belongs to.
Naive Bayes is a machine learning algorithm for classification problems. It is based on Bayes’ probability theorem. Naive Bayes classifier is primarily used for text classification which involves high dimensional training data sets. A few examples are spam filtration, sentimental analysis, and classifying news articles. Naive Bayes is not only known for its simplicity, but also for its effectiveness. Naive Bayes is fast to build models and make predictions with the Naive Bayes algorithm. Naive Bayes is the first algorithm that should be considered for solving a text classification problem. Hence, you should learn this algorithm thoroughly.
For any queries or suggestions, Write to us at [email protected]
We value your feedback.
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Visit Again!! 😇

Views: 8759
Code Wrestling

Provides steps for applying Naive Bayes Classification with R.
Data: https://goo.gl/nCFX1x
R file: https://goo.gl/Feo5mT
Machine Learning videos: https://goo.gl/WHHqWP
Naive Bayes Classification is an important tool related to analyzing big data or working in data science field.
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 19612
Bharatendra Rai

Naive Bayes | Naive Bayes Algorithm | Naive Bayes Algorithm In Data Mining
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Noureddin Sadawi

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Last moment tuitions

This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst.
You can check out the full details of the program here: https://www.udacity.com/course/nd002.

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Udacity

[http://bit.ly/N-Bayes] How can we use Naive Bayes classifier with continuous (real-valued) attributes? We estimate the priors and the means / variances for the Gaussians (two in this example).

Views: 31795
Victor Lavrenko

شرح مادة داتامايننك Naive Bayes Classifier

Views: 14389
Sudets1

Document Download Link:
https://drive.google.com/file/d/0BzfRBPjlIsD8dG1VQnJLRkNEdFk/view?usp=sharing

Views: 1675
Mahmudul Hasan

More Data Mining with Weka: online course from the University of Waikato
Class 2 - Lesson 6: Multinomial Naïve Bayes
http://weka.waikato.ac.nz/
Slides (PDF):
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New Zealand
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Views: 19548
WekaMOOC

How to apply naive bayes algorithm | classifier in weka tool ?
In this video, I explained that how can you apply naive bayes algorithm in weka tool.

Views: 5917
DataMining Tutorials

This is a low math introduction and tutorial to classifying text using Naive Bayes. One of the most seminal methods to do so.

Views: 96482
Francisco Iacobelli

Introduction
Heart Diseases remain the biggest cause of deaths for the last two epochs.
Recently computer technology develops software to assistance doctors in making decision of heart disease in the early stage. Diagnosing the heart disease mainly depends on clinical and obsessive data.
Prediction system of Heart disease can assist medical experts for predicting heart disease current status based on the clinical data of various patients.
In this project, the Heart disease prediction using classification algorithm Naive Bayes, and Random Forest is discussed.
Naive Bayes Algorithm
The Naive Bayes classification algorithm is a probabilistic classifier. It is based on probability models that incorporate strong independence assumptions.
Naive Bayes is a simple technique for constructing classifiers models that assign class labels to problem instances.
It assume that the value of a particular feature is independent of the value of any other feature, given the class variable. For example, a fruit may be considered to be an apple if it is red, round, and about 10 cm in diameter. A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of any possible correlations between the color, roundness, and diameter features.
Random Forest Technique
In this technique, a set of decision trees are grown and each tree votes for the most popular class, then the votes of different trees are integrated and a class is predicted for each sample.
This approach is designed to increase the accuracy of the decision tree, more trees are produced to vote for class prediction. This approach is an ensemble classifier composed of some decision trees and the final result is the mean of individual trees results.
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Views: 1218
E2MATRIX RESEARCH LAB

THIS VIDEO SHOWS VERY EASY EXPLANATION OF NAIVE BAYES THEOREM WITH SIMPLE EXAMPLE

Views: 2737
yogesh murumkar

Views: 20078
Machine Learning- Sudeshna Sarkar

A visual description of Bayes' Theorem and the Naive Bayes algorithm, and an application to spam detection.
No previous knowledge is needed, aside from knowing how to multiply and divide, a visual mind and a desire to learn.

Views: 2861
Luis Serrano

Hii there from Codegency!
We are a team of young software developers and IT geeks who are always looking for challenges and ready to solve them, Feel free to contact us..
Do visit my instagram page and also like us on facebook, stay connected :)
Instagram: https://www.instagram.com/code_gency/
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Contact: +919769620035, +918108849398
For Blackbook Writeups & Descriptions: https://codegency.blogspot.in
For Latest Notes & References: https://sites.google.com/view/itscholar/home

Views: 1449
Codegency

In this Python for Data Science tutorial, You will learn about Naive Bayes classifier (Multinomial Bernoulli Gaussian) using scikit learn and Urllib in Python to how to detect Spam using Jupyter Notebook.
Multinomial Naive Bayes Classifier
Bernoulli Naive Bayes Classifier
Gaussian Naive Bayes Classifier
This is the 32th Video of Python for Data Science Course! In This series I will explain to you Python and Data Science all the time! It is a deep rooted fact, Python is the best programming language for data analysis because of its libraries for manipulating, storing, and gaining understanding from data. Watch this video to learn about the language that make Python the data science powerhouse. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Harvard Business Review named data scientist "the sexiest job of the 21st century." Python pandas is a commonly-used tool in the industry to easily and professionally clean, analyze, and visualize data of varying sizes and types. We'll learn how to use pandas, Scipy, Sci-kit learn and matplotlib tools to extract meaningful insights and recommendations from real-world datasets.
Download Link for Cars Data Set:
https://www.4shared.com/s/fWRwKoPDaei
Download Link for Enrollment Forecast:
https://www.4shared.com/s/fz7QqHUivca
Download Link for Iris Data Set:
https://www.4shared.com/s/f2LIihSMUei
https://www.4shared.com/s/fpnGCDSl0ei
Download Link for Snow Inventory:
https://www.4shared.com/s/fjUlUogqqei
Download Link for Super Store Sales:
https://www.4shared.com/s/f58VakVuFca
Download Link for States:
https://www.4shared.com/s/fvepo3gOAei
Download Link for Spam-base Data Base:
https://www.4shared.com/s/fq6ImfShUca
Download Link for Parsed Data:
https://www.4shared.com/s/fFVxFjzm_ca
Download Link for HTML File:
https://www.4shared.com/s/ftPVgKp2Lca

Views: 20740
TheEngineeringWorld

Take the Full Course of Artificial Intelligence
What we Provide
1) 28 Videos (Index is given down)
2)Hand made Notes with problems for your to practice
3)Strategy to Score Good Marks in Artificial Intelligence
Sample Notes : https://goo.gl/aZtqjh
To buy the course click
https://goo.gl/H5QdDU
if you have any query related to buying the course feel free to email us : [email protected]
Other free Courses Available :
Python : https://goo.gl/2gftZ3
SQL : https://goo.gl/VXR5GX
Arduino : https://goo.gl/fG5eqk
Raspberry pie : https://goo.gl/1XMPxt
Artificial Intelligence Index
1)Agent and Peas Description
2)Types of agent
3)Learning Agent
4)Breadth first search
5)Depth first search
6)Iterative depth first search
7)Hill climbing
8)Min max
9)Alpha beta pruning
10)A* sums
11)Genetic Algorithm
12)Genetic Algorithm MAXONE Example
13)Propsotional Logic
14)PL to CNF basics
15) First order logic solved Example
16)Resolution tree sum part 1
17)Resolution tree Sum part 2
18)Decision tree( ID3)
19)Expert system
20) WUMPUS World
21)Natural Language Processing
22) Bayesian belief Network toothache and Cavity sum
23) Supervised and Unsupervised Learning
24) Hill Climbing Algorithm
26) Heuristic Function (Block world + 8 puzzle )
27) Partial Order Planing
28) GBFS Solved Example

Views: 223922
Last moment tuitions

Views: 23796
Prabhudev Konana

MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: John Guttag
Prof. Guttag introduces supervised learning with nearest neighbor classification using feature scaling and decision trees.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

Views: 38956
MIT OpenCourseWare

Full course: https://www.udemy.com/data-science-and-machine-learning-with-python-hands-on/?couponCode=DATATUBE
We'll actually write a working spam classifier, using real email training data and a surprisingly small amount of code!

Views: 7759
Sundog Education with Frank Kane

A Simple Introduction To ML
More Videos on the Way.......
LIKE,SHARE,SUBSCRIBE
Credits-IIT Madras,NPTEL

Views: 49
Online sam

Machine Learning, Classification and Algorithms using MATLAB: Learn to Implement Classification Algorithms In One of the Most Power Tool used by Scientists and Engineer.
This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox.We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Output Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the essential ideas. The following are the course outlines.
Segment 1: Grabbing and Importing Dataset + Segment 2: K-Nearest Neighbor + Segment 3: Naive Bayes + Segment 4: Decision Trees + Segment 5: Discriminant Analysis + Segment 6: Support Vector Machines + Segment 7: Error Correcting Output Codes + Segment 8: Classification with Ensembles + Segment 9: Validation Methods + Segment 10: Evaluating Performance.

Views: 829
GeoEngineerings School

Graphical User Interface Tool to display and compare the performance metrics of 4 algorithms namely ANN, Naive Bayes, CART, KNN. Performance metrics considered for tool are Accuracy, ROC curve and execution time are shown in graphs. This is an in-class project for CS 5593 DATA MINING course at The University of Oklahoma

Views: 183
Anusha Saranam

simple example of Naive Bayes Algorithm in hindi

Views: 2343
Red Apple Tutorials