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The Best Way to Prepare a Dataset Easily
 
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In this video, I go over the 3 steps you need to prepare a dataset to be fed into a machine learning model. (selecting the data, processing it, and transforming it). The example I use is preparing a dataset of brain scans to classify whether or not someone is meditating. The challenge for this video is here: https://github.com/llSourcell/prepare_dataset_challenge Carl's winning code: https://github.com/av80r/coaster_racer_coding_challenge Rohan's runner-up code: https://github.com/rhnvrm/universe-coaster-racer-challenge Come join other Wizards in our Slack channel: http://wizards.herokuapp.com/ Dataset sources I talked about: https://github.com/caesar0301/awesome-public-datasets https://www.kaggle.com/datasets http://reddit.com/r/datasets More learning resources: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-prepare-data http://machinelearningmastery.com/how-to-prepare-data-for-machine-learning/ https://www.youtube.com/watch?v=kSslGdST2Ms http://freecontent.manning.com/real-world-machine-learning-pre-processing-data-for-modeling/ http://docs.aws.amazon.com/machine-learning/latest/dg/step-1-download-edit-and-upload-data.html http://paginas.fe.up.pt/~ec/files_1112/week_03_Data_Preparation.pdf Please subscribe! And like. And comment. That's what keeps me going. 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: 191594 Siraj Raval
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: 470480 Brandon Weinberg
Machine Learning Made Easy
 
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Get a Free Trial: https://goo.gl/C2Y9A5 Download a free Machine Learning with MATLAB Ebook: https://goo.gl/WmZXUR Download the Example code used in this demo: http://goo.gl/WmMvSX Machine learning is ubiquitous. From medical diagnosis, speech, and handwriting recognition to automated trading and movie recommendations, machine learning techniques are being used to make critical business and life decisions every moment of the day. Each machine learning problem is unique, so it can be challenging to manage raw data, identify key features that impact your model, train multiple models, and perform model assessments. In this session we explore the fundamentals of machine learning using MATLAB. Through several examples we review typical workflows for both supervised learning (classification) and unsupervised learning (clustering). Highlights include Accessing, exploring, analyzing, and visualizing data in MATLAB Using the Classification Learner app and functions in the Statistics and Machine Learning Toolbox to perform common machine learning tasks such as: Feature selection and feature transformation Specifying cross-validation schemes Training a range of classification models, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor, and discriminant analysis Performing model assessment and model comparisons using confusion matrices and ROC curves to help choose the best model for your data Integrating trained models into applications such as computer vision, signal processing, and data analytics.
Views: 49690 MATLAB
Advanced Data Mining with Weka (4.6: Application: Image classification)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 6: Application: Image classification http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/msswhT https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 8966 WekaMOOC
How to Pull in Data from a Website into an Excel Spreadsheet
 
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This tutorial explains how to index tables on specific websites and extract real time data into an Excel spreadsheet.
Views: 137881 edutechional
Automate Data Extraction – Web Scraping, Screen Scraping, Data Mining
 
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Extract data from unstructured sources with Automate. Learn more: https://www.helpsystems.com/product-lines/automate/data-scraping-extraction Modern businesses run on data. However, if the source of the data is unstructured, extracting what you need can be labor-intensive. For example, you may want to pull information from the body of incoming emails, which have no pre-determined structure. Especially important for today’s enterprises is gleaning data from the web. Using traditional methods, website data extraction can involve creating custom processing and filtering algorithms for each site. Then you might need additional scripts or a separate tool to integrate the scraped data with the rest of your IT infrastructure. Your busy employees don’t have time for that. Any company that handles a high volume of data needs a comprehensive automation tool to bridge the gap between unstructured data and business applications. Automate’s sophisticated data extraction, transformation, and transport tools keep your critical data moving without the need for tedious manual tasks or custom script writing. Learn more: https://www.helpsystems.com/product-lines/automate/data-scraping-extraction
Views: 3586 HelpSystems
Scraping From PDF To Excelsheet | Freelancer for beginners in hindi | Hindi
 
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In This Video Learn How to Scrape PDF offline, And Fill in the spreadsheet | Visit Our Website: http://myhindihelp.com Visite Our Blog: http://mindedsir.com More From Website: Data Entry Jobs Online Earning method http://myhindihelp.com/2016/12/data-entry-jobs/ IBPS kya hai Uske Bare Me Jankari http://myhindihelp.com/2016/07/ibps-kya-hai-ibps-selection-process-html/ Mobile Number Malik Ki Jankari Lagane Ke liye: http://myhindihelp.com/2016/01/mobil-number-ke-malik-aur-location-kaise-pta-kare-special-tips/ Don't forget subscribe our Channel to get more knowledge subscribe here : https://www.youtube.com/channel/UCUmP6ZeWpySqmiGqzE6cTDA We Regular share technical and interesting videos about Latest tech Mahaveer Jain : http://www.facebook.com/mahaveerbtp Like our Page : http://www.facebook.com/mindedsir Minded Sir: http://www.mindedsir.com
Views: 7910 Minded Sir
Anomaly Detection in Telecommunications Using Complex Streaming Data | Whiteboard Walkthrough
 
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In this Whiteboard Walkthrough Ted Dunning, Chief Application Architect at MapR, explains in detail how to use streaming IoT sensor data from handsets and devices as well as cell tower data to detect strange anomalies. He takes us from best practices for data architecture, including the advantages of multi-master writes with MapR Streams, through analysis of the telecom data using clustering methods to discover normal and anomalous behaviors. For additional resources on anomaly detection and on streaming data: Download free pdf for the book Practical Machine Learning: A New Look at Anomaly Detection by Ted Dunning and Ellen Friedman https://www.mapr.com/practical-machine-learning-new-look-anomaly-detection Watch another of Ted’s Whiteboard Walkthrough videos “Key Requirements for Streaming Platforms: A Microservices Advantage” https://www.mapr.com/blog/key-requirements-streaming-platforms-micro-services-advantage-whiteboard-walkthrough-part-1 Read technical blog/tutorial “Getting Started with MapR Streams” sample programs by Tugdual Grall https://www.mapr.com/blog/getting-started-sample-programs-mapr-streams Download free pdf for the book Introduction to Apache Flink by Ellen Friedman and Ted Dunning https://www.mapr.com/introduction-to-apache-flink
Views: 4894 MapR Technologies
Text Mining in R Tutorial: Term Frequency & Word Clouds
 
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This tutorial will show you how to analyze text data in R. Visit https://deltadna.com/blog/text-mining-in-r-for-term-frequency/ for free downloadable sample data to use with this tutorial. Please note that the data source has now changed from 'demo-co.deltacrunch' to 'demo-account.demo-game' Text analysis is the hot new trend in analytics, and with good reason! Text is a huge, mainly untapped source of data, and with Wikipedia alone estimated to contain 2.6 billion English words, there's plenty to analyze. Performing a text analysis will allow you to find out what people are saying about your game in their own words, but in a quantifiable manner. In this tutorial, you will learn how to analyze text data in R, and it give you the tools to do a bespoke analysis on your own.
Views: 68193 deltaDNA
I will do web scraping and data mining for lead generation
 
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Hi Welcome to my Gig. Here I'm expert in Data Mining, web Scraping, web crawling, Email extraction, Data Entry, Data Conversion and so on. I have lots of experience in this field. So just send your requirements to me before place the order. Here is my working area for this gig: Website scraping Data Mining Yellowpages scraping Business Lead generation Social Media Scrape Email list extraction Big database scraping/collection Use proxies for scraping Download images & content Extract data from PDF Data Entry, Copy Paste, CSV, PNG, PDF, EXCEL, OCR file conversion Please knock before place the order so that we can mutually accept the cost and delivery schedule of the project. Thanks place Order Here:https://www.fiverr.com/foysal123/do-web-scraping-and-data-mining-for-lead-generation
Views: 139 Foysal Rahaman
Data Mining 101
 
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Data Mining 101 Presentation by James E Holladay II for EMB 606
Views: 89 Jamie Holladay
Data Mining with Weka (1.3: Exploring datasets)
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 3: Exploring datasets http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 82675 WekaMOOC
How to Use Google Forms to Collect Data
 
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Learn to use Google Forms to collect data from specific people by sharing the link on email or on social media. It is a 3 step process that starts with creation of form and then next step is to distribute it to the audience and third step is to collect the data. All the three steps to use Google form have been explained in this tutorial. Share this video:http://youtu.be/s94fL4g0riI common use of various question types in Google forms is given below - Text — Respondents provide short answers Paragraph text — Use this for long answers Multiple choice — Respondents select one option from among several Checkboxes — select as many options from checkboxes. Choose from a list — respondents select one option from a dropdown menu Scale — rank something along a scale of numbers (e.g., from 1 to 5) Grid — respondents select a point from a two-dimensional grid Date — People filling the form can use a calendar picker to enter a date Time — respondents select a time (either a time of day or a duration of time) You can plan events, make a survey, give quiz to students or simply collect any data on Google Forms. It can save you from lot of physical effort as the collected data automatically gets saved in the linked spreadsheet.You can insert an image or video on Google forms and also set a customize theme for your form. Google forms is a gem in the Google docs, that's free to use and has a powerful tools like regular expressions that help to validate the data.Currently, only Text, Paragraph text, Check boxes, and Grid questions have support for validation. This video tutorial also covers certain Frequently asked questions given below- Can I capture respondents email address automatically? Can I receive notification on email every time a form is submitted? Can i send a personal thank you message to the respondents? Can i use Capcha on Google Form? Harish Bali is the creator of this video, he is a social media expert and an SEO. ............................................................................................... Learn to use Regular expressions in Google forms: https://support.google.com/docs/answer/3378864?hl=en Learn useful regular expressions for data validation by Labnol.org: http://www.labnol.org/internet/regular-expressions-forms/28380/ You can watch my other video on: How to use mail merge in gmail: https://www.youtube.com/watch?v=skrGMoq_TRA How to use Google drive to share files: https://www.youtube.com/watch?v=Itn3WIhQ6NQ Follow us: Blog on Tech Guide: http://www.technofare.com/ Google Plus Technofare : https://plus.google.com/+Technofareblog/posts Google Plus Harish Bali: https://plus.google.com/+harishBali/posts Facebook: https://www.facebook.com/technofare?ref=bookmarks Subscribe to Channel: https://www.youtube.com/user/Technofare Hope you found the detailed tutorial on how to use Google forms to collect data useful. Do share it with your friends on social media.
Views: 234414 Technofare
Data Structures and Algorithms Complete Tutorial Computer Education for All
 
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Computer Education for all provides complete lectures series on Data Structure and Applications which covers Introduction to Data Structure and its Types including all Steps involves in Data Structures:- Data Structure and algorithm Linear Data Structures and Non-Linear Data Structure on Stack Data Structure on Arrays Data Structure on Queue Data Structure on Linked List Data Structure on Tree Data Structure on Graphs Abstract Data Types Introduction to Algorithms Classifications of Algorithms Algorithm Analysis Algorithm Growth Function Array Operations Two dimensional Arrays Three Dimensional Arrays Multidimensional arrays Matrix operations Operations on linked lists Applications of linked lists Doubly linked lists Introductions to stacks Operations on stack Array based implementation of stack Queue Data Structures Operations on Queues Linked list based implementation of queues Application of Trees Binary Trees Types of Binary Trees Implementation of Binary Trees Binary Tree Traversal Preorder Post order In order Binary Search Tree Introduction to Sorting Analysis of Sorting Algorithms Bubble Sort Selection Sort Insertion Sort Shell Sort Heap Sort Merge Sort Quick Sort Applications of Graphs Matrix representation of Graphs Implementations of Graphs Breadth First Search Topological Sorting Subscribe for More https://www.youtube.com/channel/UCiV37YIYars6msmIQXopIeQ Find us on Facebook: https://web.facebook.com/Computer-Education-for-All-1484033978567298 Java Programming Complete Tutorial for Beginners to Advance | Complete Java Training for all https://youtu.be/gg2PG3TwLx4
Data Mining with Weka (5.4: Summary)
 
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Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 4: Summary http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/5DW24X https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 11732 WekaMOOC
Wrapping - Georgia Tech - Machine Learning
 
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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-627968607/m-601008609 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 1322 Udacity
ROC Curves and Area Under the Curve (AUC) Explained
 
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An ROC curve is the most commonly used way to visualize the performance of a binary classifier, and AUC is (arguably) the best way to summarize its performance in a single number. As such, gaining a deep understanding of ROC curves and AUC is beneficial for data scientists, machine learning practitioners, and medical researchers (among others). SUBSCRIBE to learn data science with Python: https://www.youtube.com/dataschool?sub_confirmation=1 JOIN the "Data School Insiders" community and receive exclusive rewards: https://www.patreon.com/dataschool RESOURCES: - Transcript and screenshots: https://www.dataschool.io/roc-curves-and-auc-explained/ - Visualization: http://www.navan.name/roc/ - Research paper: http://people.inf.elte.hu/kiss/13dwhdm/roc.pdf LET'S CONNECT! - Newsletter: https://www.dataschool.io/subscribe/ - Twitter: https://twitter.com/justmarkham - Facebook: https://www.facebook.com/DataScienceSchool/ - LinkedIn: https://www.linkedin.com/in/justmarkham/
Views: 315609 Data School
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
More Data Mining with Weka (1.1: Introduction)
 
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More Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 1: Introduction http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/Le602g https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 16431 WekaMOOC
Hak5 - Water Cooling, EXIF data mining and 25GB free cloud storage
 
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This week Darren heads to the Department of Spontaneous Combustion to meet with PC guru Colleen Kelly and get learned up on the arts of water cooling. Then we're joined by Mubix (aka Rob Fuller) for a discussion on EXIF data, geo location, and twitpic privacy. Plus, Shannon has the hookup on 25 gigs of free cloud storage!
Views: 20510 Hak5
Data Mining with Weka (1.1: Introduction)
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 1: Introduction http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 128791 WekaMOOC
Advanced Data Mining with Weka (1.1: Introduction)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 1: Introduction 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: 7486 WekaMOOC
Advanced Data Mining with Weka (4.3: Using Naive Bayes and JRip)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 3: Using Naive Bayes and JRip http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/msswhT https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 4400 WekaMOOC
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
Sampling & its 8 Types: Research Methodology
 
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Dr. Manishika Jain in this lecture explains the meaning of Sampling & Types of Sampling Research Methodology Population & Sample Systematic Sampling Cluster Sampling Non Probability Sampling Convenience Sampling Purposeful Sampling Extreme, Typical, Critical, or Deviant Case: Rare Intensity: Depicts interest strongly Maximum Variation: range of nationality, profession Homogeneous: similar sampling groups Stratified Purposeful: Across subcategories Mixed: Multistage which combines different sampling Sampling Politically Important Cases Purposeful Sampling Purposeful Random: If sample is larger than what can be handled & help to reduce sample size Opportunistic Sampling: Take advantage of new opportunity Confirming (support) and Disconfirming (against) Cases Theory Based or Operational Construct: interaction b/w human & environment Criterion: All above 6 feet tall Purposive: subset of large population – high level business Snowball Sample (Chain-Referral): picks sample analogous to accumulating snow Advantages of Sampling Increases validity of research Ability to generalize results to larger population Cuts the cost of data collection Allows speedy work with less effort Better organization Greater brevity Allows comprehensive and accurate data collection Reduces non sampling error. Sampling error is however added. Population & Sample @2:25 Sampling @6:30 Systematic Sampling @9:25 Cluster Sampling @ 11:22 Non Probability Sampling @13:10 Convenience Sampling @15:02 Purposeful Sampling @16:16 Advantages of Sampling @22:34 #Politically #Purposeful #Methodology #Systematic #Convenience #Probability #Cluster #Population #Research #Manishika #Examrace For IAS Psychology postal Course refer - http://www.examrace.com/IAS/IAS-FlexiPrep-Program/Postal-Courses/Examrace-IAS-Psychology-Series.htm For NET Paper 1 postal course visit - https://www.examrace.com/CBSE-UGC-NET/CBSE-UGC-NET-FlexiPrep-Program/Postal-Courses/Examrace-CBSE-UGC-NET-Paper-I-Series.htm types of sampling types of sampling pdf probability sampling types of sampling in hindi random sampling cluster sampling non probability sampling systematic sampling
Views: 384801 Examrace
More Data Mining with Weka (1.6: Working with big data)
 
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More Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 6: Working with big data http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/Le602g https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 10536 WekaMOOC
Data Mining with Weka (1.6: Visualizing your data)
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 6: Visualizing your data http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 70964 WekaMOOC
Introduction to Text and Data Mining
 
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Heard about Text and Data Mining (TDM) and wondering if it might be a good fit for your research? Find out what text and data mining is and how it can usefully be applied in a research context. Also learn about data sources for text and data mining projects and support, tools, and resources for learning more.
Views: 93 UniSydneyLibrary
genetic algorithm in artificial intelligence | genetic algorithm in hindi | Artificial intelligence
 
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Hey friends welcome to well academy here is the topic genetic algorithm in artificial intelligence in hindi DBMS Gate Lectures Full Course FREE Playlist : https://goo.gl/Z7AAyV Facebook Me : https://goo.gl/2zQDpD Click here to subscribe well Academy https://www.youtube.com/wellacademy1 GATE Lectures by Well Academy Facebook Group https://www.facebook.com/groups/1392049960910003/ Thank you for watching share with your friends Follow on : Facebook page : https://www.facebook.com/wellacademy/ Instagram page : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy genetic algorithm in artificial intelligence, genetic algorithm in artificial intelligence in hindi, genetic algorithm in artificial intelligence example, genetic algorithm in artificial intelligence tutorial, genetic algorithm in artificial intelligence in urdu, genetic algorithm in artificial intelligence hindi, genetic algorithm in hindi, genetic algorithm in ai, genetic algorithm artificial intelligence, genetic algorithm, genetic algorithm ai, genetic algorithm well academy, genetic algorithm crossover genetic algorithm tutorial genetic algorithm example genetic algorithm genetic algorithm fitness function genetic algorithm artificial intelligence artificial intelligence well academy well academy artificial intelligence artificial intelligence tutorial artificial intelligence in hindi artificial intelligence lecture artificial intelligence lecture in hindi
Views: 151455 Well Academy
Prediction of effective rainfall and crop water needs using data mining techniques
 
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Prediction of effective rainfall and crop water needs using data mining techniques- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project NETWORKING 1. A Non-Monetary Mechanism for Optimal Rate Control Through Efficient Cost Allocation 2. A Probabilistic Framework for Structural Analysis and Community Detection in Directed Networks 3. A Ternary Unification Framework for Optimizing TCAM-Based Packet Classification Systems 4. Accurate Recovery of Internet Traffic Data Under Variable Rate Measurements 5. Accurate Recovery of Internet Traffic Data: A Sequential Tensor Completion Approach 6. Achieving High Scalability Through Hybrid Switching in Software-Defined Networking 7. Adaptive Caching Networks With Optimality Guarantees 8. Analysis of Millimeter-Wave Multi-Hop Networks With Full-Duplex Buffered Relays 9. Anomaly Detection and Attribution in Networks With Temporally Correlated Traffic 10. Approximation Algorithms for Sweep Coverage Problem With Multiple Mobile Sensors 11. Asynchronously Coordinated Multi-Timescale Beamforming Architecture for Multi-Cell Networks 12. Attack Vulnerability of Power Systems Under an Equal Load Redistribution Model 13. Congestion Avoidance and Load Balancing in Content Placement and Request Redirection for Mobile CDN 14. Data and Spectrum Trading Policies in a Trusted Cognitive Dynamic Network Architecture 15. Datum: Managing Data Purchasing and Data Placement in a Geo-Distributed Data Market 16. Distributed Packet Forwarding and Caching Based on Stochastic NetworkUtility Maximization 17. Dynamic, Fine-Grained Data Plane Monitoring With Monocle 18. Dynamically Updatable Ternary Segmented Aging Bloom Filter for OpenFlow-Compliant Low-Power Packet Processing 19. Efficient and Flexible Crowdsourcing of Specialized Tasks With Precedence Constraints 20. Efficient Embedding of Scale-Free Graphs in the Hyperbolic Plane 21. Encoding Short Ranges in TCAM Without Expansion: Efficient Algorithm and Applications 22. Enhancing Fault Tolerance and Resource Utilization in Unidirectional Quorum-Based Cycle Routing 23. Enhancing Localization Scalability and Accuracy via Opportunistic Sensing 24. Every Timestamp Counts: Accurate Tracking of Network Latencies Using Reconcilable Difference Aggregator 25. Fast Rerouting Against Multi-Link Failures Without Topology Constraint 26. FINE: A Framework for Distributed Learning on Incomplete Observations for Heterogeneous Crowdsensing Networks 27. Ghost Riders: Sybil Attacks on Crowdsourced Mobile Mapping Services 28. Greenput: A Power-Saving Algorithm That Achieves Maximum Throughput in Wireless Networks 29. ICE Buckets: Improved Counter Estimation for Network Measurement 30. Incentivizing Wi-Fi Network Crowdsourcing: A Contract Theoretic Approach 31. Joint Optimization of Multicast Energy in Delay-Constrained Mobile Wireless Networks 32. Joint Resource Allocation for Software-Defined Networking, Caching, and Computing 33. Maximizing Broadcast Throughput Under Ultra-Low-Power Constraints 34. Memory-Efficient and Ultra-Fast Network Lookup and Forwarding Using Othello Hashing 35. Minimizing Controller Response Time Through Flow Redirecting in SDNs 36. MobiT: Distributed and Congestion-Resilient Trajectory-Based Routing for Vehicular Delay Tolerant Networks
Advanced Data Mining with Weka: trailer
 
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Advanced Data Mining with Weka: online course from the University of Waikato Trailer http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/EWLxD4 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 11073 WekaMOOC
Data Mining with Weka (5.2: Pitfalls and pratfalls)
 
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Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 2: Pitfalls and pratfalls http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/5DW24X https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 12543 WekaMOOC
Hierarchical Clustering Algorithm  شرح
 
43:51
Hierarchical Clustering Algorithm Data Mining
Views: 29458 Emad Tolba
Time Series Analysis in Python | Time Series Forecasting | Data Science with Python | Edureka
 
38:20
** Python Data Science Training : https://www.edureka.co/python ** This Edureka Video on Time Series Analysis n Python will give you all the information you need to do Time Series Analysis and Forecasting in Python. Below are the topics covered in this tutorial: 1. Why Time Series? 2. What is Time Series? 3. Components of Time Series 4. When not to use Time Series 5. What is Stationarity? 6. ARIMA Model 7. Demo: Forecast Future Subscribe to our channel to get video updates. Hit the subscribe button above. Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm #timeseries #timeseriespython #machinelearningalgorithms - - - - - - - - - - - - - - - - - About the Course Edureka’s Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Python Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR. During our Python Certification Training, our instructors will help you to: 1. Master the basic and advanced concepts of Python 2. Gain insight into the 'Roles' played by a Machine Learning Engineer 3. Automate data analysis using python 4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 6. Explain Time Series and it’s related concepts 7. Perform Text Mining and Sentimental analysis 8. Gain expertise to handle business in future, living the present 9. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). 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
Views: 81954 edureka!
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
Gini index based Decision Tree
 
04:03
How does a Decision Tree Work? A Decision Tree recursively splits training data into subsets based on the value of a single attribute. Splitting stops when every subset is pure (all elements belong to a single class) Code for visualising a decision tree - https://github.com/bhattbhavesh91/visualize_decision_tree If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer those. If you enjoy these tutorials & would like to support them then the easiest way is to simply like the video & give it a thumbs up & also it's a huge help to share these videos with anyone who you think would find them useful. Please consider clicking the SUBSCRIBE button to be notified for future videos & thank you all for watching. You can find me on: GitHub - https://github.com/bhattbhavesh91 Medium - https://medium.com/@bhattbhavesh91 #decisiontree #Gini #machinelearning #python #giniindex
Views: 31733 Bhavesh Bhatt
Naive Bayes Classifier Tutorial | Naive Bayes Classifier Example | Naive Bayes in R | Edureka
 
01:04:06
( Data Science Training - https://www.edureka.co/data-science ) This Naive Bayes Tutorial video from Edureka will help you understand all the concepts of Naive Bayes classifier, use cases and how it can be used in the industry. This video is ideal for both beginners as well as professionals who want to learn or brush up their concepts in Data Science and Machine Learning through Naive Bayes. Below are the topics covered in this tutorial: 1. What is Machine Learning? 2. Introduction to Classification 3. Classification Algorithms 4. What is Naive Bayes? 5. Use Cases of Naive Bayes 6. Demo – Employee Salary Prediction in R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #NaiveBayes #NaiveBayesTutorial #DataScienceTraining #Datascience #Edureka How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 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. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). 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 Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best."
Views: 49310 edureka!
Random Forest Tutorial | Random Forest in R | Machine Learning | Data Science Training | Edureka
 
01:07:14
( Data Science Training - https://www.edureka.co/data-science ) This Edureka Random Forest tutorial will help you understand all the basics of Random Forest machine learning algorithm. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn random forest analysis along with examples. Below are the topics covered in this tutorial: 1) Introduction to Classification 2) Why Random Forest? 3) What is Random Forest? 4) Random Forest Use Cases 5) How Random Forest Works? 6) Demo in R: Diabetes Prevention Use Case Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #RandomForest #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 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. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). 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 Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 62531 edureka!
Advanced Data Mining with Weka (3.6: Application: Functional MRI Neuroimaging data)
 
05:22
Advanced Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 6: Application: Functional MRI Neuroimaging data http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/8yXNiM https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 1450 WekaMOOC
Healthcare Data Mining with Matrix Models (KDD 2016)
 
03:15:57
Healthcare Data Mining with Matrix Models KDD 2016 Fei Wang Ping Zhang Joel Dudley In the last decade, advances in high-throughput technologies, growth of clinical data warehouses, and rapid accumulation of biomedical knowledge provided unprecedented opportunities and challenges to researchers in biomedical informatics. One distinct solution, to efficiently conduct big data analytics for biomedical problems, is the application of matrix computation and factorization methods such as non-negative matrix factorization, joint matrix factorization, tensor factorization. Compared to probabilistic and information theoretic approaches, matrix-based methods are fast, easy to understand and implement. In this tutorial, we provide a review of recent advances in algorithms and methods using matrix and their potential applications in biomedical informatics. We survey various related articles from data mining venues as well as from biomedical informatics venues to share with the audience key problems and trends in matrix computation research, with different novel applications such as drug repositioning, personalized medicine, and electronic phenotyping.
Research Methods - Introduction
 
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In this video, Dr Greg Martin provides an introduction to research methods, methedology and study design. Specifically he takes a look at qualitative and quantitative research methods including case control studies, cohort studies, observational research etc. Global health (and public health) is truly multidisciplinary and leans on epidemiology, health economics, health policy, statistics, ethics, demography.... the list goes on and on. This YouTube channel is here to provide you with some teaching and information on these topics. I've also posted some videos on how to find work in the global health space and how to raise money or get a grant for your projects. Please feel free to leave comments and questions - I'll respond to all of them (we'll, I'll try to at least). Feel free to make suggestions as to future content for the channel. SUPPORT: —————- This channel has a crowd-funding campaign (please support if you find these videos useful). Here is the link: http://bit.ly/GH_support OTHER USEFUL LINKS: ———————— Channel page: http://bit.ly/GH_channel Subscribe: http://bit.ly/GH_subscribe Google+: http://bit.ly/GH_Google Twitter: @drgregmartin Facebook: http://bit.ly/GH_facebook HERE ARE SOME PLAYLISTS ——————————————- Finding work in Global Health: http://bit.ly/GH_working Epidemiology: http://bit.ly/GH_epi Global Health Ethics: http://bit.ly/GH_ethics Global Health Facts: http://bit.ly/GH_facts WANT CAREER ADVICE? ———————————— You can book time with Dr Greg Martin via Google Helpouts to get advice about finding work in the global health space. Here is the link: http://bit.ly/GH_career -~-~~-~~~-~~-~- Please watch: "Know how interpret an epidemic curve?" https://www.youtube.com/watch?v=7SM4PN7Yg1s -~-~~-~~~-~~-~-
How to find the best model parameters in scikit-learn
 
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In this video, you'll learn how to efficiently search for the optimal tuning parameters (or "hyperparameters") for your machine learning model in order to maximize its performance. I'll start by demonstrating an exhaustive "grid search" process using scikit-learn's GridSearchCV class, and then I'll compare it with RandomizedSearchCV, which can often achieve similar results in far less time. Download the notebook: https://github.com/justmarkham/scikit-learn-videos Grid search user guide: http://scikit-learn.org/stable/modules/grid_search.html GridSearchCV documentation: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html RandomizedSearchCV documentation: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html Comparing randomized search and grid search: http://scikit-learn.org/stable/auto_examples/model_selection/plot_randomized_search.html Randomized search video: https://youtu.be/0wUF_Ov8b0A?t=17m38s Randomized search notebook: https://github.com/amueller/pydata-nyc-advanced-sklearn/blob/master/Chapter%203%20-%20Randomized%20Hyper%20Parameter%20Search.ipynb Random Search for Hyper-Parameter Optimization: http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf WANT TO GET BETTER AT MACHINE LEARNING? HERE ARE YOUR NEXT STEPS: 1) WATCH my scikit-learn video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A 2) SUBSCRIBE for more videos: https://www.youtube.com/dataschool?sub_confirmation=1 3) JOIN "Data School Insiders" to access bonus content: https://www.patreon.com/dataschool 4) ENROLL in my Machine Learning course: https://www.dataschool.io/learn/ 5) LET'S CONNECT! - Newsletter: https://www.dataschool.io/subscribe/ - Twitter: https://twitter.com/justmarkham - Facebook: https://www.facebook.com/DataScienceSchool/ - LinkedIn: https://www.linkedin.com/in/justmarkham/
Views: 85112 Data School
Topological Sorting (with Examples) | How to find all topological orderings of a Graph
 
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In today's Video I have explained Topological Sorting (with Examples) | How to find all topological orderings of a Graph Jenny’s Lectures CS/IT NET&JRF is a Free YouTube Channel providing Computer Science / Information Technology / Computer-related tutorials including Programming Tutorials, NET & JRF Coaching Videos, Algorithms, GATE Coaching Videos, UGC NET, NTA NET, JRF, BTech, MTech, Ph.D., tips and other helpful videos for Computer Science / Information Technology students to advanced tech theory and computer science lectures, Teaching Computer Science in Informal Space. Learning to teach computer scienceoutside the classroom…. YouTube a top choice for users that want to learn computer programming, but don't have the money or the time to go through a complete college/ Institute / Coaching Centre course. ... Jenny’s Lectures CS/IT NET&JRF is aFree YouTube Channel providing computer-related ... and educate students in science, technology and other subjects. If you have any further questions, query, topic, please don't hesitate to contact me. Please feel free to comment or contact by ([email protected]), if you require any further information. Main Topics: Algorithms, Applied Computer Science, Artificial Intelligence, Coding, Computer Engineering, Computer Networking,Design and Analysis Of Algorithms, Data Structures, Digital Electronics, Object Oriented Programming using C++/Java/Python, Discrete Mathematical Structures, Operating Systems Computer Simulation, Computing, Bit Torrent, Abstract, C, C++, Acrobat, Ada, Pascal, ADABAS, Ad-Aware, Add-in, Add-on, Application Development, Adobe Acrobat, Automatic Data Processing, Adware, Artificial Intelligence, AI, Algorithm, Alphanumeric, Apache, Apache Tomcat, API, Application Programming Interface, Applet, Application, Application Framework, Application Macro, Application Package, Application Program, Application Programmer, Application Server, Application Software, Application Stack, Application Suite, System Administrator, Ada Programming, Architecture, computer software, ASP, Active Server Pages, Assembly, Assembly Language, Audacity, AutoCAD, Autodesk, Auto sketch, Backup, Restore, Backup & Recovery, BASH, BASIC, Beta Version, Binary Tree, Boolean, Boolean Algebra, Boolean AND, Boolean logic, Boolean OR, Boolean value, Binary Search Tree, BST, Bug, Business Software, C Programming Language, Computer Aided Design, Auto CAD, National Testing Agency, NTA,CAD, Callback, Call-by-Reference, Call by reference, Call-by-Value, Call by Value, CD/DVD, Encoding, Mapping, Character, Class, Class Library, ClearCase, ClearQuest, Client, Client-Side, cmd.exe, Cloud computing, Code, Codec, ColdFusion, Command, Command Interpreter, Command.com, Compiler, Animation, Computer Game, Computer Graphics, Computer Science, CONFIG.SYS, Configuration, Copyright, Customer Relationship Management, CRM, CVS, Data, Data Architect, Data Architecture, Data Cleansing, Data Conversion, Data Element, Data Mapping, Data Migration, Data Modeling, Data Processing, Data Scrubbing, Data Structure , Data Transformation, Database Administration, Database Model, Query Language, Database Server, Data log, Debugger, Database Management System, DBMS, Data Definition Language, DDL, Dead Code, Debugger, Decompile, Defragment, Delphi, Design Compiler, Device Driver, Distributed, Data Mart, Data Mining, Data Manipulation Language, DML, DOS, Disk Operating System, Dreamweaver, Drupal, Data Warehouse, Extensible Markup Language, XML, ASCII, Fibonacci , Firefox, Firmware, GUI, Graphical User Interface, LINUX, UNIX, J2EE, Java 2 Platform, Enterprise Edition, Java, Java EE, Java Beans, Java Programming Language, JavaScript, JDBC, Java Database Connectivity, Kernel, Keyboard, Keygen, LAMP, MySQL, Perl, PHP, Python, Logic Programming, Locator, Fusion, Fission, Low-Level Language, Mac OS, Macintosh Operating System, Machine Code, Machine Language, Metadata, Microsoft Access, Microsoft .Net Framework, Microsoft .Net, Microsoft SQL Server, Microsoft Windows, Middleware, MIS, Management Information systems, Module, Mozilla, MS-DOS,Microsoft Disk Operating System, Magic User Interface, MUI, MySQL, Normalization, Numerical, Object-Oriented, Open Source, Solaris, Parallel Processing, Parallel, Patch, Pascal, PDF, Portable Document Format, Postgres, Preemptive, Program, Programming Language, QuickTime, Report Writer, Repository, Rewind, Runtime, Scripting Languages, Script, Search Engine, Software Life-Cycle, VBScript, Virtual Basic Script, Classes, Queues, Stack, B-Tree, Computer Science, Information Technology, IT, CSE
Web data extractor & data mining- Handling Large Web site Item | Excel data Reseller & Dropship
 
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Web scraping web data extractor is a powerful data, link, url, email tool popular utility for internet marketing, mailing list management, site promotion and 2 discover extractor, the scraper that captures alternative from any website social media sites, or content area on if you are interested fully managed extraction service, then check out promptcloud's services. Use casesweb data extractor extracting and parsing github wanghaisheng awesome web a curated list webextractor360 open source codeplex archive. It uses regular expressions to find, extract and scrape internet data quickly easily. Whether seeking urls, phone numbers, 21 web data extractor is a scraping tool specifically designed for mass gathering of various types. Web scraping web data extractor extract email, url, meta tag, phone, fax from download. Web data extractor pro 3. It can be a url, meta tags with title, desc and 7. Extract url, meta tag (title, desc, keyword), body text, email, phone, fax from web site, search 27 data extractor can extract of different kind a given website. Web data extraction fminer. 1 (64 bit hidden web data extractor semantic scholar. It is very web data extractor pro a scraping tool specifically designed for mass gathering of various types. The software can harvest urls, extracting and parsing structured data with jquery selector, xpath or jsonpath from common web format like html, xml json a curated list of promising extractors resources webextractor360 is free open source extractor. It scours the internet finding and extracting all relative. Download the latest version of web data extractor free in english on how to use pro vimeo. It can harvest urls, web data extractor a powerful link utility. A powerful web data link extractor utility extract meta tag title desc keyword body text email phone fax from site search results or list of urls high page 1komal tanejashri ram college engineering, palwal gandhi1211 gmail mdu rohtak with extraction, you choose the content are looking for and program does rest. Web data extractor free download for windows 10, 7, 8. Custom crawling 27 2011 web data extractor promises to give users the power remove any important from a site. A deep dive into natural language processing (nlp) web data mining is divided three major groups content mining, structure and usage. Web mining wikipedia web is the application of data techniques to discover patterns from world wide. This survey paper reports the basic web mining aims to discover useful information or knowledge from hyperlink structure, page, and usage data. Web data mining, 2nd edition exploring hyperlinks, contents, and web mining not just on the software advice. Data mining in web applications. Web data mining exploring hyperlinks, contents, and usage in web applications what is mining? Definition from whatis searchcrm. Web data mining and applications in business intelligence web humboldt universitt zu berlin. Web mining aims to dis cover useful data and web are not the same thing. Extracting the rapid growth of web in past two decades has made it larg est publicly accessible data source world. Web mining wikipedia. The web is one of the biggest data sources to serve as input for mining applications. Web data mining exploring hyperlinks, contents, and usage web mining, book by bing liu uic computer sciencewhat is mining? Definition from techopedia. Most useful difference between data mining vs web. As the name proposes, this is information gathered by web mining aims to discover useful and knowledge from hyperlinks, page contents, usage data. Although web mining uses many is the process of using data techniques and algorithms to extract information directly from by extracting it documents 19 that are generated systems. Web data mining is based on ir, machine learning (ml), statistics web exploring hyperlinks, contents, and usage (data centric systems applications) [bing liu] amazon. Based on the primary kind of data used in mining process, web aims to discover useful information and knowledge from hyperlinks, page contents, usage. Data mining world wide web tutorialspoint.
Views: 277 CyberScrap youpul
E05 - Data Preprocessing Topics - Machine learning course free ( Data Science Alive )
 
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*For the playlist , please click the below Link: https://www.youtube.com/watch?v=L7tEs7kraTQ&list=PL-1QQC56x1gGsEWQP1dZ4rOQvKyUhdttW #Data_science_alive #Machine_learning #No_1_Trending_video #Machine_learning_Python_R *Visit Our website : https://datasciencealive.wordpress.com/machine-learning/ *Please click the following link to download the dataset: https://datasciencealive.wordpress.com/data-set/ *In this session we will look into topics that will be covered on the data preprocessing techniques using pandas in python . In machine learning most of the time will be spend on data preprocessing , data mining and feature extraction . Hence please listen to this topic more carefully . *This is a Data science course . This is a full fledge course for free and we will cove all the main topics on the machine learning algorithm. This course is specifically designed to address all the queries from beginners to expert . Artificial intelligence ( AI ) is a bigger umbrella ,In that Machine learning ( ML ) and Deep Learning ( DL ) are part of Artificial Intelligence. *In this video we will have an overview on the topics that will be covered. On high level it will be *Data Preprocessing *Supervised Learning - Algorithm *Classification *Regression *Association *Unsupervised learning - Algorithm *Clustering *Dimensionality Reduction (PCA) *Semi -Supervised learning *Re- Enforcement learning *Best approach for Model selection *Intro to Deep Learning The above topics will be covered in-detail on the upcoming session which you can find it in the playlist . *For the playlist , please click the below Link: https://www.youtube.com/watch?v=L7tEs7kraTQ&list=PL-1QQC56x1gGsEWQP1dZ4rOQvKyUhdttW #Data_science_alive #Machine_learning #Machine_learning_Python_R #No_1_Trending_video
Views: 118 Data Science Alive
Fast & Free Data Analytics Part 1: Introduction
 
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This is the first in a 6-part series explaining how to quickly deploy a powerful, fast and scalable data analytics solution using the free components of the Elastic stack. In this video I'll introduce the goals and audience of this series and give a brief overview of what the remaining videos will cover. Full series: https://www.youtube.com/playlist?list=PL47fLIoyuij9xfhLrwCB3Emt1N9oNTuuF
Views: 43 Brian F
AnswerDock AI and Search Driven Analytics.
 
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AnswerDock is an AI-driven analytics solution that uses Natural Language Processing to provide answers to business users' questions, allowing them to make better and faster data-driven decisions, without the need for data analysts. You can try the product free by signing up here: https://answerdock.com/ You will be able to upload your own data and experience most of the features of AnswerDock in the free version. Using AnswerDock, business users create their own reports and dashboards by typing their questions, just like using a web search engine. For example, users can type "Top 10 Sales People by growth in number of leads this quarter". AnswerDock runs the analysis and displays the optimal visualization instantly. AnswerDock runs powerful data mining algorithms to answer questions asked in natural language, such as: • What drives my conversion rate up? • Why did Sales increase yesterday? • Whats driving shipment status to be delayed? • How does PageViews affect Revenue? AnswerDock connects to a variety of sources from excel files to relational databases (Mysql, SQL Server, … ) to 3rd-party APIs such as Google Analytics .Users can create dashboards combining multiple sources, enabling them to have an integrated view on their business. AnswerDock provides a comprehensive data platform with tons of features: • Natural Language Processing • Auto Chart Selection • 30+ Interactive chart type • 50+ Customization Options • Data mining and Insights Discovery • Analysis Explanation • Custom Keywords • Automatic Data Indexing • Sharing and Collaboration • Formula-based Columns • Datasets Joins • Administration Console • Scheduled Data Loads • Export to CSV, PNG or PDF • Column, Row and Dataset Permissions • Users Management • Interactive Dashboards Industry professionals from any business function can use AnswerDock to easily explore their company's data, using an intuitive search-like interface with no required training. AnswerDock supports professionals in Retail and Ecommerce, Finance and Insurance, Healthcare, Transportation and Logistics, Communications and Media, Manufacturing, among other industries. Music: https://www.bensound.com You can try the product free by signing up here: https://answerdock.com/
Different Data Mining Approaches for Forecasting Use of Bike Sharing System
 
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R Codes are available on below link: https://github.com/mayurkmane/ADM-Project-A12-Group Document related to this data mining study is available on below link: https://www.dropbox.com/s/r5qw4mofej23gbg/Group-A12%20ADM%20Project.pdf?dl=0 https://ie.linkedin.com/in/mayurkmane
Views: 118 Mayur Mane
5 - Beginning to Extract Data - Web Crawling with Python
 
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Best Web Crawling Method and Tutorial
Views: 17280 Umer Javed