Search results “Text mining classification ppt”
What is Text Mining?
An introduction to the basics of text and data mining. To learn more about text mining, view the video "How does Text Mining Work?" here: https://youtu.be/xxqrIZyKKuk
Views: 49725 Elsevier
Data Mining Lecture -- Bayesian Classification | Naive Bayes Classifier | Solved Example (Eng-Hindi)
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: 157057 Well Academy
Data Mining Classification and Prediction ( in Hindi)
A tutorial about classification and prediction in Data Mining .
Views: 29510 Red Apple Tutorials
find relevant notes at-https://viden.io/
Views: 8107 LearnEveryone
8. Text Mining Webinar - Topic Detection
This video from the recordings of the KNME Text Mining Webinar of October 30 2013 (https://www.youtube.com/edit?o=U&video_id=tY7vpTLYlIg). This part shows how to implement a topic detection with text mining and KNIME nodes.
Views: 2535 KNIMETV
Views: 419 LearnEveryone
Aspect Based Sentiment Analysis.
This is a Project built as a part of Information Retrieval and Extraction Course at IIIT-Hyderabad. Sentiment analysis is increasingly viewed as a vital task both from an academic and a commercial standpoint. The majority of current approaches, however, attempt to detect the overall polarity of a sentence, paragraph, or text span, regardless of the entities mentioned (e.g., laptops, restaurants) and their aspects (e.g., battery, screen; food, service). By contrast, this task is concerned with aspect based sentiment analysis (ABSA), where the goal is to identify the aspects of given target entities and the sentiment expressed towards each aspect. The project is built in python using stanford coreNLP and NLTK as 3rd party tools. github link:-https://github.com/SaujanyaReddy/Aspect-Based-Sentiment-Analysis-IRE-Major-Project dropbox link to ppt and report:-https://www.dropbox.com/sh/krpv30cwdakgr90/AAC-cQ-Vgkm1OpWaokZIEZlba?dl=0 slideshare link to ppt:-http://www.slideshare.net/IndranilMukherjee20/absa-project-60961283
Views: 5215 Indranil Mukherjee
Twitter Sentiment Analysis in Python using Tweepy and TextBlob
In this tutorial we will do sentiment analysis in python by analyzing tweets about any topic happening in the world to see how positive or negative it's emotion is. We will use tweepy for fetching tweets and textblob for natural language processing (nlp) Text Based Tutorial http://www.letscodepro.com/Twitter-Sentiment-Analysis/ Github link for project https://github.com/the-javapocalypse/Twitter-Sentiment-Analysis Further Reading Material http://docs.tweepy.org/en/v3.5.0/api.html http://textblob.readthedocs.io/en/dev/ Please Subscribe! And like. And comment. That's what keeps me going. Follow Me Facebook: https://www.facebook.com/javapocalypse Instagram: https://www.instagram.com/javapocalypse
Views: 24924 Javapocalypse
Naive Bayes Theorem | Introduction to Naive Bayes Theorem | Machine Learning Classification
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 About us: HackerEarth is building the largest hub of programmers to help them practice and improve their programming skills. At HackerEarth, programmers: 1. Solve problems on Algorithms, DS, ML etc(https://goo.gl/6G4NjT). 2. Participate in coding contests(https://goo.gl/plOmbn) 3. Participate in hackathons(https://goo.gl/btD3D2) Subscribe Our Channel For More Updates : https://goo.gl/suzeTB For More Updates, Please follow us on: Facebook : https://goo.gl/40iEqB Twitter : https://goo.gl/LcTAsM LinkedIn : https://goo.gl/iQCgJh Blog : https://goo.gl/9yOzvG
Views: 85262 HackerEarth
Weka Text Classification for First Time & Beginner Users
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: 135331 Brandon Weinberg
Machine Learning Lecture 2: Sentiment Analysis (text classification)
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: 53123 MLexplained
Introduction to Text Analytics with R: Overview
The overview of this video series provides an introduction to text analytics as a whole and what is to be expected throughout the instruction. It also includes specific coverage of: – Overview of the spam dataset used throughout the series – Loading the data and initial data cleaning – Some initial data analysis, feature engineering, and data visualization About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models Kaggle Dataset: https://www.kaggle.com/uciml/sms-spam-collection-dataset The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5JLp0 See what our past attendees are saying here: https://hubs.ly/H0f5JZl0 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 65320 Data Science Dojo
presentation of text mining
Presentation of text mining in EEL6825,UFL. Recorded by camera, date error are caused by camera setting. (The debut audio recorder has some problem that cannot be heard clearly, so I switched to camera.)
Views: 522 Daifei Han
Classification consists of predicting a certain outcome based on a given input. In order to predict the outcome, the algorithm processes a training set containing a set of attributes and the respective outcome, usually called goal or prediction attribute. The algorithm tries to discover relationships between the attributes that would make it possible to predict the outcome. Next the algorithm is given a data set not seen before, called prediction set, which contains the same set of attributes, except for the prediction attribute – not yet known. The algorithm analyses the input and produces a prediction.
Views: 32341 Nina Canares
The 5 Types of Text Structure
How do authors organize the texts they write? This unit teaches five common text structures used in informational and nonfiction text: description, sequence, cause and effect, compare and contrast, and problem and solution. Check out all the educational videos from Flocabulary, often called the "Schoolhouse Rock" of the 21st Century, at http://flocabulary.com For lesson plans and activities that go along with this video, visit https://www.flocabulary.com/unit/text-structure/ Connect With Us! Twitter: http://www.twitter.com/flocabulary Facebook: http://www.facebook.com/flocabulary Instagram: http://www.instagram.com/flocabulary Pinterest: http://www.pinterest.com/flocabulary Beat by BogoBeats
Views: 551910 Flocabulary
Text Mining for Beginners
This is a brief introduction to text mining for beginners. Find out how text mining works and the difference between text mining and key word search, from the leader in natural language based text mining solutions. Learn more about NLP text mining in 90 seconds: https://www.youtube.com/watch?v=GdZWqYGrXww Learn more about NLP text mining for clinical risk monitoring https://www.youtube.com/watch?v=SCDaE4VRzIM
Views: 76406 Linguamatics
Machine Learning: Multiclass Classification
How to turn binary classifiers into multiclass classifiers.
Views: 37595 Jordan Boyd-Graber
Klassify - The Data Classification toolkit
Klassify is the Data classification tool which helps user to classify the unstructured data like Microsoft Word , Excel , Powerpoint and Aobe PDF.
Views: 2016 Vishal Bindra
How K-Nearest Neighbors (kNN) Classifier Works
My web page: www.imperial.ac.uk/people/n.sadawi
Views: 89502 Noureddin Sadawi
Diagnosis of Lung Cancer Prediction System Using Data Mining Classification Techniques
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 6375 Clickmyproject
Data Mining  Association Rule - Basic Concepts
short introduction on Association Rule with definition & Example, are explained. Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database. Parts of Association rule is explained with 2 measurements support and confidence. types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples. Names of Association rule algorithm and fields where association rule is used is also mentioned.
Text Dependent Analysis Lesson
Recorded with http://screencast-o-matic.com
Views: 6939 Sarah Dilling
How kNN algorithm works
In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3. This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy
Views: 400322 Thales Sehn Körting
data mining powerpoint
IASP 520 - Data Mining How Data Mining is used in the Healthcare Field
Views: 936 Stephanie Hansen
How CNN (Convolutional Neural Networks - Deep Learning) algorithm works
In this video I present a simple example of a CNN (Convolutional Neural Network) applied to image classification of digits. CNN is one of the well known Deep Learning algorithms. I firstly explain the basics of Neural Networks, i.e. the artificial neuron, followed by the concept of convolution, and the common layers in a CNN, such as convolutional, pooling, fully connected, and softmax classification. I read several references to prepare this material, but the main references are: * Towards better exploiting convolutional neural networks for Remote Sensing scene classification. By Keiller Nogueira, Otávio Penatti, Jefersson dos Santos * Everything you wanted to know about Deep Learning for computer vision but were afraid to ask. By Moacir Ponti, Leonardo Ribeiro, Tiago Nazaré, Tu Bui, John Collomosse I also created an Octave (Matlab like) source code to implement the basic CNN showed in this video, which are available at my github. Please follow the link for more details on the source code: https://github.com/tkorting/youtube/tree/master/deep-learning-cnn This presentation is available at my Prezi site, at this link: http://prezi.com/n_r8p1ytanyh/?utm_campaign=share&utm_medium=copy Thanks for watching this video, please like and share, and subscribe to my channel. Regards
Views: 32805 Thales Sehn Körting
Using Machine Learning to Classify Intent with Python
Views: 3074 Ben Hoff
Fuzzy Logic Tutorials | Introduction to Fuzzy Logic, Fuzzy Sets & Fuzzy Set Operations
Fuzzy logic tutorials to understand the basic concept of fuzzy set and fuzzy set operations. How fuzzy set is different from traditional/binary logic. Understand membership function in fuzzy logic and understand the difference between crisp set and fuzzy set. Learn the basic representation of fuzzy sets. Simple Snippets Official Website - https://simplesnippets.tech/ Simple Snippets on Facebook- https://www.facebook.com/simplesnippets/ Simple Snippets on Instagram- https://www.instagram.com/simplesnippets/ Simple Snippets email ID- [email protected] For Classroom Coaching in Mumbai for Programming & other IT/CS Subjects Checkout UpSkill Infotech - https://upskill.tech/ UpSkill is an Ed-Tech Company / Coaching Centre for Information Technology / Computer Science oriented courses and offer coacing for various Degree courses like BSc.IT, BSc.CS, BCA, MSc.IT, MSc.CS, MCA etc. Contact via email /call / FB /Whatsapp for more info email - [email protected] We also Provide Certification courses like - Android Development Web Development Java Developer Course .NET Developer Course Check us out on Social media platforms like Facebook, Instagram, Google etc Facebook page - https://www.facebook.com/upskillinfotech/ Insta page - https://www.instagram.com/upskill_infotech/ Google Maps - https://goo.gl/maps/vjNtZazLzW82
Views: 150991 Simple Snippets
Data Warehousing and Data Mining
This course aims to introduce advanced database concepts such as data warehousing, data mining techniques, clustering, classifications and its real time applications. SlideTalk video created by SlideTalk at http://slidetalk.net, the online solution to convert powerpoint to video with automatic voice over.
Views: 4185 SlideTalk
Tricks, tips and topics in Text Analysis - Bhargav Srinivasa Desikan
PyData Amsterdam 2018 There is an abundance of easily mineable text data (Whatsapp, twitter, and even our own e-mails!), and we have no excuse to not analyse it. In this workshop, we will learn some tips and tricks to deal with messy text data, before moving on to some lesser looked at text analysis techniques, such as text summarisation, working with distance metrics, and an old personal favorite - topic models. Slides: https://github.com/bhargavvader/personal/tree/master/notebooks/text_analysis_tutorial -- www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 1242 PyData
Mining Structured and Unstructured Data
Oracle Advanced Analytics (OAA) Database Option leverages Oracle Text, a free feature of the Oracle Database, to pre-process (tokenize) unstructured data for ingestion by the OAA data mining algorithms. By moving, parallelized implementations of machine learning algorithms inside the Oracle Database, data movement is eliminated and we can leverage other strengths of the Database such as Oracle Text (not to mention security, scalability, auditing, encryption, back up, high availability, geospatial data, etc.. This YouTube video presents an overview of the capabilities for combing and data mining structured and unstructured data, includes several brief demonstrations and instructions on how to get started--either on premise or on the Oracle Cloud.
Views: 2434 Charlie Berger
SAP HANA Academy - Live5: Text Analysis Overview
In this video tutorial, Tahir Hussain Babar (Bob) gives an Overview of Text Analysis in SAP HANA and shows how to set up the "Voice of Customer" Configuration in the multi-target application project using SAP Web IDE for SAP HANA. For the full playlist on Building Solutions Live5, see: http://bit.ly/SHA_Live5 For the code snippets on GitHub, see: http://github.com/saphanaacademy/Live5 If you like our video tutorials, please subscribe to our channel: http://youtube.com/saphanaacademy BLOGS Stay up to date with our latest blogs on SAP.com: https://people.sap.com/denys.kempen https://people.sap.com/philip.mugglestone Do you speak a different language to English? Did you know you can submit Subtitles on all of our videos on YouTube? If you are interested to support this project, please send a mail to [email protected] CONNECT WITH US Feel free to connect with us at the links below: LinkedIn: https://linkedin.com/saphanaacademy Twitter: https://twitter.com/saphanaacademy Facebook: https://www.facebook.com/saphanaacademy/ Google+: https://plus.google.com/u/0/111935864030551244982 Github: https://github.com/saphanaacademy Thank you for watching. Video by the SAP HANA Academy.
Views: 526 SAP HANA Academy
Neural Networks in R: Example with Categorical Response at Two Levels
Provides steps for applying artificial neural networks to do classification and prediction. R file: https://goo.gl/VDgcXX Data file: https://goo.gl/D2Asm7 Machine Learning videos: https://goo.gl/WHHqWP Includes, - neural network model - input, hidden, and output layers - min-max normalization - prediction - confusion matrix - misclassification error - network repetitions - example with binary data neural network is an important tool related to analyzing big data or working in data science field. Apple has reported using neural networks for face recognition in iPhone X. 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: 24528 Bharatendra Rai
Data Science Tutorial | Text Analytics in R  - Creating a Stunning Word Cloud in R - Part 1
In this data science tutorial video I’ve talked about text analytics in R and using the text analytics in R how you can create the stunning word cloud that will help your understand the gist of the entire book or speech or long corporate emails. Wordcloud is a very simple yet very helpful tool to have it in your pocket to really get to know how your leaders are thinking and may take decision in future. In this video I’ve shown you basic functioning of creating wordcloud in R and then how you can tune the wordcloud parameter for a stunning wordcloud in action.
Text analytics with R | How to create the background table of wordcloud for better understanding
In this text analytics data science tutorial video I’ve talked about how you can create the background table of wordcloud so that your stakeholders are aware that why a certain word is coming as large or small. This also helps in auditing of wordcloud in case somebody really want to know the background data based on which it is getting produced. The basic idea behind creating the frequency table of wordcloud is to create the term document matrix that calculate how many times each word has been appeared in the document and then creating matrix for sorting and then creating a data frame to present the data properly to our audience. Data Science Tutorial,word cloud in R,how to create word cloud in r,background data of word cloud,frequeyncy table of wordcloud,how to create data table of wordcloud,creating frequency table for wordcloud,auditing wordcloud data,analyzing textual data in R,text analytics in r,r text analytics,how to analyzing text data in r,r wordlcoud,r analytic,R Programming tutorial,text mining in R,R Text mining,R Online tutorial video,R Complete Tutorial,wordcloud in r
data mining fp growth | data mining fp growth algorithm | data mining fp tree example | fp growth
In this video FP growth algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining algorithms in hindi, data mining in hindi, data mining lecture, data mining tools, data mining tutorial, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining fp growth, data mining fp growth algorithm, data mining fp tree example, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining, fp growth algorithm, fp growth algorithm example, fp growth algorithm in data mining, fp growth algorithm in data mining example, fp growth algorithm in data mining examples ppt, fp growth algorithm in data mining in hindi, fp growth algorithm in r, fp growth english, fp growth example, fp growth example in data mining, fp growth frequent itemset, fp growth in data mining, fp growth step by step, fp growth tree
Views: 126381 Well Academy
Final Year Projects| An Ontology-Based Text-Mining Method to Cluster Proposals for Research
Final Year Projects | An Ontology-Based Text-Mining Method to Cluster Proposals for Research Project Selection More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 1346 Clickmyproject
Seminar for Data Mining and Text Analytics     Omer Balola
pp for (Syntactic Annotation Guidelines for the Quranic Arabic Dependency Treebank) paper By student: Omer Balola Ali, Supervised by: prof. Eric Atwell
Views: 107 omer ali
How SVM (Support Vector Machine) algorithm works
In this video I explain how SVM (Support Vector Machine) algorithm works to classify a linearly separable binary data set. The original presentation is available at http://prezi.com/jdtqiauncqww/?utm_campaign=share&utm_medium=copy&rc=ex0share
Views: 512284 Thales Sehn Körting
"Text Mining Unstructured Corporate Filing Data" by Yin Luo
Yin Luo, Vice Chairman at Wolfe Research, LLC presented this talk at QuantCon NYC 2017. In this talk, he showcases how web scraping, distributed cloud computing, NLP, and machine learning techniques can be applied to systematically analyze corporate filings from the EDGAR database. Equipped with his own NLP algorithms, he studies a wide range of models based on corporate filing data: measuring the document tone or sentiment with finance oriented lexicons; investigating the changes in the language structure; computing the proportion of numeric versus textual information, and estimating the word complexity in corporate filings; and lastly, using machine learning algorithms to quantify the informative contents. His NLP-based stock selection signals have strong and consistent performance, with low turnover and slow decay, and is uncorrelated to traditional factors. ------- Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice. More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian. In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Views: 1753 Quantopian
ABC Analysis
Thank you very much for viewing our video lectures. Join our Whatsapp Broadcast / Group to receive daily lectures on similar topics through this Whatsapp direct link https://wa.me/917736022001/?text=Subscribing%20Youtube%20Lectures Also check out our Social Responsibility Campaign where students can enroll in our CA/CMA/CS/CFA/CIMA/ACCA/Banking related Online Courses under "PAY WHAT YOUR HEART FEELS" Scheme. It means students can decide how much to pay for each course by considering 1) Value they derive 2) Affordability 3) Our efforts gone into course creation. Preview courses here https://carajaclasses.com/ Enrollment Form under PAY WHAT YOUR HEART FEEL Scheme https://carajaclasses.com/PAYHEARTFEELS.html Also install our android app CARAJACLASSES to view lectures direct in your mobile - https://bit.ly/2S1oPM6 -Team CARAJACLASSES "Never stop learning, because life never stops teaching" Did you liked this video lecture? Then please check out the complete course related to this lecture, COST ACCOUNTING A COMPREHENSIVE STUDY with 280+ Lectures, 29+ hours content available at discounted price(10% off ) with life time validity and certificate of completion. Enrollment Link For Students Outside India: https://bit.ly/2wiWgj8 Enrollment Link For Students From India: https://www.instamojo.com/caraja/cost-accounting-a-comprehensive-study/?discount=inycaacs2 Our website link : https://www.carajaclasses.com Welcome to Cost Accounting - A Comprehensive Study Course. Yes! This is a comprehensive course because you are going to learn all the following in this single course: a) Basics of Costing Accounting (22 Lectures covering Introduction to Costing, Cost Classifications and Cost Sheet) b) Material costing (11 Lectures covering covering Basics of Material Costing) c) Labour Costing (23 Lectures) d) Overheads Costing (18 Lectures) e) Standard Costing Techniques (13 Lectures) f) Standard Costing Variances (17 Lectures) g) Operating Costing (6 Lectures) h) Marginal Costing(30 Lectures) Altogether, you get to access 118 Lectures. So, how this course is relevant for you? If you a Professional course student in the line of Finance or Accounting, then you would have Cost Accounting as part of your major subject. This course will explain theory and practical concepts in Cost Accounting which will help you to excel in Academic Examinations. If you are an Accounting or Finance or Cost Accounting Executive, this course will help you to brush up you basics in Cost Accounting and all the contents have immediate practical relevance and application. With this course, you will be able to understand -basic concepts and processes used to determine product costs; -understand Cost Accounting Statements; -solve simple case studies.This course is structured in self paced learning style.Video lectures were used for delivering the course content. All the sections of this are also available as individual courses. If you aspire to gain strong foundation in Cost Accounting, then this course is for you. Happy Learning and Best Wishes! • Category: Business What's in the Course? 1. Over 138 lectures and 12.5 hours of content! 2. Understand Basics of Cost Accounting 3. Understand Material Costing 4. Understand Labour Costing 5. Understand Overheads Costing 6. Understand Standard Costing Techniques 7. Understand Standard Costing Variances 8. Understand Operating Costing 9. Understand Marginal Costing Course Requirements: 1. Basics of Accounting Who Should Attend? 1. Cost Accounting Students and Executives 2. Accounting Students and Executives 3. Finance Students and Executives 4. MBA Finance Students 5. B.Com., BBA, CA, CMA, CS, CFA, CPA, CIMA Students
Data Mining Lecture -- Rule - Based Classification (Eng-Hindi)
-~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
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PPT For Details Is Here: https://drive.google.com/file/d/0B3uT8Rls4MQUZUhiTjFEZGxQWUk/view?usp=sharing Website: www.projectwale.com (+919004670813)
Text Analytics with R | Automating Wordcloud in Shiny - Part 2 | Shiny Web Application Tutorial
In this text analytics with R tutorial, I've talked about how you can automate wordcloud in shiny and add the parameters as well so that you can focus more on analytics and less on code. You just need to supply the text file to shiny web application for wordcloud creation and shiny app will do all the heavy lifting of doing the background process and give you wordcloud for your text analytics. Shiny web application,r shiny,creating word cloud in r,automating wordcloud creation in R,text analytics in shiny,shiny text analytics,how to automate wordclouds in shiny,automating analytics,shiny tutorial,wordcloud shiny tutorial,automate wordclouds with shiny,using shiny to automate wordclouds,shiny for text analytics,shiny web application for text analytics,analyzing textual data with shiny,automating text analytics wordcloud in shiny,R Programming tutorial
SharePoint Content Classification: Layer2 Auto Tagger
Download: https://www.layer2solutions.com/registration-layer2-knowledge-management-suite Features: • Increased productivity and precision while bulk-tagging SharePoint items and documents automatically. • Content classification rules supported to increase the precision of classification. • Installed IFilters are used for content analysis, e.g. Word, Excel, PowerPoint, PDF and many more. • Fully integrated with SharePoint 2010 / 2013 default tagging. • Helps to manage libraries that host more than 5.000 items (list view threshold). • Flexible background operation settings. • External data sources fully supported. The Layer2 Auto Tagger for Microsoft SharePoint Server 2010 and 2013 automatically categorizes SharePoint items and documents in background using taxonomy-based managed metadata and classification rules organized in the SharePoint Term Store. Content classification rules, item and document properties and metadata, information store context and textual document contents are considered with the auto-classification. By default Microsoft SharePoint Server 2010 and 2013 offers a manual content classification feature only.

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