Search results “Data mining bayes classifier python”

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: 11427
TheEngineeringWorld

** 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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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.
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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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Views: 4251
edureka!

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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#NaiveBayes #MachineLearningAlgorithms #DataScienceCourse #DataScience #SimplilearnMachineLearning
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Why learn Machine Learning?
Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
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4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
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Views: 11479
Simplilearn

Naive Bayes Classifier- Fun and Easy Machine Learning
https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML
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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Views: 65671
Augmented Startups

( 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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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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Views: 35907
edureka!

The algorithm of choice, at least at a basic level, for text analysis is often the Naive Bayes classifier. Part of the reason for this is that text data is almost always massive in size. The Naive Bayes algorithm is so simple that it can be used at scale very easily with minimal process requirements.
Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1
sample code: http://pythonprogramming.net
http://hkinsley.com
https://twitter.com/sentdex
http://sentdex.com
http://seaofbtc.com

Views: 60145
sentdex

simple and easy explanation of Naive Bayes Algorithm in Hindi

Views: 5477
Red Apple Tutorials

This lecture continues to build on Bayes rule that we learned last time. We define training and testing data sets and build a Bayesian classifier. Specifically we will define prior, likelihood and posterior. We will express the posterior in terms of the likelihood and prior and apply this for text classification. We use titles and product descriptions from a retailer and attempt to find the top level category that the product is listed under. The likelihood corresponds to per category word frequencies and the prior correspond to the number of products under each category. We run into implementation issues such as laplacian smoothing, numerical instability etc which we deal with in a quick and hacky manner. But this lecture builds a full classifier from scratch in both the design and complete python implementation.

Views: 48155
BloomReach

Join me as I build a spam filtering bot using Python and Scikit-learn.
In this video, we are going to preprocess some data to make it suitable to train a model on.
Code is optimised for Python 2.
Download the dataset here: http://www.aueb.gr/users/ion/data/enron-spam/preprocessed/enron1.tar.gz
Part 2: https://youtu.be/6Wd1C0-3RXM
Entire code available here: https://gist.github.com/SouravJohar/bcbbad0d0b7e881cd0dca3481e32381f

Views: 6447
Sourav Johar

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

Views: 174537
Noureddin Sadawi

Simple example of the Naive Bayes classification algorithm

Views: 117787
Francisco Iacobelli

Homework 3 2a Naive Bayes Laplacian Smoothing ANSWER

Views: 14340
knowitvideos

In this video I will show you how to do text classification with machine learning using python, nltk, scikit and pandas. The concepts shown in this video will enable you to build your own models for your own use cases. So let's go!
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Views: 13866
Coding-Maniac

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

Views: 81429
Francisco Iacobelli

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.

Views: 3328
Data Science Tutorials

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Datamart in datawarehouse :https://goo.gl/rzE7SJ
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decision tree with solved example:https://goo.gl/nNTFJ3
K mean clustering algorithm:https://goo.gl/9gGGu5
Introduction to data mining and architecture:https://goo.gl/8dUADv
Naive bayes classifier:https://goo.gl/jVUNyc
Apriori Algorithm:https://goo.gl/eY6Kbx
Agglomerative clustering algorithmn:https://goo.gl/8ktMss
KDD in data mining :https://goo.gl/K2vvuJ
ETL process:https://goo.gl/bKnac9
FP TREE Algorithm:https://goo.gl/W24ZRF
Decision tree:https://goo.gl/o3xHgo
more videos coming soon so channel ko subscribe karke rakho

Views: 102030
Last moment tuitions

Quick tutorial using a sample data set on running a Naive Bayes Classifier in Pyspark.

Views: 452
MSBA Group5

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

Views: 5691
macheads101

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: 3758
Sundog Education

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

Views: 892
zaneacademy

We'll build a Spam Detector using a machine learning model called a Naive Bayes Classifier! This is our first real dip into probability theory in the series; I'll talk about the types of probability, then we'll use Bayes Theorem to help us build our classifier.
Code for this video:
https://github.com/llSourcell/naive_bayes_classifier/
Hammad's Winning Code:
https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/tree/master/Principal%20Component%20Analysis
Kristian's Runner up Code:
https://github.com/kwichmann/PCA_and_autoencoders
Please Subscribe! And like. And comment. That's what keeps me going.
More Learning Resources:
http://machinelearningmastery.com/naive-bayes-tutorial-for-machine-learning/
http://blog.datumbox.com/machine-learning-tutorial-the-naive-bayes-text-classifier/
http://machinelearningmastery.com/naive-bayes-classifier-scratch-python/
https://www.analyticsvidhya.com/blog/2015/09/naive-bayes-explained/
https://www.youtube.com/watch?v=psHrcSacU9Y
https://hackernoon.com/how-to-build-a-simple-spam-detecting-machine-learning-classifier-4471fe6b816e
https://www.autonlab.org/tutorials/naive.html
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Views: 77045
Siraj Raval

Skipper Seabold
http://www.pyvideo.org/video/3548/classification-using-pandas-and-scikit-learn
This will be a tutorial-style talk demonstrating how to use pandas and scikit-learn to do classification tasks. We will do some data munging and visualization using pandas and matplotlib. Then we will introduce some of the different classifiers in scikit-learn and show how to include them into a classification pipeline to produce the best predictive model. Interactive IPython/Jupyter notebooks will be provided.

Views: 35286
Next Day Video

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

Views: 17058
Mike Bernico

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

Views: 9474
Easy Engineering Classes

The Facebook page:
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My Blog to download books:
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Views: 2379
The New Edge

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.

Views: 43407
Udacity

Views: 12278
Machine Learning- Sudeshna Sarkar

In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3.

Views: 338101
Thales Sehn Körting

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

Views: 5520
Anuradha Bhatia

Implementation of Naive Bayes Classifier in R using dataset mushroom from the UCI repository.
You may wanna add pakages e1071 and rminer in R because they were not present in R x64 3.3.1 by default.
Music - Daft Punk - Instant Crush ft. Julian Casblancas

Views: 13620
NISHANT KAUSHIK 14BCE0398

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: 9124
Bharatendra Rai

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.

Views: 58916
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: 26438
Victor Lavrenko

Now that we understand some of the basics of of natural language processing with the Python NLTK module, we're ready to try out text classification. This is where we attempt to identify a body of text with some sort of label.
To start, we're going to use some sort of binary label. Examples of this could be identifying text as spam or not, or, like what we'll be doing, positive sentiment or negative sentiment.
Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1
sample code: http://pythonprogramming.net
http://hkinsley.com
https://twitter.com/sentdex
http://sentdex.com
http://seaofbtc.com

Views: 85017
sentdex

** 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
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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]
Call us at US: +18336900808 (Toll Free) or India: +918861301699
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edureka!

This playlist/video has been uploaded for Marketing purposes and contains only selective videos.
For the entire video course and code, visit [http://bit.ly/2FtnxpQ].
Naive Bayes is an algorithm that uses probability to classify the data according to Bayes theorem for the strong independence of the features. Bayes theorem estimates the probability of an event based on prior conditions. So, overall, we will use a set of feature values to estimate a value assuming the same conditions hold true when those features have similar values. Also, we will implement naive Bayes using the R programming language.
• Install the package and load the library
• Measure the accuracy of the model
• Determine the accuracy of the model
For the latest Big Data and Business Intelligence tutorials, please visit
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Packt Video

Sample Notes : https://drive.google.com/file/d/19xmuQO1cprKqqbIVKcd7_-hILxF9yfx6/view?usp=sharing
for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3
For full course:https://goo.gl/bYbuZ2
More videos coming soon so Subscribe karke rakho : https://goo.gl/85HQGm
for full notes
please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2
For full hand made notes of data warehouse and data mining its only 200 rs
payment options is PAYTM :7038604912
once we get payment notification we will mail you the notes on your email id
contact us at :[email protected]
For full course :https://goo.gl/Y1UcLd
Topic wise:
Introduction to Datawarehouse:https://goo.gl/7BnSFo
Meta data in 5 mins :https://goo.gl/7aectS
Datamart in datawarehouse :https://goo.gl/rzE7SJ
Architecture of datawarehouse:https://goo.gl/DngTu7
how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT
what is Olap operation :https://goo.gl/RYQEuN
OLAP vs OLTP:https://goo.gl/hYL2kd
decision tree with solved example:https://goo.gl/nNTFJ3
K mean clustering algorithm:https://goo.gl/9gGGu5
Introduction to data mining and architecture:https://goo.gl/8dUADv
Naive bayes classifier:https://goo.gl/jVUNyc
Apriori Algorithm:https://goo.gl/eY6Kbx
Agglomerative clustering algorithmn:https://goo.gl/8ktMss
KDD in data mining :https://goo.gl/K2vvuJ
ETL process:https://goo.gl/bKnac9
FP TREE Algorithm:https://goo.gl/W24ZRF
Decision tree:https://goo.gl/o3xHgo
more videos coming soon so channel ko subscribe karke rakho

Views: 124688
Last moment tuitions

In this video we have explain the basic concept of Naive Bayes and some concept of condition probability and solved an Example of Naive bayes in which we try to find out the prediction and its also help in classification
Sample Notes : https://drive.google.com/file/d/19xmuQO1cprKqqbIVKcd7_-hILxF9yfx6/view?usp=sharing
for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3
For full course:https://goo.gl/bYbuZ2
More videos coming soon so Subscribe karke rakho : https://goo.gl/85HQGm
for full notes
please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2
For full hand made notes of data warehouse and data mining its only 200 rs
payment options is PAYTM :7038604912
once we get payment notification we will mail you the notes on your email id
contact us at :[email protected]
For full course :https://goo.gl/Y1UcLd
Topic wise:
Introduction to Datawarehouse:https://goo.gl/7BnSFo
Meta data in 5 mins :https://goo.gl/7aectS
Datamart in datawarehouse :https://goo.gl/rzE7SJ
Architecture of datawarehouse:https://goo.gl/DngTu7
how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT
what is Olap operation :https://goo.gl/RYQEuN
OLAP vs OLTP:https://goo.gl/hYL2kd
decision tree with solved example:https://goo.gl/nNTFJ3
K mean clustering algorithm:https://goo.gl/9gGGu5
Introduction to data mining and architecture:https://goo.gl/8dUADv
Naive bayes classifier:https://goo.gl/jVUNyc
Apriori Algorithm:https://goo.gl/eY6Kbx
Agglomerative clustering algorithmn:https://goo.gl/8ktMss
KDD in data mining :https://goo.gl/K2vvuJ
ETL process:https://goo.gl/bKnac9
FP TREE Algorithm:https://goo.gl/W24ZRF
Decision tree:https://goo.gl/o3xHgo
more videos coming soon so channel ko subscribe karke rakho

Views: 28230
Last moment tuitions

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

Views: 3434
zaneacademy

Support Vector Machine (SVM) - Fun and Easy Machine Learning
https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples.
To understand SVM’s a bit better, Lets first take a look at why they are called support vector machines. So say we got some sample data over here of features that classify whether a observed picture is a dog or a cat, so we can for example look at snout length or and ear geometry if we assume that dogs generally have longer snouts and cat have much more pointy ear shapes.
So how do we decide where to draw our decision boundary?
Well we can draw it over here or here or like this. Any of these would be fine, but what would be the best? If we do not have the optimal decision boundary we could incorrectly mis-classify a dog with a cat. So if we draw an arbitrary separation line and we use intuition to draw it somewhere between this data point for the dog class and this data point of the cat class.
These points are known as support Vectors – Which are defined as data points that the margin pushes up against or points that are closest to the opposing class. So the algorithm basically implies that only support vector are important whereas other training examples are ‘ignorable’. An example of this is so that if you have our case of a dog that looks like a cat or cat that is groomed like a dog, we want our classifier to look at these extremes and set our margins based on these support vectors.
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To learn more on Augmented Reality, IoT, Machine Learning FPGAs, Arduinos, PCB Design and Image Processing then Check out
http://www.arduinostartups.com/
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Views: 84947
Augmented Startups

( 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: 58752
edureka!

[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.

Views: 30252
Victor Lavrenko

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: 3026
DataMining Tutorials

Links:
1) https://www.youtube.com/watch?v=Ea6CVk1Uiac&index=21&list=PLh6SAYydrIpc6eezCoBpPjBv0jtXbG4rJ

Views: 1411
MachineLearningGod

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