Search results “Data mining bayes classifier decision”

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.
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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 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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شرح مادة داتامايننك Naive Bayes Classifier

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Sudets1

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

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

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Saurabh Singh

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.
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** 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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Muhammad Iqbal
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Data Warehouse and Mining
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Anuradha Bhatia

[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).

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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.
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Code Wrestling

Book: Introduction to Statistical Learning - with Applications in R
http://www-bcf.usc.edu/~gareth/ISL/

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MachineLearningGod

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

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GKMC datamining

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

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yogesh murumkar

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.

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Luis Serrano

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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Pattern Recognition and Application by Prof. P.K. Biswas,Department of Electronics & Communication Engineering,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in

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nptelhrd

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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simple and easy explanation of Naive Bayes Algorithm in Hindi

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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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Bayesian Network in Artificial Intelligence | Bayesian Belief Network | https://youtu.be/0U5xH4b7nPc
Decision Tree Learning using ID3 Algorithm | Artificial intelligence https://youtu.be/pvTejBgiF3I
Supervised Learning and Unsupervised Learning | Learning in Artificial Intelligence https://youtu.be/Wn2JgBfAsSM
Genetic Algorithm | Artificial Intelligence Tutorial in Hindi Urdu https://youtu.be/frB2zIpOOBk
Comparison of Search Algorithm https://youtu.be/QMz7jwXDvwg
Resolution in Artificial Intelligence | Resolution Rules in AI https://youtu.be/oQmqJPLqHZA
Inference rules in Predicate logic https://youtu.be/Y8KCh4VRRwM
Predicate logic in AI | First order logic in Artificial Intelligence https://youtu.be/sFINpc5KA3E
Wumpus World Proving | Propositional logic Example https://youtu.be/bDu9iNJ8h58
PROPOSITIONAL LOGIC | Artificial Intelligence https://youtu.be/oUR11UUIDvA
Knowledge based Agents | Logical agents https://youtu.be/Y7CS-1BfA6o
Alpha Beta Pruning | Problem #2 https://youtu.be/QL-g1FDls74
A Decision tree represents a function that takes as input a vector of attribute values and returns a “decision”—a single output value.
The input and output values can be discrete or continuous.
A decision tree reaches its decision by performing a sequence of tests.
There are many specific decision-tree algorithms. Notable ones include:
ID3 (Iterative Dichotomiser 3)
C4.5 (successor of ID3)
CART (Classification And Regression Tree)
CHAID (Chi-squared Automatic Interaction Detector). Performs multi-level splits when computing classification trees.
MARS: extends decision trees to handle numerical data better.
ID3 is one of the most common decision tree algorithm
Dichotomisation means dividing into two completely opposite things.
Algorithm iteratively divides attributes into two groups which are the most dominant attribute and others to construct a tree.
Then, it calculates the Entropy and Information Gains of each attribute. In this way, the most dominant attribute can be founded.
After then, the most dominant one is put on the tree as decision node.
Entropy and Gain scores would be calculated again among the other attributes.
Procedure continues until reaching a decision for that branch.
algorithm steps:
Calculate the entropy of every attribute using the data set S
Entropy(S) = ∑ – p(I) . log2p(I)
Split the set S into subsets using the attribute for which the resulting entropy (after splitting) is minimum (or, equivalently, information gain is maximum)
Gain(S, A) = Entropy(S) – ∑ [ p(S|A) . Entropy(S|A) ]
Make a decision tree node containing that attribute
Recurse on subsets using remaining attributes.
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Ask Faizan

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Victor Lavrenko

In this video we have explained Bayesian Classification that includes Bayes Theorem, along with the concept of hypothesis followed by Naive-Bayes classification. The video also includes the different measures for predicting performance of the classifier so made. and also include confusion matrix
Ml full notes rupees 200 only
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Machine Learning- Sudeshna Sarkar

( 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:
1. What is Machine Learning?
2. Introduction to Classification
3. Classification Algorithms
4. What is Naive Bayes?
5. Use Cases of Naive Bayes
6. Demo – Employee Salary Prediction in R
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How it Works?
1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get Lifetime Access to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate!
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About the Course
Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
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Why Learn Data Science?
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
After the completion of the Data Science course, you should be able to:
1. Gain insight into the 'Roles' played by a Data Scientist
2. Analyse Big Data using R, Hadoop and Machine Learning
3. Understand the Data Analysis Life Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
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Who should go for this course?
The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. Information Architects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies
For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Customer Reviews:
Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best."

Views: 50137
edureka!

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: 7157
DataMining Tutorials

Creating a data mining structure with two models - Naive Bayes and Decision Trees

Views: 565
Ben KIM

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: 45616
MIT OpenCourseWare

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Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3"
https://www.youtube.com/watch?v=GS3HKR6CV8E
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Views: 217143
Well Academy

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: 25319
Bharatendra Rai

In this video we have explain the concept of Rule based Classifier in hindi
Ml full notes rupees 200 only
ML notes form : https://goo.gl/forms/7rk8716Tfto6MXIh1
Machine learning introduction : https://goo.gl/wGvnLg
Machine learning #2 : https://goo.gl/ZFhAHd
Machine learning #3 : https://goo.gl/rZ4v1f
Linear Regression in Machine Learning : https://goo.gl/7fDLbA
Logistic regression in Machine learning #4.2 : https://goo.gl/Ga4JDM
decision tree : https://goo.gl/Gdmbsa
K mean clustering algorithm : https://goo.gl/zNLnW5
Agglomerative clustering algorithmn : https://goo.gl/9Lcaa8
Apriori Algorithm : https://goo.gl/hGw3bY
Naive bayes classifier : https://goo.gl/JKa8o2

Views: 16929
Last moment tuitions

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: 1777
GeoEngineerings School

( Data Science Training - https://www.edureka.co/data-science )
Watch sample class recording: http://www.edureka.co/data-science?utm_source=youtube&utm_medium=referral&utm_campaign=naive-bayes-classifier-15
Data science is the study of the generalizable extraction of knowledge from data, yet the key word is science. The tutorial wil give a brief understanding about Data Science.
The topics covered in the video:
1.Naive Bayes Classifier in r
2.Naive Bayes Classifier
3.Naive Bayes Classifier Overview
4.Naive Bayes Classifier Example
5.Probability Model for Classifier
6.Bayes Theorem
7.ROC Receiver Operating Characteristic
Related Posts:
http://www.edureka.co/blog/who-can-take-up-a-data-science-tutorial/?utm_source=youtube&utm_medium=referral&utm_campaign=naive-bayes-classifier-15
http://www.edureka.co/blog/enroll-for-a-data-science-course/?utm_source=youtube&utm_medium=referral&utm_campaign=naive-bayes-classifier-15
http://www.edureka.co/blog/types-of-data-scientists/?utm_source=youtube&utm_medium=referral&utm_campaign=naive-bayes-classifier-15
http://www.edureka.co/blog/core-data-scientist-skills/?utm_source=youtube&utm_medium=referral&utm_campaign=naive-bayes-classifier-15
Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would prefer a hassle free and self paced learning environment, accessible from any part of the world.
‘Naive Bayes Classifier’ have been widely covered in our course ‘Data Science’.
For more information, please write back to us at [email protected]
Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004

Views: 61857
edureka!

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Naive Bayes Theorem 2018
Naive Baye
SÖZ
Çarpışma 1. Bölüm
LİNÇ@
KAFALAR KARIŞIK
Sen Anlat KAradeniz

Views: 157
Sheely Sensgton

Views: 24525
Victor Lavrenko

simple example of Naive Bayes Algorithm in hindi

Views: 3045
Red Apple Tutorials

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: 25445
TheEngineeringWorld

** Machine Learning with Python : https://www.edureka.co/machine-learning-certification-training **
This Edureka video on Decision Tree Algorithm in Python will take you through the fundamentals of decision tree machine learning algorithm concepts and its demo in Python. Below are the topics covered in this tutorial:
1. What is Classification?
2. Types of Classification
3. Classification Use Case
4. What is Decision Tree?
5. Decision Tree Terminology
6. Visualizing a Decision Tree
7 Writing a Decision Tree Classifier fro Scratch in Python using CART Algorithm
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check out our Python Machine Learning Playlist: https://goo.gl/UxjTxm
#decisiontree #decisiontreepython #machinelearningalgorithms
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About the Course
Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience.
After completing this Machine Learning Certification Training using Python, you should be able to:
Gain insight into the 'Roles' played by a Machine Learning Engineer
Automate data analysis using python
Describe Machine Learning
Work with real-time data
Learn tools and techniques for predictive modeling
Discuss Machine Learning algorithms and their implementation
Validate Machine Learning algorithms
Explain Time Series and it’s related concepts
Gain expertise to handle business in future, living the present
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Why learn Machine Learning with Python?
Data Science is a set of techniques that enables 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.
For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

Views: 84057
edureka!

© 2019 Swiftmailer transport options

Business continuity resources may include spare or redundant systems that serve as a backup in case primary systems fail. Systems for crisis communications may include existing voice and data technology for communicating with customers, employees and others. Equipment. Equipment includes the means for teams to communicate. Radios, smartphones, wired telephone and pagers may be required to alert team members to respond, to notify public agencies or contractors and to communicate with other team members to manage an incident. Many tools may be required to prepare a facility for a forecast event such as a hurricane, flooding or severe winter storm. Materials and Supplies. Materials and supplies are needed to support members of emergency response, business continuity and crisis communications teams. Food and water are basic provisions. Systems and equipment needed to support the preparedness program require fuel. Emergency generators and diesel engine driven fire pumps should have a fuel supply that meets national standards or local regulatory requirements. That means not allowing the fuel supply to run low because replenishment may not be possible during an emergency. Spare batteries for portable radios and chargers for smartphones and other communications devices should be available. Funding. Worksheets.