Search results “Naive bayes classifier text mining”

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

Views: 81360
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: 3323
Data Science Tutorials

This set of videos come from Andrew Ng's courses on Stanford OpenClassroom at http://openclassroom.stanford.edu/MainFolder/HomePage.php
OpenClassroom is the predecessor of the famous MOOC platform Coursera. However, some of these videos are not published in Coursera Machine Learning course, i.e., Newton's Methods, Naive Bayes, etc. We selected some of them to share with you.

Views: 18749
Wang Zhiyang

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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Views: 51499
HackerEarth

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
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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: 28061
Last moment tuitions

We show how to build a machine learning document classification system from scratch in less than 30 minutes using R. We use a text mining approach to identify the speaker of unmarked presidential campaign speeches. Applications in brand management, auditing, fraud detection, electronic medical records, and more.

Views: 156413
Timothy DAuria

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: 111367
Well Academy

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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Views: 11354
Simplilearn

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: 9048
Bharatendra Rai

Step by step on the next video
http://lazeybutexpert.com

Views: 1276
lazybutexpert tutorial

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

Views: 174494
Noureddin Sadawi

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: 101816
Last moment tuitions

Views: 17035
Mike Bernico

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: 11403
TheEngineeringWorld

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.
To learn more on Augmented Reality, IoT, Machine Learning FPGAs, Arduinos, PCB Design and Image Processing then Check out
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Views: 65553
Augmented Startups

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: 5680
macheads101

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: 60091
sentdex

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: 77732
Udacity

( 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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#NaiveBayes #NaiveBayesTutorial #DataScienceTraining #Datascience #Edureka
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Views: 35824
edureka!

** 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
Check out our playlist for more videos: http://bit.ly/2taym8X
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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.
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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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: 4148
edureka!

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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And please support me on Patreon:
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Views: 77001
Siraj Raval

This is PART 1 OF 3 videos that explains an example of how Naive Bayes classifies text documents and its implementation with scikit-learn.
The example has been adapted from the relevant portion of the textbook by Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008.
LINK TO THE RELEVANT PORTION (TEXT CLASSIFICATION WITH NAIVE BAYES): https://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html
This video has not been monetized and does not promote any product.

Views: 241
Abhishek Babuji

Views: 10870
GKMC datamining

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

Views: 3383
zaneacademy

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: 6367
Sourav Johar

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!
_About the channel_____________________
TL;DR
Awesome Data science with very little math!
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Hello I'm Jo the “Coding Maniac”!
On my channel I will show you how to make awesome things with Data Science. Further I will present you some short Videos covering the basic fundamentals about Machine Learning and Data Science like Feature Tuning, Over/Undersampling, Overfitting, ... with Python.
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Views: 13821
Coding-Maniac

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: 48118
BloomReach

This set of videos come from Andrew Ng's courses on Stanford OpenClassroom at http://openclassroom.stanford.edu/MainFolder/HomePage.php
OpenClassroom is the predecessor of the famous MOOC platform Coursera. However, some of these videos are not published in Coursera Machine Learning course, i.e., Newton's Methods, Naive Bayes, etc. We selected some of them to share with you.

Views: 32136
Wang Zhiyang

This video tutorial has been taken from Deep learning for NLP using Python. You can learn more and buy the full video course here [http://bit.ly/2sIocLw]
Find us on Facebook -- http://www.facebook.com/Packtvideo
Follow us on Twitter - http://www.twitter.com/packtvideo

Views: 103
Packt Video

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: 84896
sentdex

[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: 30197
Victor Lavrenko

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: 3723
Sundog Education

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: 13601
NISHANT KAUSHIK 14BCE0398

Classifying text by naïve bayes with arabic example

Views: 722
Mona Walaie

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
Data mining algorithms Playlist:
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book name: techmax publications datawarehousing and mining by arti deshpande n pallavi halarnkar
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NAIVE BAYES CLASSIFIER
In machine learning, naive Bayes classifiers 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

Views: 37666
fun 2 fun2code

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: 49067
Udacity

In this video we work on an actual sentiment analysis dataset (which is an instance of text classification), for which I also provide Python code (see below). The approach is very similar to something that is commonly called a Naive Bayes Classifier.
Website associated with this video:
http://karpathy.ca/mlsite/lecture2.php

Views: 49035
MLexplained

Part 2 of 2. This video discusses the classification of text in RapidMiner.
There are three types of classification:
- Decision Trees
- KNN
- Naive Bayes
in this video, we used Naive Bayes classification method.

Views: 1941
Alaa Khalid

The goal of text classification is the classification of text documents into a fixed number of predefined categories. Text classification has a number of applications ranging from email spam detection to providing news feed content to users based on user preferences.
In this session, we explore how to perform text classification using Spark’s Machine Learning Library (MLlib). We see how MLlib provides a set of high-level APIs for constructing, evaluating and tuning a machine learning workflow. We explore how Spark represents a workflow as a Pipeline, which consists of a sequence of stages to be run in a specific order. The Pipeline for our text classification use case utilizes Transformer stages to prepare the raw text documents for classification, and Estimator stages to learn a machine learning model that can be used to classify documents. Finally, we illustrate how to tune the model for best fit.
Although a document classification use case is specifically explored, many of the principles demonstrated in the session can be employed in a variety of other machine learning use cases.
Here's the link to the slides
https://ibm.box.com/s/atp4ezwvo5jr27zpxlu4987ercep2arn
And the link to the notebook as an .ipynb file.
https://ibm.box.com/s/spcj7f3uz6qetq8442mnvw5j264wbilj

Views: 9928
Data Gurus

59-minute beginner-friendly tutorial on text classification in WEKA; all text changes to numbers and categories after 1-2, so 3-5 relate to many other data analysis (not specifically text classification) using WEKA.
5 main sections:
0:00 Introduction (5 minutes)
5:06 TextToDirectoryLoader (3 minutes)
8:12 StringToWordVector (19 minutes)
27:37 AttributeSelect (10 minutes)
37:37 Cost Sensitivity and Class Imbalance (8 minutes)
45:45 Classifiers (14 minutes)
59:07 Conclusion (20 seconds)
Some notable sub-sections:
- Section 1 -
5:49 TextDirectoryLoader Command (1 minute)
- Section 2 -
6:44 ARFF File Syntax (1 minute 30 seconds)
8:10 Vectorizing Documents (2 minutes)
10:15 WordsToKeep setting/Word Presence (1 minute 10 seconds)
11:26 OutputWordCount setting/Word Frequency (25 seconds)
11:51 DoNotOperateOnAPerClassBasis setting (40 seconds)
12:34 IDFTransform and TFTransform settings/TF-IDF score (1 minute 30 seconds)
14:09 NormalizeDocLength setting (1 minute 17 seconds)
15:46 Stemmer setting/Lemmatization (1 minute 10 seconds)
16:56 Stopwords setting/Custom Stopwords File (1 minute 54 seconds)
18:50 Tokenizer setting/NGram Tokenizer/Bigrams/Trigrams/Alphabetical Tokenizer (2 minutes 35 seconds)
21:25 MinTermFreq setting (20 seconds)
21:45 PeriodicPruning setting (40 seconds)
22:25 AttributeNamePrefix setting (16 seconds)
22:42 LowerCaseTokens setting (1 minute 2 seconds)
23:45 AttributeIndices setting (2 minutes 4 seconds)
- Section 3 -
28:07 AttributeSelect for reducing dataset to improve classifier performance/InfoGainEval evaluator/Ranker search (7 minutes)
- Section 4 -
38:32 CostSensitiveClassifer/Adding cost effectiveness to base classifier (2 minutes 20 seconds)
42:17 Resample filter/Example of undersampling majority class (1 minute 10 seconds)
43:27 SMOTE filter/Example of oversampling the minority class (1 minute)
- Section 5 -
45:34 Training vs. Testing Datasets (1 minute 32 seconds)
47:07 Naive Bayes Classifier (1 minute 57 seconds)
49:04 Multinomial Naive Bayes Classifier (10 seconds)
49:33 K Nearest Neighbor Classifier (1 minute 34 seconds)
51:17 J48 (Decision Tree) Classifier (2 minutes 32 seconds)
53:50 Random Forest Classifier (1 minute 39 seconds)
55:55 SMO (Support Vector Machine) Classifier (1 minute 38 seconds)
57:35 Supervised vs Semi-Supervised vs Unsupervised Learning/Clustering (1 minute 20 seconds)
Classifiers introduces you to six (but not all) of WEKA's popular classifiers for text mining; 1) Naive Bayes, 2) Multinomial Naive Bayes, 3) K Nearest Neighbor, 4) J48, 5) Random Forest and 6) SMO.
Each StringToWordVector setting is shown, e.g. tokenizer, outputWordCounts, normalizeDocLength, TF-IDF, stopwords, stemmer, etc. These are ways of representing documents as document vectors.
Automatically converting 2,000 text files (plain text documents) into an ARFF file with TextDirectoryLoader is shown.
Additionally shown is AttributeSelect which is a way of improving classifier performance by reducing the dataset.
Cost-Sensitive Classifier is shown which is a way of assigning weights to different types of guesses.
Resample and SMOTE are shown as ways of undersampling the majority class and oversampling the majority class.
Introductory tips are shared throughout, e.g. distinguishing supervised learning (which is most of data mining) from semi-supervised and unsupervised learning, making identically-formatted training and testing datasets, how to easily subset outliers with the Visualize tab and more...
----------
Update March 24, 2014: Some people asked where to download the movie review data. It is named Polarity_Dataset_v2.0 and shared on Bo Pang's Cornell Ph.D. student page http://www.cs.cornell.edu/People/pabo/movie-review-data/ (Bo Pang is now a Senior Research Scientist at Google)

Views: 129089
Brandon Weinberg

This is PART 3 OF 3 videos that explains an example of how Naive Bayes classifies text documents and its implementation with scikit-learn.
The example has been adapted from the the relevant portion of the textbook by Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008.
LINK TO THE RELEVANT PORTION (TEXT CLASSIFICATION WITH NAIVE BAYES): https://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html
This video has not been monetized and does not promote any product.

Views: 44
Abhishek Babuji

( 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
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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: 58731
edureka!

#MachineLearningText #NLP #CountVectorizer #DataScience #ScikitLearn #TextFeatures #DataAnalytics #MachineLearning
Text cannot be used as an input to ML algorithms, therefore we use certain techniques to extract features from text. Count Vectorizer extracts features based on word count.
We then apply the features to Multinomial Naive bayes Classifier to classify Spam/ Non Spam messages.
For dataset and Ipython Notebooks.
GitHub: https://github.com/shreyans29/thesemicolon
Support us on Patreon : https://www.patreon.com/thesemicolon
Facebook: https://www.facebook.com/thesemicolon.code/

Views: 17896
The SemiColon

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

Views: 9295
Easy Engineering Classes

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

Views: 5487
Anuradha Bhatia

شرح مادة داتامايننك Naive Bayes Classifier

Views: 12911
Sudets1

#MachineLearningText #NLP #TFIDF #DataScience #ScikitLearn #TextFeatures #DataAnalytics #SpamFilter
Correction in video : TFIDF- Term Frequency Inverse Document Frequency.
Text cannot be used as an input to ML algorithms, therefore we use certain techniques to extract features from text. TFIDF Vectorizer extracts features based on word count giving less weightage to frequent words and more weigtage to rare words.
We then apply the features to Multinomial Naive bayes Classifier to classify Spam/ Non Spam messages.
For dataset and Ipython Notebooks.
GitHub: https://github.com/shreyans29/thesemicolon
Support us on Patreon : https://www.patreon.com/thesemicolon
Facebook: https://www.facebook.com/thesemicolon.code/

Views: 14742
The SemiColon

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