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Weka Tutorial 02: Data Preprocessing 101 (Data Preprocessing)
 
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This tutorial demonstrates various preprocessing options in Weka. However, details about data preprocessing will be covered in the upcoming tutorials.
Views: 162641 Rushdi Shams
How to process text files with RapidMiner
 
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In this video I process transcriptions from Hugo Chavez's TV programme "Alo Presidente" to find patterns in his speech. Watching this video you will learn how to: -Download several documents at once from a webpage using a Firefox plugin. - Batch convert pdf files to text using a very simple script and a java application. - Process documents with Rapid Miner using their association rules feature to find patterns in them.
Views: 35320 Alba Madriz
Data Science Tutorial | Text analytics with R | Cleaning Data and Creating Document Term Matrix
 
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In this Data Science Tutorial video, I have talked about how you can use the tm package in R. tm package is text mining package in r for doing the text mining. Here in this r Programming tutorial video, we have discussed about how to create corpus of data, clean it and then create document term matrix to study each and every important word from the dataset. In the next video, I'll talk about how to do modeling from this data. Link to the text spam csv file - https://drive.google.com/open?id=0B8jkcc4fRf35c3lRRC1LM3RkV0k
Advanced Data Mining with Weka (3.5: Using R to preprocess data)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 5: Using R to preprocess data http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/8yXNiM https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 1881 WekaMOOC
Code Preprocessing
 
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We're often familiar with the concept of compilation, but turns out, that is often not the only procedure done to code! The preprocessor is another very powerful tool that transforms code as text! In this video, we take a look at preprocessors designed for, and applied in several different contexts, from the very classic C preprocessor to another very classic example, PHP. We'll also look at more modern variants such as Flask for webhosting, and SASS for generation of CSS. = 0612 TV = 0612 TV, a sub-project of NERDfirst.net, is an educational YouTube channel. Started in 2008, we have now covered a wide range of topics, from areas such as Programming, Algorithms and Computing Theories, Computer Graphics, Photography, and Specialized Guides for using software such as FFMPEG, Deshaker, GIMP and more! Enjoy your stay, and don't hesitate to drop me a comment or a personal message to my inbox =) If you like my work, don't forget to subscribe! Like what you see? Buy me a coffee → http://www.nerdfirst.net/donate/ 0612 TV Official Writeup: http://nerdfirst.net/0612tv More about me: http://about.me/lcc0612 Official Twitter: http://twitter.com/0612tv = NERDfirst = NERDfirst is a project allowing me to go above and beyond YouTube videos into areas like app and game development. It will also contain the official 0612 TV blog and other resources. Watch this space, and keep your eyes peeled on this channel for more updates! http://nerdfirst.net/ ----- Disclaimer: Please note that any information is provided on this channel in good faith, but I cannot guarantee 100% accuracy / correctness on all content. Contributors to this channel are not to be held responsible for any possible outcomes from your use of the information.
Import Data and Analyze with MATLAB
 
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Data are frequently available in text file format. This tutorial reviews how to import data, create trends and custom calculations, and then export the data in text file format from MATLAB. Source code is available from http://apmonitor.com/che263/uploads/Main/matlab_data_analysis.zip
Views: 360413 APMonitor.com
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
 
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Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language. The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text. NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more! Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK! 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: 421650 sentdex
Data Mining with Weka (1.4: Building a classifier)
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 4: Building a classifier http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 78430 WekaMOOC
More Data Mining with Weka (2.4: Document classification)
 
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More Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 4: Document classification http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/QldvyV https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 7655 WekaMOOC
Data Mining with Weka (5.4: Summary)
 
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Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 4: Summary http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/5DW24X https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 11305 WekaMOOC
More Data Mining with Weka (2.3: Discretization in J48)
 
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More Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 3: Discretization in J48 http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/QldvyV https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 9167 WekaMOOC
PDF Data Scraping
 
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Automated web scraping services provide fast data acquirement in structured format. No matter if used for big data, data mining, artificial intelligence, machine learning or business intelligence applications. The scraped data come from various sources and forms. It can be websites, various databases, XML feeds and CSV, TXT or XLS file formats for example. Billions of PDF files stored online form a huge data library worth scraping. Have you ever tried to get any data from various PDF files? Then you know how panful it is. We have created an algorithm that allows you to extract data in an easily readable structured way. With PDFix we can recognize all logical structures and we can give you a hierarchical structure of document elements in a correct reading order. With the PDFix SDK we believe your web crawler can be programmed to access the PDF files and: - Search Text inside PDFs – you can find and extract specific information - Detect and Export Tables - Extract Annotations - Detect and Extract Related Images - Use Regular Expression, Pattern Matching - Detect and Scrape information from Charts Structured format You will need the scraped data from PDFs in various formats. With the PDFix you will get a structured output in: - CSV - HTML - XML - JSON
Views: 192 Team PDFix
Advanced Data Mining with Weka (3.4: Using R to run a classifier)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 4: Using R to run a classifier http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/8yXNiM https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 2598 WekaMOOC
Text Mining (part 1)  -  Import Text into R (single document)
 
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Text Mining with R. Import a single document into R.
Views: 17496 Jalayer Academy
Keyword Extraction With Machine Learning - Part I: Introduction
 
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Please watch: "Why NEURALINK is a Really Bad Idea" https://www.youtube.com/watch?v=KYdpEH8wuUc --~-- We dive in how to apply a sequence to sequence machine learning model to automated keyword extraction on short text. Source [1] Automated Keyword Extraction – TF-IDF, RAKE, and TextRank https://goo.gl/LtAC8U Machine Learning for Absolute Beginners: A Plain English Introduction https://goo.gl/oEBXna Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems https://goo.gl/sZYoVr Contact: Website : http://www.theapemachine.com Twitter : https://twitter.com/ApeMachineGames Facebook : https://www.facebook.com/theapemachine/ Slack : https://goo.gl/uC4HaH Discord : https://discord.gg/pTWFCkN
Views: 2238 The Ape Machine
R PROGRAMMING TEXT MINING TUTORIAL
 
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Learn how to perform text analysis with R Programming through this amazing tutorial! Podcast transcript available here - https://www.superdatascience.com/sds-086-computer-vision/ Natural languages (English, Hindi, Mandarin etc.) are different from programming languages. The semantic or the meaning of a statement depends on the context, tone and a lot of other factors. Unlike programming languages, natural languages are ambiguous. Text mining deals with helping computers understand the “meaning” of the text. Some of the common text mining applications include sentiment analysis e.g if a Tweet about a movie says something positive or not, text classification e.g classifying the mails you get as spam or ham etc. In this tutorial, we’ll learn about text mining and use some R libraries to implement some common text mining techniques. We’ll learn how to do sentiment analysis, how to build word clouds, and how to process your text so that you can do meaningful analysis with it.
Views: 2526 SuperDataScience
Text Mining Example Using RapidMiner
 
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Explains how text mining can be performed on a set of unstructured data
Views: 13733 Gautam Shah
R tutorial: What is text mining?
 
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Learn more about text mining: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words Hi, I'm Ted. I'm the instructor for this intro text mining course. Let's kick things off by defining text mining and quickly covering two text mining approaches. Academic text mining definitions are long, but I prefer a more practical approach. So text mining is simply the process of distilling actionable insights from text. Here we have a satellite image of San Diego overlaid with social media pictures and traffic information for the roads. It is simply too much information to help you navigate around town. This is like a bunch of text that you couldn’t possibly read and organize quickly, like a million tweets or the entire works of Shakespeare. You’re drinking from a firehose! So in this example if you need directions to get around San Diego, you need to reduce the information in the map. Text mining works in the same way. You can text mine a bunch of tweets or of all of Shakespeare to reduce the information just like this map. Reducing the information helps you navigate and draw out the important features. This is a text mining workflow. After defining your problem statement you transition from an unorganized state to an organized state, finally reaching an insight. In chapter 4, you'll use this in a case study comparing google and amazon. The text mining workflow can be broken up into 6 distinct components. Each step is important and helps to ensure you have a smooth transition from an unorganized state to an organized state. This helps you stay organized and increases your chances of a meaningful output. The first step involves problem definition. This lays the foundation for your text mining project. Next is defining the text you will use as your data. As with any analytical project it is important to understand the medium and data integrity because these can effect outcomes. Next you organize the text, maybe by author or chronologically. Step 4 is feature extraction. This can be calculating sentiment or in our case extracting word tokens into various matrices. Step 5 is to perform some analysis. This course will help show you some basic analytical methods that can be applied to text. Lastly, step 6 is the one in which you hopefully answer your problem questions, reach an insight or conclusion, or in the case of predictive modeling produce an output. Now let’s learn about two approaches to text mining. The first is semantic parsing based on word syntax. In semantic parsing you care about word type and order. This method creates a lot of features to study. For example a single word can be tagged as part of a sentence, then a noun and also a proper noun or named entity. So that single word has three features associated with it. This effect makes semantic parsing "feature rich". To do the tagging, semantic parsing follows a tree structure to continually break up the text. In contrast, the bag of words method doesn’t care about word type or order. Here, words are just attributes of the document. In this example we parse the sentence "Steph Curry missed a tough shot". In the semantic example you see how words are broken down from the sentence, to noun and verb phrases and ultimately into unique attributes. Bag of words treats each term as just a single token in the sentence no matter the type or order. For this introductory course, we’ll focus on bag of words, but will cover more advanced methods in later courses! Let’s get a quick taste of text mining!
Views: 23619 DataCamp
Artificial intelligence: Bringing unstructured data in contracts to life at scale - BRK2460
 
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For the first time in history, contracts are being digitized, joining financial, human capital, supplier and customer information as an enterprise’s fifth pool of critical data. Icertis is applying AI to this data to transform contracts from static documents into strategic assets which can interact with humans, surrounding systems and other contracts. In this session, Monish Darda shares how Icertis uses cognitive services including Text Analytics, Bing Entity Search API and Translator Text API, employs Azure Machine Learning Package for Text Analytics to train custom models, applies them to 5 million contracts in 40 languages across 25 vertical industries and 90 countries to infuse AI within the Icertis Contract Management (ICM) platform.
Views: 166 Microsoft Ignite
Getting Started with Orange 19: How to Import Text Documents
 
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How to import your own text files, create corpus and define custom class values from scratch. License: GNU GPL + CC Music by: http://www.bensound.com/ Website: https://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 10846 Orange Data Mining
Text Mining (part 5) -  Import a Corpus in R
 
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Import multiple text documents and create a Corpus.
Views: 9910 Jalayer Academy
More Data Mining with Weka (2.2: Supervised discretization and the FilteredClassifier)
 
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More Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 2: Supervised discretization and the FilteredClassifier http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/QldvyV https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 10567 WekaMOOC
Advanced Data Mining with Weka (4.3: Using Naive Bayes and JRip)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 3: Using Naive Bayes and JRip http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/msswhT https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3933 WekaMOOC
More Data Mining with Weka (2.1: Discretizing numeric attributes)
 
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More Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 1: Discretizing numeric attributes http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/QldvyV https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 19454 WekaMOOC
How to Clean Up Raw Data in Excel
 
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Al Chen (https://twitter.com/bigal123) is an Excel aficionado. Watch as he shows you how to clean up raw data for processing in Excel. This is also a great resource for data visualization projects. Subscribe to Skillshare’s Youtube Channel: http://skl.sh/yt-subscribe Check out all of Skillshare’s classes: http://skl.sh/youtube Like Skillshare on Facebook: https://www.facebook.com/skillshare Follow Skillshare on Twitter: https://twitter.com/skillshare Follow Skillshare on Instagram: http://instagram.com/Skillshare
Views: 76907 Skillshare
How to Make a Text Summarizer - Intro to Deep Learning #10
 
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I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory. Code for this video (Challenge included): https://github.com/llSourcell/How_to_make_a_text_summarizer Jie's Winning Code: https://github.com/jiexunsee/rudimentary-ai-composer More Learning resources: https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html https://en.wikipedia.org/wiki/Automatic_summarization http://deeplearning.net/tutorial/rnnslu.html http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 145404 Siraj Raval
What is Text Analytics Toolbox? - Text Analytics Toolbox Overview
 
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Text Analytics Toolbox™ provides tools for extracting text from documents, preprocessing raw text, visualizing text, and performing machine learning on text data. The typical workflow begins by importing text data from documents, such as PDF and Microsoft® Word® files, and then extracting meaningful words from the data. Once text is preprocessed, you can interact with your data in a number of ways, including converting the text into a numeric representation and visualizing the text with word clouds or scatter plots. Features created with Text Analytics Toolbox can also be combined with features from other data sources to build machine learning models that take advantage of textual, numeric, audio, and other types of data. You can import pretrained word-embedding models, such as those available in word2vec, FastText, and GloVe formats, to map the words in your dataset to their corresponding word vectors. You can also perform topic modeling and dimensionality reduction with machine learning algorithms such as LDA and LSA. To get started transforming large sets of text data into meaningful insight, download a free trial of Text Analytics Toolbox: http://bit.ly/2Jp3t6a Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names maybe trademarks or registered trademarks of their respective holders.
Views: 953 MATLAB
Introduction to R Data Analysis: Data Cleaning
 
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Data Cleaning and Dates using lubridate, dplyr, and plyr
Views: 43269 John Muschelli
Weka Tutorial 24: Model Comparison (Model Evaluation)
 
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In this tutorial, you will learn how to use Weka Experimenter to compare the performances of multiple classifiers on single or multiple datasets. Please subscribe to get more updates and like if the tutorial is useful. Link in: http://www.linkedin.com/pub/rushdi-shams/3b/83b/9b3
Views: 28486 Rushdi Shams
Naive Bayes algorithm in Machine learning Program | Text Classification python (2018)
 
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We have implemented Text Classification in Python using Naive Bayes Classifier. It explains the text classification algorithm from beginner to pro. For understanding the co behind it, refer: https://www.youtube.com/watch?v=Zt83JnjD8zg Here, we have used 20 Newsgroup dataset to train our model for the classification. Link to download the 20 Newsgroup dataset: http://qwone.com/~jason/20Newsgroups/20news-bydate.tar.gz Packages used here are: 1. sklearn 2. Tfidf Vectorizer 3. Multinomial Naive Bayes Classifier 4. Pipeline 5. Metrics Refer the entire code at: https://github.com/codewrestling/TextClassification/blob/master/Text%20Classification.py For slides, refer: https://github.com/codewrestling/TextClassification/raw/master/Text%20Classification.pdf Follow us on Github for more codes: https://github.com/codewrestling machine learning python beginner,machine learning python basics,machine learning python regression,machine learning game python,machine learning applications python
Views: 2865 Code Wrestling
Cognitive Invoice Data Capture Webinar
 
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Watch Rossum's webinar on cognitive invoice data capture to learn: - How to implement cognitive invoice data capture in your organization instead of traditional OCR - How to speed up invoice processing by 6x - How to capture data from business documents without rules or templates - How Rossum helps companies like Molson Coors Learn more at: https://rossum.ai
Views: 361 Rossum
More Data Mining with Weka (5.6: Summary)
 
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More Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 6: Summary http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/rDuMqu https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3314 WekaMOOC
Data Mining with Weka (2.4: Baseline accuracy)
 
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Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 4: Baseline accuracy http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/D3ZVf8 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 34834 WekaMOOC
Data Mining with Weka (1.2: Exploring the Explorer)
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 2: Exploring the Explorer http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 89282 WekaMOOC
Machine Learning :  Introduction (in Hindi)
 
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Machine Learning Machine learning is a subfield of computer science (CS) and artificial intelligence (AI) that deals with the construction and study of systems that can learn from data, rather than follow only explicitly programmed instructions. Besides CS and AI, it has strong ties to statistics and optimization, which deliver both methods and theory to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit, rule-based algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Machine learning, data mining, and pattern recognition are sometimes conflated. Machine learning tasks can be of several forms. In supervised learning, the computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs. Spam filtering is an example of supervised learning. In unsupervised learning, no labels are given to the learning algorithm, leaving it on its own to groups of similar inputs (clustering), density estimates orprojections of high-dimensional data that can be visualised effectively. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end. Topic modeling is an example of unsupervised learning, where a program is given a list of human language documents and is tasked to find out which documents cover similar topics. In reinforcement learning, a computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether it has come close to its goal or not. Definition In 1959, Arthur Samuel defined machine learning as a “Field of study that gives computers the ability to learn without being explicitly programmed”. Tom M. Mitchell provided a widely quoted, more formal definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E”. This definition is notable for its defining machine learning in fundamentally operational rather than cognitive terms, thus following Alan Turing's proposal in Turing's paper “Computing Machinery and Intelligence” that the question “Can machines think?” be replaced with the question “Can machines do what we (as thinking entities) can do?” Generalization: A core objective of a learner is to generalize from its experience. Generalization in this context is the ability of a learning machine to perform accurately on new, unseen tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases. These two terms are commonly confused, as they often employ the same methods and overlap significantly. They can be roughly defined as follows: 1. Machine learning focuses on prediction, based on known properties learned from the training data. 2. Data Mining focuses on the discovery of (previously)unknown properties in the data. This is the analysis step of Knowledge Discovery in Databases. The two areas overlap in many ways: data mining uses many machine learning methods, but often with a slightly different goal in mind. On the other hand, machine learning also employs data mining methods as “unsupervised learning” or as a preprocessing step to improve learner accuracy. Human Interaction Some machine learning systems attempt to eliminate the need for human intuition in data analysis, while others adopt a collaborative approach between human and machine
Views: 23174 sangram singh
Advanced Data Mining with Weka (3.2: Setting up R with Weka)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 2: Setting up R with Weka http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/8yXNiM https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 6132 WekaMOOC
Data Science Tutorial | Introduction of Text Analytics in R | R Programming Tutorial
 
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In this Data Science Tutorial videos, I am starting the series of Text mining in R. Text mining is a branch of data mining which specifically look at the mining textual data and found knowledge from it. In this video I've given the overview of text mining along with that started with one of the sample data and provided you couple of R Commands to start grilling the data and find basic knowledge from it by creating histogram and tables to look at the distribution of data in R. Link to the text spam csv file - https://drive.google.com/open?id=0B8jkcc4fRf35c3lRRC1LM3RkV0k
Advanced Data Mining with Weka (1.6: Application: Infrared data from soil samples)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 6: Infrared data from soil samples http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/JyCK84 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 1974 WekaMOOC
Automatic Document Classification with AI
 
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Roberto Calandrini's project pitch for PwC Italy. Presentation of the progresses made at the 2017 edition of the School of Artificial Intelligence. Are you an outstanding Engineer ready to make a difference in the field? Apply here: pischool.link/apply-students-school-of-ai Are you a Company facing a business challenge that can be solved by AI? Become a sponsor here: pischool.link/apply-sponsor-school-of-ai
Views: 635 Pi School
Advanced Data Mining with Weka (2.5: Classifying tweets)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 5: Classifying tweets http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3788 WekaMOOC
More Data Mining with Weka (4.4: Fast attribute selection using ranking)
 
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More Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 4: Fast attribute selection using ranking http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/I4rRDE https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 14975 WekaMOOC
Data Mining with Weka (4.1: Classification boundaries)
 
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Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 1: Classification boundaries http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/augc8F https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 25437 WekaMOOC
More Data Mining with Weka (3.5: Representing clusters)
 
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More Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 5: Representing clusters http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/nK6fTv https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 49040 WekaMOOC
Learn basic data exploration in R in 2 minutes
 
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This video explains how data contained in a data frame is explored in R before starting any data analysis in R. PDF Document: http://bit.ly/2AMnAbF Visit: www.ceekh.com
Views: 70 Ceekh
Parsing Text with R
 
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Using R to parse Text. Resources: **BOOK** "R for data science": http://amzn.to/2DjBqCg
Views: 1890 Tomer Ben David
What is DATA EXTRACTION? What does DATA EXTRACTION mean? DATA EXTRACTION meaning & explanation
 
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What is DATA EXTRACTION? What does DATA EXTRACTION mean? DATA EXTRACTION meaning - DATA EXTRACTION definition - DATA EXTRACTION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Data extraction is the act or process of retrieving data out of (usually unstructured or poorly structured) data sources for further data processing or data storage (data migration). The import into the intermediate extracting system is thus usually followed by data transformation and possibly the addition of metadata prior to export to another stage in the data workflow. Usually, the term data extraction is applied when (experimental) data is first imported into a computer from primary sources, like measuring or recording devices. Today's electronic devices will usually present an electrical connector (e.g. USB) through which 'raw data' can be streamed into a personal computer. Typical unstructured data sources include web pages, emails, documents, PDFs, scanned text, mainframe reports, spool files, classifieds, etc. Which is further used for sales / marketing leads. Extracting data from these unstructured sources has grown into a considerable technical challenge where as historically data extraction has had to deal with changes in physical hardware formats, the majority of current data extraction deals with extracting data from these unstructured data sources, and from different software formats. This growing process of data extraction from the web is referred to as Web scraping. The act of adding structure to unstructured data takes a number of forms Using text pattern matching such as regular expressions to identify small or large-scale structure e.g. records in a report and their associated data from headers and footers; Using a table-based approach to identify common sections within a limited domain e.g. in emailed resumes, identifying skills, previous work experience, qualifications etc. using a standard set of commonly used headings (these would differ from language to language), e.g. Education might be found under Education/Qualification/Courses; Using text analytics to attempt to understand the text and link it to other information.
Views: 531 The Audiopedia
Text analytics extract key phrases using Power BI and Microsoft Cognitive Services
 
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Download the PDF to keep as reference http://theexcelclub.com/extract-key-phrases-from-text/ FREE Power BI course - Power BI - The Ultimate Orientation http://theexcelclub.com/free-excel-training/ Or on Udemy https://www.udemy.com/power-bi-the-ultimate-orientation Or on Android App https://play.google.com/store/apps/details?id=com.PBI.trainigapp Carry out a text analytics like the big brand...only for free with Power BI and Microsoft Cognitive Services. this video will cover Obtain a Text Analytics API Key from Microsoft Cognitive Services Power BI – Setting up the Text Data Setting up the Parameter in Power BI Setting up the Custom function Query(with code to copy) Grouping the text Running the Key Phrase Extraction by calling the custom function. Extracting the key phrases from the returned Json file. Sign up to our newsletter http://theexcelclub.com/newsletter/ Watch more Power BI videos https://www.youtube.com/playlist?list=PLJ35EHVzCuiEsQ-68y0tdnaU9hCqjJ5Dh Watch Excel Videos https://www.youtube.com/playlist?list=PLJ35EHVzCuiFFpjWeK7CE3AEXy_IRZp4y Join the online Excel and PowerBI community https://plus.google.com/u/0/communities/110804786414261269900
Views: 4340 Paula Guilfoyle
More Data Mining with Weka (5.5: ARFF and XRFF)
 
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More Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 5: ARFF and XRFF http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/rDuMqu https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3900 WekaMOOC
R tutorial: Getting started with text mining?
 
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Learn more about text mining with R: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words Boom, we’re back! You used bag of words text mining to make the frequent words plot. You can tell you used bag of words and not semantic parsing because you didn’t make a plot with only proper nouns. The function didn’t care about word type. In this section we are going to build our first corpus from 1000 tweets mentioning coffee. A corpus is a collection of documents. In this case, you use read.csv to bring in the file and create coffee_tweets from the text column. coffee_tweets isn’t a corpus yet though. You have to specify it as your text source so the tm package can then change its class to corpus. There are many ways to specify the source or sources for your corpora. In this next section, you will build a corpus from both a vector and a data frame because they are both pretty common.
Views: 4734 DataCamp

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