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
-~-~~-~~~-~~-~-

Views: 157459
Well Academy

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

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Easy Engineering Classes

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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Augmented Startups

simple and easy explanation of Naive Bayes Algorithm in Hindi

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Red Apple Tutorials

Naive Bayes Classification Algorithm – Solved Numerical Question 2 in Hindi
Data Warehouse and Data Mining Lectures in Hindi

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Easy Engineering Classes

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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HackerEarth

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.
****************************************************************************************************************************************
All data mining algorithm videos
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5 Minutes Engineering

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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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Ranji Raj

أهلا وسهلا فيكم بالدرس الثاني من سلسلة شروحات خوارزميات التنقيب عن البيانات - خوارزمية الـ Naive Bayes .. بتمنى يكون الشرح واضح وإذا في عندكم أي تساؤل جاهزين إنشالله.
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ماتنسوا تشاركوا السلسلة مع زملائكم وتدعموا القناة لنستمر بالعطاء ^^
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Knowledge Network

** Machine Learning Training with Python: https://www.edureka.co/python **
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:
1. What is Naive Bayes?
2. Bayes Theorem and its use
3. Mathematical Working of Naive Bayes
4. Step by step Programming in Naive Bayes
5. Prediction Using Naive Bayes
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Data Science is a set of techniques that enable the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.
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edureka!

Naive Bayes | Naive Bayes Algorithm | Naive Bayes Algorithm In Data Mining
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Learning With Mahamud

Naive bayes classifier: naive bayes algorithm with Example
Naive bayes example for machine learning and data mining .
naive bayes classifier example- naive bayes classification - naive bayes machine learning
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Naïve Bayes Classifier - Fun and Easy Machine Learning
Naive Bayes - Georgia Tech - Machine Learning
Naive Bayes Theorem | Introduction to Naive Bayes Theorem | Machine Learning Classification

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Online Courses

Data Warehouse and Mining
For more: http://www.anuradhabhatia.com

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Anuradha Bhatia

شرح مادة داتامايننك Naive Bayes Classifier

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Sudets1

( Data Science Training - https://www.edureka.co/data-science )
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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2. Introduction to Classification
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4. What is Naive Bayes?
5. Use Cases of Naive Bayes
6. Demo – Employee Salary Prediction in R
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edureka!

Qualification for probabilities from admission predict and diabetes dataset
Music: Joakim Karud - Classic

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Muhammad Fadhiil Rachman

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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Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Naive-Bayes-Classifier-l3dZ6ZNFjo0&utm_medium=Tutorials&utm_source=youtube
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Simplilearn

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Noureddin Sadawi

#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.
Thank You!!
Visit Again!! 😇

Views: 7487
Code Wrestling

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

Views: 93575
Francisco Iacobelli

Views: 22457
Victor Lavrenko

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: 17759
Bharatendra Rai

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

Views: 33718
Saurabh Singh

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 :)
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Codegency

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):
http://goo.gl/QldvyV
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 19282
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: 5426
DataMining Tutorials

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

Views: 779
yogesh murumkar

Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-478818537/m-482228628
Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262
Georgia Tech online Master's program: https://www.udacity.com/georgia-tech

Views: 97576
Udacity

simple example of Naive Bayes Algorithm in hindi

Views: 2127
Red Apple Tutorials

We have implemented Text Classification in Python using Naive Bayes Classifier. It explains the text classification algorithm from beginner to pro.
For understanding the co behind it, refer:
https://www.youtube.com/watch?v=Zt83JnjD8zg
Here, we have used 20 Newsgroup dataset to train our model for the classification.
Link to download the 20 Newsgroup dataset:
http://qwone.com/~jason/20Newsgroups/20news-bydate.tar.gz
Packages used here are:
1. sklearn
2. Tfidf Vectorizer
3. Multinomial Naive Bayes Classifier
4. Pipeline
5. Metrics
Refer the entire code at:
https://github.com/codewrestling/TextClassification/blob/master/Text%20Classification.py
For slides, refer:
https://github.com/codewrestling/TextClassification/raw/master/Text%20Classification.pdf
Follow us on Github for more codes:
https://github.com/codewrestling
machine learning python beginner,machine learning python basics,machine learning python regression,machine learning game python,machine learning applications python

Views: 4326
Code Wrestling

Simple example of the Naive Bayes classification algorithm

Views: 128121
Francisco Iacobelli

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

Views: 1534
Mahmudul Hasan

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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E2MATRIX RESEARCH LAB

Simple explanation of Bayes' Theorem for Naive Bayes Algorithm in hindi

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Red Apple Tutorials

In this third video text analytics in R, I've talked about modeling process using the naive bayes classifier that helps us creating a statistical text classifier model which helps classifying the data in ham or spam sms message. You will see how you can tune the parameters also and make the best use of naive bayes classifier model.

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Data Science Tutorials

Here are some of the most commonly used classification algorithms -- Logistic Regression, Naïve Bayes, Stochastic Gradient Descent, K-Nearest Neighbours, Decision Tree, Random Forest and Support Vector Machine.
https://analyticsindiamag.com/7-types-classification-algorithms/
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Analytics India Magazine

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

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TheEngineeringWorld

My web page:
www.imperial.ac.uk/people/n.sadawi

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Noureddin Sadawi

Website + download source code @ http://www.zaneacademy.com

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zaneacademy

[http://bit.ly/N-Bayes] How can we distinguish spam from non-spam with a Naive Bayes classifier? We estimate the priors and multiple Bernoulli distributions for each class. Also learn how Naive Bayes can misclassify its own training examples.

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Victor Lavrenko

Views: 18934
Machine Learning- Sudeshna Sarkar

[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: 30699
Victor Lavrenko

In this video, I show how to use Bayes classifiers to determine if a piece of text is "positive" or "negative". In other words, I show you how to make a program with feelings!
The kind of classifier I show is called a Bernoulli naive Bayes classifier:
https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Bernoulli_naive_Bayes
The demo at the beginning of the video can be found at:
http://macheads101.com/demos/sentiment/
The source for the demo, as well as for my program to graph the mood over books, can be found here:
https://github.com/unixpickle/sentigraph

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macheads101

Take the Full Course of Artificial Intelligence
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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

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Last moment tuitions

This tutorial starts with introduction of Dataset. All aspects of dataset are discussed. Then basic working of RapidMiner is discussed. Once the viewer is acquainted with the knowledge of dataset and basic working of RapidMiner, following operations are performed on the dataset.
K-NN Classification
Naïve Bayes Classification
Decision Tree
Association Rules

Views: 36418
RapidMinerTutorial

Naive Bayes Algorithm || Naive Bayes Algorithm Types 1 with example in Hindi || Dataware house and Mining || rst
All topics of Dataware House And Mining (DWM) will be covered in these series of videos.
All videos here are for all students and teachers form beginner to expert level.
All subjects solution are explained here in easy and simple way.
We are Rising Scholars Tutorial (RST) team.
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Rising Scholars Tutorial (RST)