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UiPath PDF Data Extraction | OCR Data Extraction | UiPath Tutorial | RPA Training | Edureka
 
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** RPA Training: https://www.edureka.co/robotic-process-automation-training ** This session on UiPath PDF Data Extraction will cover all the concepts on how to extract data from PDFs using UiPath. Below are the topics covered in the video: 02:03 Extracting Large Texts 16:49 Extracting Specific Elements Subscribe to our Edureka YouTube channel to get video updates: https://goo.gl/6ohpTV Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka How it Works? 1. This is a 4 Week Instructor led Online Course, 25 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training, you will have to work on a project, based on which we will provide you a Grade and a Verifiable Certificate! -------------------------------------------------------------------------------------- About the Robotic Process Automation Course Edureka’s RPA training helps you to understand the concepts around Robotic Process Automation using the leading RPA tool named ‘UiPath’. Robotic Process Automation is the automation of repetitive and rule based human tasks working with the software applications at the presentation/UI level i.e. no software integrations are needed at middleware, server or database levels. In this course, you will learn about the RPA concepts and will gain in-depth knowledge on UiPath tool using which you will be able to automate real-world processes at the enterprise level such as Insurance Claims Processing, Accounts Payable / Purchase Orders Processing, Invoice Processing, Complaints Management, Customer Feedback Analysis, Employee Onboarding, Compliance Reporting, and many more. -------------------------------------------------------------------------------------- What are the Objectives of our Robotic Process AutomationTraining? After completing this course, you will be able to: 1. Know about Robotics Process Automations and their working 2. Assess the key considerations while designing an RPA solution 3. Work proficiently with the leading RPA tool ‘UiPath 4. Have practical knowledge on designing RPA solutions using UiPath 5. Perform Image and Text automation 6. Learn Data Manipulation using variables and arguments 7. Create automations with applications running in Virtual Environments 8. Debug and handle exceptions in workflow automations ------------------------------------------------------------------------------------------------------- Why learn Robotic Process Automation? Robotic Process Automation (RPA) is an automation technology for making smart software by applying intelligence to do high volume and repeatable tasks that are time-consuming. RPA is automating the tasks of wide variety of industries, hence reducing the time and increasing the output. Some of facts about RPA includes: 1. A 2016 report by McKinsey and Co. predicts that the robotic process automation market could be worth $6.7 trillion by 2025 2. A major global wine business, after implementing RPA, increased the order accuracy from 98% to 99.7% while costs reduced to Rs. 5.2 Crore 3. A global dairy company used RPA to automate the processing and validation of delivery claims, reduced goodwill write-offs by Rs. 464 Million ------------------------------------------------------------------------------------------------------- What are the pre-requisites for this course? To master the concept of RPA, you need to have basic understanding of : 1.Basic programming knowledge of using if/else and switch conditions 2.Logical thinking ability Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 16030 edureka!
Extracting from a PDF Data Source
 
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Visit us online at http://learn.objectiflune.com Learn about what we do on our blog http://blog.objectiflune.com For more industry stories, follow us on twitter http://twitter.com/objlune OL is a trademark of Objectif Lune Inc. All registered trademarks displayed are the property of their respective owners. © 2015 Objectif Lune Incorporated. All rights reserved.
Views: 1702 OL Learn
HOW TO ANALYZE PEOPLE ON SIGHT - FULL AudioBook - Human Analysis, Psychology, Body Language
 
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How To Analyze People On Sight | GreatestAudioBooks 🎅 Give the gift of audiobooks! 🎄 Click here: http://affiliates.audiobooks.com/tracking/scripts/click.php?a_aid=5b8c26085f4b8&a_bid=ec49a209 🌟SPECIAL OFFERS: ► Free 30 day Audible Trial & Get 2 Free Audiobooks: https://amzn.to/2Iu08SE ...OR: 🌟 try Audiobooks.com 🎧for FREE! : http://affiliates.audiobooks.com/tracking/scripts/click.php?a_aid=5b8c26085f4b8 ► Shop for books & gifts: https://www.amazon.com/shop/GreatestAudioBooks How To Analyze People On Sight | GreatestAudioBooks by Elsie Lincoln Benedict & Ralph Pain Benedict - Human Analysis, Psychology, Body Language - In this popular American book from the 1920s, "self-help" author Elsie Lincoln Benedict makes pseudo-scientific claims of Human Analysis, proposing that all humans fit into specific five sub-types. Supposedly based on evolutionary theory, it is claimed that distinctive traits can be foretold through analysis of outward appearance. While not considered to be a serious work by the scientific community, "How To Analyze People On Sight" makes for an entertaining read. . ► Follow Us On TWITTER: https://www.twitter.com/GAudioBooks ► Friend Us On FACEBOOK: http://www.Facebook.com/GreatestAudioBooks ► For FREE SPECIAL AUDIOBOOK OFFERS & MORE: http://www.GreatestAudioBooks.com ► SUBSCRIBE to Greatest Audio Books: http://www.youtube.com/GreatestAudioBooks ► BUY T-SHIRTS & MORE: http://bit.ly/1akteBP ► Visit our WEBSITE: http://www.GreatestAudioBooks.com READ along by clicking (CC) for Caption Transcript LISTEN to the entire book for free! Chapter and Chapter & START TIMES: 01 - Front matter -- - 00:00 02 - Human Analysis - 04:24 03 - Chapter 1, part 1 The Alimentive Type - 46:00 04 - Chapter 1, part 2 The Alimentive Type - 1:08:20 05 - Chapter 2, part 1 The Thoracic Type - 1:38:44 06 - Chapter 2, part 2 The Thoracic Type - 2:10:52 07 - Chapter 3, part 1 The Muscular type - 2:39:24 08 - Chapter 3, part 2 The Muscular type - 3:00:01 09 - Chapter 4, part 1 The Osseous Type - 3:22:01 10 - Chapter 4, part 2 The Osseous Type - 3:43:50 11 - Chapter 5, part 1 The Cerebral Type - 4:06:11 12 - Chapter 5, part 2 The Cerebral Type - 4:27:09 13 - Chapter 6, part 1 Types That Should and Should Not Marry Each Other - 4:53:15 14 - Chapter 6, part 2 Types That Should and Should Not Marry Each Other - 5:17:29 15 - Chapter 7, part 1 Vocations For Each Type - 5:48:43 16 - Chapter 7, part 2 Vocations For Each Type - 6:15:29 #audiobook #audiobooks #freeaudiobooks #greatestaudiobooks #book #books #free #top #best #psychology This video: Copyright 2012. Greatest Audio Books. All Rights Reserved. Audio content is a Librivox recording. All Librivox recordings are in the public domain. For more information or to volunteer visit librivox.org. Disclaimer: As an Amazon Associate we earn from qualifying purchases. Your purchases through Amazon affiliate links generate revenue for this channel. Thank you for your support.
Views: 2114883 Greatest AudioBooks
Heuristics Miner Basic
 
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This is the first video in a series showcasing the use of the ProM process mining framework. Each video focusses on a specific process mining task or algorithm. ProM is open-source and freely available at: http://www.promtools.org In this video we introduce the Heuristics Miner, one of the process discovery algorithms available in ProM. The Heuristics Miner is easy to use, quick and can handle noisy event logs. The theory behind the Heuristics Miner is described in detail in: http://dx.doi.org/10.1109/CIDM.2011.5949453 and http://is.ieis.tue.nl/staff/aweijters/WP166.pdf For more information on process mining, please visit: http://www.processmining.org/ Created by: Elham Ramezani, Maikel van Eck, Eduardo González López de Murillas Special Thanks: Sander Leemans, Rafal Kocielnik, Alfredo Bolt, Sebastiaan van Zelst, Shegnan Guo
Views: 7548 P2Mchannel
Count min sketch | Efficient algorithm for counting stream of data | system design components
 
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Count Min sketch is a simple technique to summarize large amounts of frequency data. which is widely used in many places where there is a streaming big data. Donate/Patreon: https://www.patreon.com/techdummies CODE: ---------------------------------------------------------------------------- By Varun Vats: https://gist.github.com/VarunVats9/7f379199d7658b96d479ee3c945f1b4a Applications of count min sketch: ---------------------------------------------------------------------------- http://theory.stanford.edu/~tim/s15/l/l2.pdf http://highscalability.com/blog/2016/1/25/design-of-a-modern-cache.html https://spark.apache.org/docs/2.0.1/api/java/org/apache/spark/util/sketch/CountMinSketch.html Applications using Count Tracking There are dozens of applications of count tracking and in particular, the Count-Min sketch datastructure that goes beyond the task of approximating data distributions. We give three examples. 1. A more general query is to identify the Heavy-Hitters, that is, the query HH(k) returns theset of items which have large frequency (say 1/k of the overall frequency). Count trackingcan be used to directly answer this query, by considering the frequency of each item. Whenthere are very many possible items, answering the query in this way can be quite slow. Theprocess can be sped up immensely by keeping additional information about the frequenciesof groups of items [6], at the expense of storing additional sketches. As well as being ofinterest in mining applications, finding heavy-hitters is also of interest in the context of signalprocessing. Here, viewing the signal as defining a data distribution, recovering the heavy-hitters is key to building the best approximation of the signal. As a result, the Count-Minsketch can be used in compressed sensing, a signal acquisition paradigm that has recentlyrevolutionized signal processing [7]. 2. One application where very large data sets arise is in Natural Language Processing (NLP).Here, it is important to keep statistics on the frequency of word combinations, such as pairsor triplets of words that occur in sequence. In one experiment, researchers compacted a large6 Page 7 90GB corpus down to a (memory friendly) 8GB Count-Min sketch [8]. This proved to be justas effective for their word similarity tasks as using the exact data. 3. A third example is in designing a mechanism to help users pick a safe password. To makepassword guessing difficult, we can track the frequency of passwords online and disallowcurrently popular ones. This is precisely the count tracking problem. Recently, this wasput into practice using the Count-Min data structure to do count tracking (see http://www.youtube.com/watch?v=qo1cOJFEF0U). A nice feature of this solution is that the impactof a false positive—erroneously declaring a rare password choice to be too popular and sodisallowing it—is only a mild inconvenience to the user
UiPath Web Automation | Automate Web Data Extraction - UiPath Studio | UiPath Training | Edureka
 
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** RPA Training - https://www.edureka.co/robotic-process-automation-training ** This Edureka video on "UiPath Web Automation" will help you know how to automate web using UiPath. Below are the topics covered in this UiPath Web Automation: 1. Data Extraction in UiPath 2. Recording in UiPath 3. Website Testing 4. Report Generation in UiPath 5. Application Transfer 6. Hands On - Web Scraping of Google Contacts Subscribe to our channel to get video updates. Hit the subscribe button above. How it Works? 1. This is a 4 Week Instructor led Online Course, 25 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to work on a project, based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka’s RPA training makes you an expert in Robotic Process Automation. Robotic Process Automation is Automation of repetitive and rule-based tasks. In Edureka's RPA online training, you will learn about the RPA concepts and will gain in-depth knowledge on UiPath tool using which you can automate the data extraction from the internet, login process, image recognition process and many more. After completing the RPA Training, you will be able to: 1. Know about Robotic Process Automations and how it works 2. Know about the patterns and key considerations while designing a RPA solution 3. Know about the leading RPA tool i.e. UiPath 4. Gain practical knowledge on designing RPA solutions using both the tools 5. Perform Image and Text automation 6. Create RPA bots and perform data manipulation 7. Debug and handle the exceptions through the tool - - - - - - - - - - - - - - Why learn Robotic Process Automation? Robotic Process Automation (RPA) is an automation technology for making smart software by applying intelligence to do high volume and repeatable tasks that are time-consuming. RPA is automating the tasks of wide variety of industries, hence reducing the time and increasing the output. Some of facts about RPA includes: 1. A 2016 report by McKinsey and Co. predicts that the robotic process automation market could be worth $6.7 trillion by 2025 2. A major global wine business, after implementing RPA, increased the order accuracy from 98% to 99.7% while costs reduced to Rs. 5.2 Crore 3. A global dairy company used RPA to automate the processing and validation of delivery claims, reduced goodwill write-offs by Rs. 464 Million For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 33064 edureka!
Automate Excel, Word, PDF, Web Scraping with Python and more : Read worksheet
 
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http://ytwizard.com/r/9t7mQj http://ytwizard.com/r/9t7mQj Automate Excel, Word, PDF, Web Scraping with Python and more Manipute routine Excel, word, pdf, HTML (Web scraping) related task from python by automation - Beautifulsoup, openpyxl
Become an Excel Wizard With Python
 
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In this talk, we will explore how the Python's openpyxl module allows your Python programs to read and modify Excel spreadsheet files. By using Python, you can take your Excel and data manipulation skills to the whole new level. PERMISSIONS: The original video was published on Six Feet Up Corp YouTube channel with the Creative Commons Attribution license (reuse allowed). CREDITS: Original video source: https://www.youtube.com/watch?v=ueq1iTWQU5U Additional recommended material for Python learners: https://amzn.to/2UMFhRt Python Programming: A Step By Step Guide From Beginner To Expert https://amzn.to/2JsiyZX A Smarter Way to Learn Python: Learn it faster. Remember it longer. https://amzn.to/2CwoGKu Python Crash Course: A Hands-On, Project-Based Introduction to Programming https://amzn.to/2Fi4cG9 Python Programming: An Introduction to Computer Science
Views: 304693 Coding Tech
any kind of data entry,web research and pdf convert work
 
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Hi, everyone my services:data entry jobs; virtual assistant; data entry tasks; vietnamese; lead generation; web research; online data entry; internet research; market research; website research; keyword research; pdf conversion; convert pdf to word; pdf to word; pdf to indesign; pdf converter; edit pdf; linkedin profile; split; pdf to excel; convert pdf; virtual assistants;
Views: 105 public tutor
ISO 9001:2015 PDF CHECKLIST | PDF Guide to ISO 9001 Quality Management Systems
 
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ISO 9001:2015 PDF checklist DOWNLOAD below! ISO 9001 online training course presented by CEO Kobi Simmat. An advanced, step-by-step breakdown of our ISO 9001:2015 PDF checklist. Go and dive into our amazing ISO 9001:2015 Gap Analysis Checklist Course that we have available as the perfect introduction and implementation tool to accompany your management system https://bit.ly/2SGswGm Follow and subscribe to: Best Practice Website : https://goo.gl/uJTioQ Facebook : https://goo.gl/VOJfKZ LinkedIn : https://goo.gl/dZmlTr Youtube : https://goo.gl/8SVD9E Instagram : @bestpracticetv Snapchat : @bestpracticetv Dreams by Joakim Karud https://soundcloud.com/joakimkarud Creative Commons — Attribution-ShareAlike 3.0 Unported— CC BY-SA 3.0 http://creativecommons.org/licenses/b... Music provided by Audio Library https://youtu.be/VF9_dCo6JT4
Views: 88347 @BestPracticeTV
UiPath Citrix Automation | Image and Text Automation in UiPath | UiPath Training | Edureka
 
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** RPA Training - https://www.edureka.co/robotic-process-automation-training ** This Edureka video on "UiPath Citrix Automation" will help you know how to automate web using UiPath. Below are the topics covered in this UiPath Citrix Automation: 1. What is RPA 2. What are Virtual Machines 3. How to Automate Tasks on Virtual Machines 4. Citrix Automation using Uipath 5. Hands-On - Automating Tasks on Simple Desktop Application 6. Hands-On - Automating Tasks on Application Running on Virtual Machine Subscribe to our channel to get video updates. Hit the subscribe button above. How it Works? 1. This is a 5 Week Instructor led Online Course, 25 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to work on a project, based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka’s RPA training makes you an expert in Robotic Process Automation. Robotic Process Automation is Automation of repetitive and rule-based tasks. In Edureka's RPA online training, you will learn about the RPA concepts and will gain in-depth knowledge on UiPath tool using which you can automate the data extraction from the internet, login process, image recognition process and many more. After completing the RPA Training, you will be able to: 1. Know about Robotic Process Automations and how it works 2. Know about the patterns and key considerations while designing a RPA solution 3. Know about the leading RPA tool i.e. UiPath 4. Gain practical knowledge on designing RPA solutions using both the tools 5. Perform Image and Text automation 6. Create RPA bots and perform data manipulation 7. Debug and handle the exceptions through the tool - - - - - - - - - - - - - - Why learn Robotic Process Automation? Robotic Process Automation (RPA) is an automation technology for making smart software by applying intelligence to do high volume and repeatable tasks that are time-consuming. RPA is automating the tasks of wide variety of industries, hence reducing the time and increasing the output. Some of facts about RPA includes: 1. A 2016 report by McKinsey and Co. predicts that the robotic process automation market could be worth $6.7 trillion by 2025 2. A major global wine business, after implementing RPA, increased the order accuracy from 98% to 99.7% while costs reduced to Rs. 5.2 Crore 3. A global dairy company used RPA to automate the processing and validation of delivery claims, reduced goodwill write-offs by Rs. 464 Million For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 11707 edureka!
More Data Mining with Weka (3.6: Evaluating clusters)
 
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More Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 6: Evaluating 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: 22561 WekaMOOC
Weka Data Mining Tutorial for First Time & Beginner Users
 
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23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 470481 Brandon Weinberg
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: 473002 sentdex
Heuristics Miner Advanced
 
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This video is part of a series showcasing the use of the ProM process mining framework. Each video focusses on a specific process mining task or algorithm. ProM is open-source and freely available at: http://www.promtools.org In this video we describe the theory behind the Heuristics Miner, one of the process discovery algorithms available in ProM. We discuss the various parameters of the Heuristics Miner and their effect on the discovered models. The theory behind the Heuristics Miner is also described in detail in: http://dx.doi.org/10.1109/CIDM.2011.5949453 and http://is.ieis.tue.nl/staff/aweijters/WP166.pdf For a quick introduction to the Heurstics Miner, please check the first video in this series: https://www.youtube.com/watch?v=MBxeUCPnLRk For more information on process mining, please visit: http://www.processmining.org/ Created by: Elham Ramezani, Maikel van Eck, Eduardo González López de Murillas
Views: 4121 P2Mchannel
Data Mining with Weka (2.5: Cross-validation)
 
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Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 5: Cross-validation 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: 41362 WekaMOOC
Veri Madenciliği(Excel -  Karışık Örnekler)
 
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Veri Madenciliği, Veri Madenciliği Dersleri, Veri Madenciliği Eğitim Seti, Veri Madenciliği Dersleri Serisi, Veri Madenciliği(Excel - Karışık Örnekler) TAGS veri madenciliği yöntemleri, veri madenciliği nedir, microsoft excell, makro nedir, makrolarla excell dersleri, mining data, mining, datamining, what is data, data mining pdf, data mining techniques, data analysis, mineria, mineria de datos, data warehouse, data warehousing, database, data mining algorithm, data mining ppt, database mining, data mining software, big data, clustering, data mining tools, google data mining, classification, big data mining, smite, datamining smite, smite data mining, coursera, smite reddit, smite patch notes, smite wiki,0 gw2 data mining, big data analytics, bigdata, big data mining, big data, slideshare, kaggle, data scientist, hadoop,0 data mining meaning, jurnal data mining, python data mining, data mining adalah, nptel, python, data mining pdf TAGS http://kodkolik.net/ Machine Learning Group at the University of Waikato Project Software Book Publications People Related Weka 3: Data Mining Software in Java Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. The name is pronounced like this, and the bird sounds like this. Weka is open source software issued under the GNU General Public License. We have put together several free online courses that teach machine learning and data mining using Weka. Check out the website for the courses for details on when and how to enrol. The videos for the courses are available on Youtube. Yes, it is possible to apply Weka to big data!
Data Access in KNIME: File Reader
 
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This video shows how to read text files. Example workflows on how to use the Table Reader node can be found on the EXAMPLES server within the KNIME Analytics Platform (www.knime.org) under 01_Data_Access/01_Common_Type_Files Previous: - "Annotations and comments" https://youtu.be/AHURYB_O8sA Next: - How to read a .table formatted files https://youtu.be/tid1qi2HAOo
Views: 6904 KNIMETV
Crime Data Analysis Using Kmeans Clustering Technique
 
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Introduction Data Mining deals with the discovery of hidden knowledge, unexpected patterns and new rules from large databases. Crime analyses is one of the important application of data mining. Data mining contains many tasks and techniques including Classification, Association, Clustering, Prediction each of them has its own importance and applications It can help the analysts to identify crimes faster and help to make faster decisions. The main objective of crime analysis is to find the meaningful information from large amount of data and disseminates this information to officers and investigators in the field to assist in their efforts to apprehend criminals and suppress criminal activity. In this project, Kmeans Clustering is used for crime data analysis. Kmeans Algorithm The algorithm is composed of the following steps: It randomly chooses K points from the data set. Then it assigns each point to the group with closest centroid. It again recalculates the centroids. Assign each point to closest centroid. The process repeats until there is no change in the position of centroids. Example of KMEANS Algorithm Let’s imagine we have 5 objects (say 5 people) and for each of them we know two features (height and weight). We want to group them into k=2 clusters. Our dataset will look like this: First of all, we have to initialize the value of the centroids for our clusters. For instance, let’s choose Person 2 and Person 3 as the two centroids c1 and c2, so that c1=(120,32) and c2=(113,33). Now we compute the Euclidean distance between each of the two centroids and each point in the data.
Views: 1420 E2MATRIX RESEARCH LAB
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: 2080 WekaMOOC
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: 4323 WekaMOOC
Advanced Data Mining with Weka (1.5: Lag creation, and overlay data)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 5: Lag creation, and overlay data 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: 2726 WekaMOOC
Data Mining with Weka (4.4: Logistic regression)
 
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Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 4: Logistic regression 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: 33808 WekaMOOC
Advanced Data Mining with Weka (2.3: The MOA interface)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 3: The MOA interface 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: 3864 WekaMOOC
Do copy paste data scraping data mining  web research
 
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http://ytwizard.com/r/JrQDzX http://ytwizard.com/r/JrQDzX Do copy paste data scraping data mining web research
Views: 22 Svetlana Dimkovik
Create Physical Data Objects with Informatica Developer
 
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Informatica's new and powerful Developer tool is used to create Data Integration and Data Quality solutions. In this vidoe you learn how to create Data Base, Flat File, and Custom Physical Data Objects(PDOs) to read or write data. These can be used in Mappings with transformational logic, and can also be used in Logical Data Objects to create Virtual Objects.
Views: 14249 dataUtrust
Data Viewers In SSIS
 
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https://www.youtube.com/user/masterkeshav This SSIS training video will explain data viewers 1.) Grid 2.) Histogram 3.) Scatter Plot 4.) Column Chart as excellent way to debug you data loads and transforms while you work with Data Flow tasks..
Views: 7580 Keshav Singh
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: 173570 Rushdi Shams
Deep Kayal - Large-scale data extraction, structuring and matching using Python and Spark
 
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"Large-scale data extraction, structuring and matching using Python and Spark [EuroPython 2017 - Talk - 2017-07-14 - Anfiteatro 1] [Rimini, Italy] Motivation - Matching data collections with the aim to augment and integrate the information for any available data point that lies in two or more of these collections, is a problem that nowadays arises often. Notable examples of such data points are scientific publications for which metadata and data are kept in various repositories, and users’ profiles, whose metadata and data exist in several social networks or platforms. In our case, collections were as follows: (1) A large dump of compressed data files on s3 containing archives in the form of zips, tars, bzips and gzips, which were expected to contain published papers in the form of xmls and pdfs, amongst other files, and (2) A large store of xmls in the form of xmls, some of which are to be matched to Collection 1. Problem Statement - The problems, then, are: (1) How to best unzip the compressed archives and extract the relevant files? (2) How to extract meta-information from the xml or pdf files? (3) How to match the meta-information from the two different collections? And all of these must be done in a big-data environment. Presentation – https://drive.google.com/open?id=1hA9J80446Qh7nd8PMYZibtIR1WjMkdLXfDgwUlts7JM The presentation will describe the solution process and the use of python and Spark in the large-scale unzipping and extraction of files from archives, and how metadata was then extracted from the files to perform the matches on. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/
Use forward and backward pass to determine project duration and critical path
 
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Check out http://www.engineer4free.com for more free engineering tutorials and math lessons! Project Management Tutorial: Use forward and backward pass to determine project duration and critical path. Please support my work: PATREON | https://www.patreon.com/Engineer4Free Every dollar is seriously appreciated and enables me to continue making more tutorials
Views: 874199 Engineer4Free
Learning Defect Predictors from Static Code Attributes: Lessons from the Tren...
 
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Google Tech Talks October 29, 2008 ABSTRACT For six years, I have worked on learning quality predictors from NASA data. Based on that experimence, this talk offers the following lessons from the trenches: 1) Real world data collection is more like ambulance chasing that bus driving. The old DoD model of rigorous process control just breaks down in the modern era of distributed software development. Rather than lament lack of formal process, we should adapt our learning methods to handle the idiosyncrasies of our data. 2) Accuracy, correlation and precision and not accurate or precise and may not correlate with any decision making process. This is especially true for data sets where only a small percentage of the data contains the target concept. 3) Static code attributes are a wide and shallow well- easy to get to the bottom, very hard to get much further. Our learners may have learned all they can learn from these attributes. 4) The only way up is sideways. My data miners have struck a performance ceiling and the only way up is to change the performance target. 5) The performance ceiling is very close- we can exploit that. Rather than large-scale automatic methods, it may be more productive to explore human-in-the-loop interactive learning strategies. 6) We can talk, but will they listen? Many times, I have found a clear signal in a software engineering data set. Clearly, our learners are good enough to assist managers in the difficult task of managing software projects. Sadly, all too often, some management edict is applied that effectively ends that project (e.g. collection of that data source is terminated). I offer some speculations on this peculiar effect. References: * "Implications of Ceiling Effects in Defect Predictors" by T. Menzies and B. Turhan and A. Bener and G. Gay and B. Cukic and Y. Jiang. Proceedings of PROMISE 2008 Workshop (ICSE) 2008 . Available from http://menzies.us/pdf/08ceiling.pdf . * "Learning Better IVV Practices" by T. Menzies and M. Benson and K. Costello and C. Moats and M. Northey and J. Richarson. Innovations in Systems and Software Engineering March 2008 . Available from http://menzies.us/pdf/07ivv.pdf . * Data Mining Static Code Attributes to Learn Defect Predictors" by Tim Menzies and Jeremy Greenwald and Art Frank. IEEE Transactions on Software Engineering January 2007 . Available from http://menzies.us/pdf/06learnPredict.pdf . * "Problems with Precision" by Tim Menzies and Alex Dekhtyar and Justin Distefano and Jeremy Greenwald. IEEE Transactions on Software Engineering September 2007 . http://menzies.us/pdf/07precision.pdf . * "Finding the Right Data for Software Cost Modeling" by Zhihao Chen and Tim Menzies and Dan Port and Barry Boehm. IEEE Software Nov 2005 . http://menzies.us/pdf/05chen.pdf Speaker: Tim Menzies Dr. Tim Menzies ([email protected]) has been working on advanced modeling and AI since 1986. He received his PhD from the University of New South Wales, Sydney, Australia and is the author of over 170 refereeed papers. A former research chair for NASA, Dr. Menzies is now a associate professor at the West Virginia University's Lane Department of Computer Science and Electrical Engineering.
Views: 5035 GoogleTechTalks
Predicting the Winning Team with Machine Learning
 
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Can we predict the outcome of a football game given a dataset of past games? That's the question that we'll answer in this episode by using the scikit-learn machine learning library as our predictive tool. Code for this video: https://github.com/llSourcell/Predicting_Winning_Teams Please Subscribe! And like. And comment. More learning resources: https://arxiv.org/pdf/1511.05837.pdf https://doctorspin.me/digital-strategy/machine-learning/ https://dashee87.github.io/football/python/predicting-football-results-with-statistical-modelling/ http://data-informed.com/predict-winners-big-games-machine-learning/ https://github.com/ihaque/fantasy https://www.credera.com/blog/business-intelligence/using-machine-learning-predict-nfl-games/ 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 Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 102218 Siraj Raval
OpenKM -  Extracted keyword's viewer from the document content
 
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Example of Text Extraction feature. It lets see the keywords extracted from the content of a document.
Views: 704 openkm
Advanced Data Mining with Weka (2.4: MOA classifiers and streams)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 4: MOA classifiers and streams 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: 3106 WekaMOOC
'Quality Assurance' Vs "Quality Control' .सिर्फ 10 मिनट में सीखें (हिंदी)
 
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In just 10 minutes understand difference between 'Quality Assurance' Vs "Quality Control . सिर्फ 10 मिनट में सीखें (हिंदी) Explained difference in 9 categories. Watch other videos from ‘Quality HUB India’- https://www.youtube.com/channel/UCdDEcmELwWVr_77GpqldKmg/videos • Subscribe to my channel ‘Quality HUB India’ for getting notification. • Like, comment & Share the video with your colleague and friends Link to buy My books 1. Mistake-Proofing Simplified: An Indian Perspective: https://www.amazon.in/gp/product/8174890165/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=8174890165&linkCode=as2&tag=qhi-21 2. Management Thoughts on Quality for Every Manager: https://www.amazon.in/gp/product/B0075MCLTO/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B0075MCLTO&linkCode=as2&tag=qhi-21 Gadgets I use and Link to buy 1. OnePlus 5 - Mobile https://www.amazon.in/gp/product/B01MXZW51M/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B01MXZW51M&linkCode=as2&tag=qhi-21 2. HP 14-AM122TU 14-inch Laptop https://www.amazon.in/gp/product/B06ZYLLT8G/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B06ZYLLT8G&linkCode=as2&tag=qhi-21 3. Canon EOS 700D 18MP Digital SLR Camera https://www.amazon.in/gp/product/B00VT61IKA/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B00VT61IKA&linkCode=as2&tag=qhi-21 4. Sonia 9 Feet Light Stand LS-250 https://www.amazon.in/gp/product/B01K7SW2OQ/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B01K7SW2OQ&linkCode=as2&tag=qhi-21 5. Sony MDR-XB450 On-Ear EXTRA BASS Headphones https://www.amazon.in/gp/product/B00NFJGUPW/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B00NFJGUPW&linkCode=as2&tag=qhi-21 6. QHM 602 USB MINI SPEAKER https://www.amazon.in/gp/product/B00L393EXC/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B00L393EXC&linkCode=as2&tag=qhi-21 7. Photron Tripod Stedy 400 with 4.5 Feet Pan Head https://www.amazon.in/gp/product/B00UBUMCNW/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B00UBUMCNW&linkCode=as2&tag=qhi-21 8. Tie Clip Collar mic Lapel https://www.amazon.in/gp/product/B00ITOD6NM/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B00ITOD6NM&linkCode=as2&tag=qhi-21 9. Hanumex Generic Green BackDrop Background 8x12 Ft for Studio Backdrop https://www.amazon.in/gp/product/B06W53TMDR/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B06W53TMDR&linkCode=as2&tag=qhi-21 10. J 228 Mini Tripod Mount + Action Camera Holder Clip Desktop Self-Tripod For Camera https://www.amazon.in/gp/product/B072JXX9DB/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B072JXX9DB&linkCode=as2&tag=qhi-21 11. Seagate Backup Plus Slim 1TB Portable External Hard Drive https://www.amazon.in/gp/product/B00GASLJK6/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B00GASLJK6&linkCode=as2&tag=qhi-21 Watch other Videos from ‘Quality HUB India’ 1. Process Capability Study (Cp,Cpk, Pp & Ppk) - https://www.youtube.com/watch?v=5hBRE0uji5w 2. What is Six Sigma ?Learn Six Sigma in 30 minutes- https://www.youtube.com/watch?v=1oiKYydbrSw 3. Failure Mode and Effects Analysis (FMEA) - https://www.youtube.com/watch?v=UxSBUHgb1V0&t=25s 4. Statistical Process Control (SPC) in Hindi – https://www.youtube.com/watch?v=WiVjjoeIrmc&t=115s 5. Measurement System Analysis (MSA) (Part 1) - https://www.youtube.com/watch?v=GGwaZeMmZS8&t=25s 6. Advanced Product Quality Planning(APQP) - https://www.youtube.com/watch?v=FaawYoPsUYE&t=35s 7. ‘Quality Circles' - https://www.youtube.com/watch?v=kRp9OIANgG8&t=25s 8. What is 'Cost of Quality' and 'Cost of Poor Quality' - https://www.youtube.com/watch?v=IsCRylbHni0&t=25s 9. How to perfectly define a problem ? 5W and 1H approach - https://www.youtube.com/watch?v=JXecodDxBfs&t=55s 10. What is 'Lean Six Sigma' ? Learn the methodology with benefits. - https://www.youtube.com/watch?v=86XJqf1IhQM&t=41s 11. What is KAIZEN ? 7 deadly Waste (MUDA) and benefit of KAIZEN - https://www.youtube.com/watch?v=TEcE-cKk1qI&t=115s 12. What is '5S' Methodology? (Hindi)- https://www.youtube.com/watch?v=dW8faNOX91M&t=25s 13. 7 Quality Control Tools - (Part 1) Hindi - https://www.youtube.com/watch?v=bQ9t3zoM0NQ&t=88s 14. "KAIZEN" in HINDI- https://www.youtube.com/watch?v=xJpbHTc3wmo&t=25s 15. 'PDCA' or 'Deming Cycle'. Plan-DO-Check-Act cycle - https://www.youtube.com/watch?v=Kf-ax6qIPVc 16. Overall Equipment Effectiveness (OEE) - https://www.youtube.com/watch?v=5OM5-3WVtd0&feature=youtu.be 17. Why-Why Analysis? - Root Cause Analysis Tool - https://www.youtube.com/watch?v=Uxn6N6OJvwA
Views: 532933 Quality HUB India
Implementing Big Data Analysis
 
04:28:37
Social Network for Developers ☞ https://morioh.com Aiodex’s Referral Program  will give you 20% -80% commission from their transaction fee for 7 years. The value will be calculated starting from the date the member you invite sign up ☞ https://aiodex.com/p/referral-program Next Generation Shorten Platform: Optimal choice to make a profit and analyze traffic sources on the network. Shorten URLs and Earn Big Money ☞ https://viralroll.com Get Free 15 Geek ☞ https://geekcash.org Developers Chat Channel ☞ https://discord.gg/KAe3AnN Playlists Video Tutorial ☞ http://dev.edupioneer.net/f086e182ab The Ultimate Hands-On Hadoop - Tame your Big Data! ☞ http://deal.codetrick.net/p/SJ0_BYGKZ Taming Big Data with Apache Spark and Python - Hands On! ☞ http://deal.codetrick.net/p/SkvnpBSXx Apache Spark 2.0 with Scala - Hands On with Big Data! ☞ http://deal.codetrick.net/p/B1MIbtMK- Scala and Spark for Big Data and Machine Learning ☞ http://deal.codetrick.net/p/SyeSqovQg Apache Spark with Scala - Learn Spark from a Big Data Guru ☞ http://deal.codetrick.net/p/rJfZjdrR- Spark and Python for Big Data with PySpark ☞ http://deal.codetrick.net/p/Sk-YpaVYyb Video source via: MVA ---------------------------------------------------- Website: https://goo.gl/XnM72d Website: https://goo.gl/AWpXfC Playlist: https://goo.gl/cknV8C Fanpage: https://goo.gl/kMBCFs Twitter: https://goo.gl/pNw922 Wordpress: https://goo.gl/qAJxMe Pinterest: https://goo.gl/GrRx7B Tumblr: https://goo.gl/6fTauh
Views: 408 Big Data Training
Mozenda - Data Mining - Web Crawler - LeadGenExample
 
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http://www.twitter.com/jbmcclelland Justin McClelland (http://www.justinmcclelland.com), provides various how-to demonstrations and example applications of the Mozenda software (http://www.getmozenda.com ). The Mozenda, Software as a Service (SaaS), platform is ideal for performing comprehensive web data gathering (a.k.a web data extraction, screen scraping, web crawling, web harvesting, etc.) Follow Me on Twitter: http://www.twitter.com/JBMcClelland
Views: 4233 Justin McClelland
What is a HashTable Data Structure - Introduction to Hash Tables , Part 0
 
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This tutorial is an introduction to hash tables. A hash table is a data structure that is used to implement an associative array. This video explains some of the basic concepts regarding hash tables, and also discusses one method (chaining) that can be used to avoid collisions. Wan't to learn C++? I highly recommend this book http://amzn.to/1PftaSt Donate http://bit.ly/17vCDFx STILL NEED MORE HELP? Connect one-on-one with a Programming Tutor. Click the link below: https://trk.justanswer.com/aff_c?offer_id=2&aff_id=8012&url_id=238 :)
Views: 812281 Paul Programming
Data Mining with Weka (2.2: Training and testing)
 
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Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 2: Training and testing 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: 76990 WekaMOOC
Tabulizer Tutorial: Create, Edit, Copy and Export Data Sources
 
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See how to perform various tasks with your data sources. For more information visit: http://www.tabulizer.com
Views: 822 alterora
Machine Learning Bangla Course: Data Processing: Missing Data through Imputing
 
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Download dataset from this link: https://drive.google.com/open?id=1yRTuRPLNpLQRI1zEcq9Gx3N6WTcBCqMP What is Machine Learning? Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. You should check this video tutorial to easily download Anaconda Navigator for Python Distribution. https://youtu.be/4v7Uke37QGs First of all, you have to download Anaconda Navigator Distribution for Python. For this go to this link and download for your computer depending on your operating system, Windows, Linux or Mac. https://www.anaconda.com/download/ We have used Python 3.6 Version for our course. So you should download that to cope up with us. The next video: https://www.youtube.com/watch?v=ohampM4H6fY&index=4&list=PLA-CsqNypl-SqtkfwXAK7trT_M2g5yAGe Data Proessing Complete Playlist: https://www.youtube.com/playlist?list=PLA-CsqNypl-SqtkfwXAK7trT_M2g5yAGe The previous video:https://www.youtube.com/watch?v=RaC85Y2kS5Q&list=PLA-CsqNypl-SqtkfwXAK7trT_M2g5yAGe&index=2 1/How can we Master Machine Learning on Python? 2/How can we Have a great intuition of many Machine Learning models? 3/How can we Make accurate predictions? 4/How can we Make powerful analysis? 5/How can we Make robust Machine Learning models? 6/How can we Create strong added value to your business? 7/How do we Use Machine Learning for personal purpose? 8/How can we Handle specific topics like Reinforcement Learning, NLP and Deep Learning? 9/How can we Handle advanced techniques like Dimensionality Reduction? 10/How do we Know which Machine Learning model to choose for each type of problem? 11/How can we Build an army of powerful Machine Learning models and know how to combine them to solve any problem? Subscribe to our channel to get video updates. সাবস্ক্রাইব করুন আমাদের চ্যানেলেঃ https://www.youtube.com/channel/UC50C-xy9PPctJezJcGO8q2g Follow us on Facebook: https://www.facebook.com/Planeter.Bangladesh/ Follow us on Instagram: https://www.instagram.com/planeter.bangladesh Follow us on Twitter: https://www.twitter.com/planeterbd Our Website: https://www.planeterbd.com For More Queries: [email protected] Phone Number: +8801727659044, +8801728697998 #machinelearning #bigdata #ML #DataScience #DataSet #XY #DeepLearning #robotics #রবোটিক্স #প্ল্যনেটার #Planeter #ieeeprotocols #DataProcessing #MissingData #SimpleLinearRegression #MultiplelinearRegression #PolynomialRegression #SupportVectorRegression(SVR) #DecisionTreeRegression #RandomForestRegression #EvaluationRegressionModelsPerformance #MachineLearningClassificatioModels #LogisticRegression #machinelearnigcourse #machinelearningcoursebangla #machinelearningforbeginners #banglamachinelearning #artificialintelligence #machinelearningtutorials #machinelearningcrashcourse #imageprocessing #SpyderIDE #BestBanglaMachineLearningTutorialSeries #ML #MachineLearning
Views: 729 Planeter
Convert Text File Into Arff File In Weka| Machine Learning Weka Arff File
 
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DescriptionAn ARFF (Attribute-Relation File Format) file is an ASCII text file that describes a list of instances sharing a set of attributes. ARFF files were developed by the Machine Learning Project ARFF files have two distinct sections. The first section is the Header information, which is followed the Data information. https://drive.google.com/file/d/1tZRRfV7a5287s7_vUi5jwtW2-Vy6m-L1/view?usp=sharing
Views: 6290 Ziyad Beg
Text and Network Analytics in KNIME
 
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This video is a part of the webinar "What is new in KNIME 2.10" July 2014. It describes the changes introduced in the TextProcessing and in the Network extension:: - Topic Extractor node - Hierarchy Extractor node - Additional Tree Layouts in the Network Viewer node The full webinar video is available at http://youtu.be/jHOUMbKjum8
Views: 1988 KNIMETV
101 Great Answers To The Toughest Interview Questions ©
 
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This is an excellent guide to attend interviews. 101 Great Answers To The Toughest Interview Questions contains a collection of all possible interview questions and how to answer and face them in a typical interview. A prospective job seeker can also get excellent tips to cracking hard interview questions in this video. Learn to crack your interview today! The interviewing process is a kind of sale. In this case, you are the product—and the salesperson. If you show up unprepared to talk about your unique features and benefits, you're not likely to motivate an interviewer to "buy." The sad fact is that many job candidates are unprepared to talk about themselves. You may have mailed a gorgeous resume and cover letter. You may be wearing the perfect clothes on the day of the interview. But if you can't convince the interviewer—face to face—that you are the right person for the job, you aren't likely to make the sale. Too many candidates hesitate after the first open-ended question, then stumble and stutter their way through a disjointed litany of resume "sound bites." Other interviewees recite canned replies that only highlight their memory skills. The days of filling out the standard application and chatting your way through one or two interviews are gone. These days, interviewers and hiring managers are reluctant to leave anything to chance. Many have begun to experiment with the latest techniques for data-gathering and analysis. For employers, interviewing has become a full-fledged science. More employers seem to be looking for a special kind of employee—someone with experience, confidence, and the initiative to learn what he or she needs to know. Someone who requires very little supervision. Someone with a hands-on attitude—from beginning to end. Because employers can't tell all that from a job application and a handshake, here's what they're making you do: Pass the test(s). You'll probably have to go through more interviews than your predecessors for the same job—no matter what your level of expertise. Knowledge and experience still give you an inside edge. But these days, you'll need stamina, too. Your honesty, your intelligence, your mental health—even the toxicity of your blood—may be measured before you can be considered fully assessed. Brave more interviews. You may also have to tiptoe through a mine field of different types of interview situations—and keep your head—to survive as a new hire. Don't go out and subscribe to a human resources journal. Just do all you can to remain confident and flexible—and ready with your answers. No matter what kind of interview you find yourself in, this approach should carry you through with flying colors. Let's take a brief, no-consequences tour of the interview circuit. What (Who) are You Up Against? There are three predominant interviewing types or styles: the Telephone Screener, the Human Screen, and the Manager. Which is which, and why would someone be considered one or the other? While personal temperament is one factor, the adoption of one or the other style is primarily a function of the interviewer's role in the organization and his or her daily workload. The Human Screen Many human resource and personnel professionals fall into this category. For these people, interviewing is not simply just a once-a-quarter or once-a-month event, but rather a key part of their daily job description. They meet and interview many people, and are more likely than either of the other two categories to consider an exceptional applicant for more than one possible opening within the organization. A primary objective of the Human Screen is to develop a strong group of candidates for Managers (see category three) to interview in person. To do this, of course, they must fend off many applicants and callers—a daunting task, because the Human Screen or the department in which he or she works is often the only contact provided in employment advertisements. Among the most common reasons for removal from the Human Screen's "hot" list are: lack of formal or informal qualifications as outlined in the organization's job description; sudden changes in hiring priorities and personnel requirements; poor performance during the in-person interview itself; and inaction due to the Human Screen's uncertainty about your current status or contact information. That last reason is more common than you might imagine. Human Screens are constantly swamped with phone calls, resumes, and unannounced visits from hopeful applicants. Odds are that despite their best efforts, they sometimes lose track of qualified people. Subscribe to our Channel at http://www.youtube.com/theinterviewskills Follow us on our Official Facebook Fanpage at http://www.facebook.com/theinterviewskills Link to this video http://www.youtube.com/watch?v=vPfN2BnnpUc
Excel Macro Online VBA Tutorial 7 - VBA to open Notepad (txt) file
 
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Start from Tutorial 1 - http://youtu.be/e1NOdjRkLIY Next Tutorial - http://youtu.be/6vyZvKueb2Y Learn how to read data from text files using excel macro. This lecture explains how to open a new Notepad file or existing notepad file using excel vba. Hit LIKE if you like this tutorial.
Mail Mining Outlook add in guide
 
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Mail Mining is a freeware Outlook add in that helps you dynamically rank email, control email alerts and file messages efficiently. Mail Mining makes you more productive.
Views: 5733 BrightEye Eran
ICO Review: Ink Protocol (XNK) - Reputation and Payments for Marketplaces
 
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Ink Protocol is a cryptocurrency used for Listia, a web and mobile marketplace where users exchange goods. Learn more: https://crushcrypto.com/ink-protocol-ico-review/ Download the PDF version of the presentation: https://crushcrypto.com/wp-content/uploads/2018/01/CrushCrypto-ICO-Review-Ink-Protocol.pdf Download the free ICO Guide which contains 6 simple steps for analyzing any ICOs to find the winning projects: https://crushcrypto.com/youtube/ Note: This is not a paid review. We do not offer promotional or advertising services. Our content is based on our own research, analysis and personal opinion. _______________________________________ What does the company/project do? Listia is a web and mobile marketplace where users exchange goods, trading unwanted items for credits that can be used to purchase goods offered by other users. These credits are currently called “Listia Credits”, a centralized digital currency controlled by Listia, Inc. They are now building the Ink Protocol and will launch it on their marketplace with a corresponding token called XNK, designed to take over the role of Listia Credits. By adopting a blockchain based system, Listia will benefit from decentralization as well as more security and transparency in their operations. First, the smart contract will feature a decentralized feedback mechanism, where buyers can leave feedback for the seller about each transaction. This feedback will consist of a rating and comment about the transaction, stored as public data on the Ethereum blockchain. Any marketplace that supports the Ink Protocol will benefit from improved trust and security for their platforms. Even brand-new markets can launch with instant trust and user feedback in place due to Ink and the XNK token. _______________________________________ What are the tokens used for and how can token holders make money? XNK will replace Listia Credits as the marketplace currency, and users with existing Listia Credits will be able to trade them in for XNK. In addition, users can earn XNK the same way they used to earn credits, which could be referring other users or completing tasks/offers. Customers will benefit from the transition to XNK because it will be more fungible and tradable for other currencies, and Listia will benefit from no longer acting as a central bank who have to print and regulate Listia Credits. As P2P transactions cannot always be trusted, the token/blockchain will also act as an added level of security using escrow and third-party dispute resolution. When a buyer pays, XNK tokens are held in the smart contract until the buyer indicates the item has been received. There is also a staking function where the seller must stake their reputation against the tokens until the buyer receives the item. If something goes wrong in this process, users assign a human or automated mediator to transactions that acts as a third party smart contract to help settle disputes. As XNK tokens are used on the Listia platform, the more activities the platform has, the more valuable XNK tokens should be. _______________________________________ Opportunities - Listia is an established platform with 10 million registered user and is backed by reputable VCs. This shows the experience and capability of the team. - The token model makes sense. We believe the switch from Listia credit to XNK token would be smooth and not impact the user experience at all. - Since Listia will implement the use of XNK tokens soon after the end of ICO, there will be immediately traction with the usage of tokens. This is different from most other ICOs that will not be able to launch their platform until 3-12 months after ICO. _______________________________________ Concerns - From the public information that we gather (app ranking history, website traffic, the co-founder admitting that Listia’s growth is not as fast as everyone wanted), and last fund raising round being four years ago), we believe Listia is not a growing business. Since the switch to XNK token does not dramatically change the business model of Listia, we are not sure if the ICO will help Listia’s growth. - It is less than 2 weeks before presale begins and the future roadmap of the project is not released yet. We cannot analyze the soundness of the roadmap and level of planning of the team in executing its vision. _______________________________________ Disclaimer The information in this video is for educational purposes only and is not investment advice. Please do your own research before making any investment decisions. Cryptocurrency investments are volatile and high risk in nature. Don't invest more than what you can afford to lose. Crush Crypto makes no representations, warranties, or assurances as to the accuracy, currency or completeness of the content contained in this video or any sites linked to or from this video.
Views: 21092 Crush Crypto
GATE highlighting words in context with jape rules example
 
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This example takes a Course syllabus (mostly semantics courses) and highlights the reading lists using Jape grammars. It recognizes things like Van Fintel and Heim 2003 as a citation and Chapters 1, 3 and 8 as a reading selections and Week 1 as a due date (among others). Its another example of what GATE can do, in this case to help automate tasks like downloading a reading list. The files are in here https://github.com/cesine/GATEinSpring/tree/master/gate/WEB-INF/gate-files
Views: 12939 cesine0