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Data Mining: How You're Revealing More Than You Think
 
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Data mining recently made big news with the Cambridge Analytica scandal, but it is not just for ads and politics. It can help doctors spot fatal infections and it can even predict massacres in the Congo. Hosted by: Stefan Chin Head to https://scishowfinds.com/ for hand selected artifacts of the universe! ---------- Support SciShow by becoming a patron on Patreon: https://www.patreon.com/scishow ---------- Dooblydoo thanks go to the following Patreon supporters: Lazarus G, Sam Lutfi, Nicholas Smith, D.A. Noe, سلطان الخليفي, Piya Shedden, KatieMarie Magnone, Scott Satovsky Jr, Charles Southerland, Patrick D. Ashmore, Tim Curwick, charles george, Kevin Bealer, Chris Peters ---------- Looking for SciShow elsewhere on the internet? Facebook: http://www.facebook.com/scishow Twitter: http://www.twitter.com/scishow Tumblr: http://scishow.tumblr.com Instagram: http://instagram.com/thescishow ---------- Sources: https://www.aaai.org/ojs/index.php/aimagazine/article/viewArticle/1230 https://www.theregister.co.uk/2006/08/15/beer_diapers/ https://www.theatlantic.com/technology/archive/2012/04/everything-you-wanted-to-know-about-data-mining-but-were-afraid-to-ask/255388/ https://www.economist.com/node/15557465 https://blogs.scientificamerican.com/guest-blog/9-bizarre-and-surprising-insights-from-data-science/ https://qz.com/584287/data-scientists-keep-forgetting-the-one-rule-every-researcher-should-know-by-heart/ https://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853 http://dml.cs.byu.edu/~cgc/docs/mldm_tools/Reading/DMSuccessStories.html http://content.time.com/time/magazine/article/0,9171,2058205,00.html https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=all&_r=0 https://www2.deloitte.com/content/dam/Deloitte/de/Documents/deloitte-analytics/Deloitte_Predictive-Maintenance_PositionPaper.pdf https://www.cs.helsinki.fi/u/htoivone/pubs/advances.pdf http://cecs.louisville.edu/datamining/PDF/0471228524.pdf https://bits.blogs.nytimes.com/2012/03/28/bizarre-insights-from-big-data https://scholar.harvard.edu/files/todd_rogers/files/political_campaigns_and_big_data_0.pdf https://insights.spotify.com/us/2015/09/30/50-strangest-genre-names/ https://www.theguardian.com/news/2005/jan/12/food.foodanddrink1 https://adexchanger.com/data-exchanges/real-world-data-science-how-ebay-and-placed-put-theory-into-practice/ https://www.theverge.com/2015/9/30/9416579/spotify-discover-weekly-online-music-curation-interview http://blog.galvanize.com/spotify-discover-weekly-data-science/ Audio Source: https://freesound.org/people/makosan/sounds/135191/ Image Source: https://commons.wikimedia.org/wiki/File:Swiss_average.png
Views: 155354 SciShow
Import Data and Analyze with Python
 
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Python programming language allows sophisticated data analysis and visualization. This tutorial is a basic step-by-step introduction on how to import a text file (CSV), perform simple data analysis, export the results as a text file, and generate a trend. See https://youtu.be/pQv6zMlYJ0A for updated video for Python 3.
Views: 219625 APMonitor.com
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: 1774 OL Learn
LIC AAO 2019 | MCQ On Data Warehousing And Data Mining For LIC AAO IT OFFICER EXAM 2019 | Part 4
 
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Ace your Exam Preparation With Adda247 : http://bit.ly/2VZq3bJ Get 25% off on all products: Video Courses, Live Batches, Ebooks and Publications. Download Our App : http://bit.ly/2VOtH8d LIC AAO 2019 | MCQ On Data Warehousing And Data Mining For LIC AAO IT OFFICER EXAM 2019 | Part 4 | Day- 30 | Solanki Sir | 6:30 PM GET LIVE BATCHES AND VIDEO COURSE FOR SBI PO/Clerk , LIC AAO & IDBI PO LINKS BELOW BY : ADDA247 1) IDBI Assistant Manager/Executive 2019 By Radhey Sir, Amit Sir, Kush Sir, Ratnesh Sir : http://bit.ly/2UhpBEU 2) Radhey Ki Reasoning By Radhey Sir:http://bit.ly/2ZalQVs 3) SBI PO/Clerk By Anchal Ma'am:http://bit.ly/2Z157DR 4) Simple To Complicated By Akanksha ma'am: http://bit.ly/2Z1jRm1 5) Victory (Arithmetic & DI) By Ashish Sir:http://bit.ly/2Z0yWo7 6) Super Special (Quant+DI) By Amit Sir: http://bit.ly/2Z2lQqg 7) Complete English By Nimisha ma'am, Ratnesh Sir and Saurabh sir:http://bit.ly/2YYASxk 8) Complete SBI PO/Clerk 2019 Batch By Sumit Sir, Radhey sir, Anchal Ma'am, Kush sir :http://bit.ly/2YYCpU6 9) GOAL SBI PO/CLERK 2019 Batch By Sumit Sir, Akanksha Ma'am, Anchal Ma'am, Kush Sir: http://bit.ly/2YYCTcS 10) Lakshya 2.0 SBI PO/Clerk By Sumit Sir: http://bit.ly/2YWsmPv 11) Insurance Market & Financial Market Batch Current Affairs By Kush Sir: http://bit.ly/2U8oc3d 12) LIC AAO Phase-1 2019 Maths, Reasoning and English Live Batch By Anchal Ma'am, Amit Sir, Akanksha Ma'am: http://bit.ly/2UdphHi 13) Mission SBI | Complete English for SBI PO/CLERK By Nimisha Ma'm : http://bit.ly/2UnNESt Video Courses: 1. SBI Video Course: A) http://bit.ly/2YXv8nH B) http://bit.ly/2Z2Mkb5 2.LIC Video Course: A) http://bit.ly/2UbglSs 3.IDBI Video Course: A) http://bit.ly/2UpcLoc =============================================================== For Extra Dose Subscribe Our New Channel’s Now 1)“Adda247 : Bank & Insurance ⇒ http://bit.ly/2Z0LFHn 2) “Adda247 : Technical” ⇒ http://bit.ly/2Z2XpsL 3) “Adda247 : SSC & Railways” ⇒ http://bit.ly/2Z4F2DI 4) “Adda247 : Teaching Exams” ⇒http://bit.ly/2Z4FCBo Telegram Channel For Bankars Adda : ⇒Bankersadda- http://bit.ly/2UtkLV9 =============================================================== === Scheduled New Live Classes from (Mon-Sat) === ⇒ ⇒ ⇒ Adda247 Morning Classes ⇒ ⇒ ⇒ 8:00 AM - Current Affairs & The Hindu by Kush Sir 9:00 AM - The Hindu Editorial by Ritu/Nimisha Ma’am 10:00 AM - SBI/IBPS English by Nimisha/Anchal Ma’am 11:00 AM - SBI/IBPS Mathematics by Ashish /Amit Sir ⇒ ⇒ ⇒ Adda247 Afternoon Classes ⇒ ⇒ ⇒ 12:00 PM - SBI/IBPS Reasoning by Sachin Sir 1:00 PM - LIC English by Saurabh Sir /Nimisha Ma’am 2:00 PM - LIC Reasoning by Sachin Sir 3:00 PM - LIC Math’s by Amit Sir ⇒ ⇒ ⇒ Adda247 Evening Classes ⇒ ⇒ ⇒ 5:00 PM – Teasers 6:00 PM - Banking Bytes 7:00 PM - Newsmakers =============================================================== FOLLOW US ON SOCIAL MEDIA:- Facebook Pages: 1) ADDA247 : https://www.facebook.com/adda247live/ 2) SSCADDA : https://www.facebook.com/sscadda1/ 3) BANKERSADDA : https://www.facebook.com/bankersadda/ Instagram: adda_247: https://www.instagram.com/adda_247/ Twiiter: @adda247live: https://twitter.com/adda247live =============================================================== FOR VIDEO COURSE: - http://bit.ly/2Z1kgF3 FOR EBOOKS: - http://bit.ly/2Z0zkTB FOR ONLINE LIVE CLASSES CLICK HERE: - http://bit.ly/2Z1Znty ===================================================== #LIC #LIC_AAO #LIC_AAO_2019
CMU Database Systems - 24 Distributed OLAP Systems (Fall 2017)
 
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Slides PDF: http://15445.courses.cs.cmu.edu/fall2017/slides/24-distributedolap.pdf Annotated Video: https://scs.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=26766b90-eece-462c-8caf-77653d50afa5 Andy Pavlo (http://www.cs.cmu.edu/~pavlo/) 15-445/645 Intro to Database Systems (Fall 2017) Carnegie Mellon University http://15445.courses.cs.cmu.edu/fall2017
Views: 991 CMU Database Group
Logistic Regression Using Excel
 
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Predict who survives the Titanic disaster using Excel. Logistic regression allows us to predict a categorical outcome using categorical and numeric data. For example, we might want to decide which college alumni will agree to make a donation based on their age, gender, graduation date, and prior history of donating. Or we might want to predict whether or not a loan will default based on credit score, purpose of the loan, geographic location, marital status, and income. Logistic regression will allow us to use the information we have to predict the likelihood of the event we're interested in. Linear Regression helps us answer the question, "What value should we expect?" while logistic regression tells us "How likely is it?" Given a set of inputs, a logistic regression equation will return a value between 0 and 1, representing the probability that the event will occur. Based on that probability, we might then choose to either take or not take a particular action. For example, we might decide that if the likelihood that an alumni will donate is below 5%, then we're not going to ask them for a donation. Or if the probability of default on a loan is above 20%, then we might refuse to issue a loan or offer it at a higher interest rate. How we choose the cutoff depends on a cost-benefit analysis. For example, even if there is only a 10% chance of an alumni donating, but the call only takes two minutes and the average donation is 100 dollars, it is probably worthwhile to call.
Views: 198564 Data Analysis Videos
Social Media Analytics Introduction
 
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Please view the full copyright statement at: http://public.dhe.ibm.com/software/data/sw-library/services/legalnotice.pdf
Data Mining Tutorials
 
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Data Mining / Business Intelligence / Data WareHousing (Offline) https://play.google.com/store/apps/details?id=itsolutionever.karanponda.dmbi This FREE app will help you to understand Data Mining properly and teach you about how to Start Coding. Here we are covering almost all Functions, Libraries, attributes, references. The sequential tutorial let you know from basic to advance level. This "Data Mining Tutorial" is helpful for students to learn Coding step by step from basic to advance level. ***FEATURES*** * FREE of Cost * Easy to Learn Programming * Data Mining Basic Tutorial ***LESSONS*** # Data Mining Tutorial * Data Mining - Data Warehouse * Data Mining - Overview * Data Mining - Reporting/Analyzing * Data Mining - OLAP * Data Mining - OLAP Operations * Data Mining - OLAP vs OLTP * Data Mining - Data Mining * Data Mining - Terminology * Data Mining - Classification & Prediction * Data Mining - Issues regarding Classification & Prediction * Data Mining - Task Primitives * Data Mining - Mining Issues * Data Mining - Association Rules * Data Mining - Integration with DW * Data Mining - KDD * Data Mining - Data Processing * Data Mining - Integration/Transformation * Data Mining - Apriori * Data Mining - Metadata * Data Mining - Market Basket Analysis * Data Mining - Frequent Item Set * Data Mining - Clustering * Data Mining - Rule Based * Data Mining - Baye's Theorem * Data Mining - Decision Tree Induction * Data Mining - Big Data * Data Mining - Hadoop * Data Mining - HDFS * Data Mining - HDFS Architecture * Data Mining - MapReduce * Data Mining - Hadoop Services * Data Mining - Command Reference * Data Mining - Hadoop Configuration * Data Mining - Big Data Analytics
Views: 9 Karan Ponda
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: 480127 Brandon Weinberg
The Data Warehouse Tool Kit by Kimball & Ross Ch. 3 Retail Sales Power Point
 
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The Data Warehouse Tool Kit 3rd Edition Kimball & Ross Ch. 3 Retail Sales Power Point
Views: 1994 Brandon Shelly
TOXBANK Tutorials: Search the Data Warehouse
 
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When both protocols and experimental data are uploaded into the ToxBank data warehouse, keywords are selected from a list and used to index the record (https://services.toxbank.net/toxbank-ui/public/resources/ToxBank_Keyword_Hierarchy.pdf). These keywords can be used to search the warehouse and retrieve the protocol or data entries. This tutorial outlines how this keyword search is performed in the ToxBank data warehouse (https://services.toxbank.net/). By Dr. Glenn J. Myatt, Chief Scientific Officer, Leadscope, Inc.
Analysis Services tutorial. Creating OLAP cube. Introduction to data warehouse
 
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Analysis Services is a collection of OLAP supplied in Microsoft SQL Server. See more lessons https://www.youtube.com/watch?v=juKEUbav5kg&list=PL99-DcFspRUqBoCUN0b-dzHjWAX-3xyWC Lesson 1: Analysis Services Tutorial - Introduction https://www.youtube.com/watch?v=juKEUbav5kg&list=PL99-DcFspRUqBoCUN0b-dzHjWAX-3xyWC&index=1 Lesson 2: Working with dimensions https://www.youtube.com/watch?v=1q8UZ945d70&list=PL99-DcFspRUqBoCUN0b-dzHjWAX-3xyWC&index=2 Lesson 3: Aggregations https://www.youtube.com/watch?v=eXUdgOsbPu8&list=PL99-DcFspRUqBoCUN0b-dzHjWAX-3xyWC&index=3 Lesson 4: Partitions https://www.youtube.com/watch?v=9QQlc60k-3Y&list=PL99-DcFspRUqBoCUN0b-dzHjWAX-3xyWC&index=4 Lesson 5: OLAP Processing https://www.youtube.com/watch?v=JMppghX86Z8&list=PL99-DcFspRUqBoCUN0b-dzHjWAX-3xyWC&index=5 Lesson 6: Automatically Processing https://www.youtube.com/watch?v=499BtNjFx30&list=PL99-DcFspRUqBoCUN0b-dzHjWAX-3xyWC&index=6 Lesson 7: Role and Security https://www.youtube.com/watch?v=YX6Fg5kWUsU&list=PL99-DcFspRUqBoCUN0b-dzHjWAX-3xyWC&index=7 Lesson 8: #MDX https://www.youtube.com/watch?v=gicnvI86XdQ&list=PL99-DcFspRUqBoCUN0b-dzHjWAX-3xyWC&index=8 Lesson 9: KPI: Key Performance Indicators https://www.youtube.com/watch?v=ymPDKnmreDI&list=PL99-DcFspRUqBoCUN0b-dzHjWAX-3xyWC&index=9 Lesson 10: Actions https://www.youtube.com/watch?v=Y4V3kkgr9pA&list=PL99-DcFspRUqBoCUN0b-dzHjWAX-3xyWC&index=10 Lesson 11: Monitoring Cube Activity https://www.youtube.com/watch?v=KiY67RlfViY&list=PL99-DcFspRUqBoCUN0b-dzHjWAX-3xyWC&index=11
Data Warehouse tutorial. Creating an ETL.
 
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All Data Warehouse tutorial: https://www.youtube.com/playlist?list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt Lesson 1: Date Warehouse Tutorial - Introduction https://www.youtube.com/watch?v=AfIHINaKD9M&list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt&index=2&t=5s Lesson 2: Data Warehouse Tutorial - Creating database https://www.youtube.com/watch?v=b_0RrFXnlhc&list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt&index=3&t=511s Lesson 3: Data Warehouse Tutorial - Creating an OLAP cube - Data Warehouse for beginners https://www.youtube.com/watch?v=Z00VTv0GA9I&list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt&index=4&t=0s Lesson 4: Data Warehouse tutorial. Creating an ETL https://www.youtube.com/watch?v=9Akvz2x0az4&list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt&index=5&t=1371s Lesson 5: Data Warehouse Tutorial - Jobs - Data Warehouse for beginners https://www.youtube.com/watch?v=b997bACm_mE&list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt&index=6&t=13s Lesson 6: Data Warehouse Tutorial - Pivot Table in Excel and data presentation https://www.youtube.com/watch?v=n7K6JEvcYFg&list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt&index=7&t=3s This Data Warehouse video tutorial demonstrates how to create ETL (Extract, Load, Transform) package.
Job Roles For DATA ENTRY OPERATOR – Entry Level,DataBase,Arts,Science,WPM, Data Management
 
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Job Roles For DATA ENTRY OPERATOR : Know more about job roles and responsibility in DATA ENTRY . Coming to DATA ENTRY OPERATOR opportunities for freshers in India,Visit http://www.freshersworld.com?src=Youtube for detailed information,Job Opportunities,Education details and Career growth of DATA ENTRY OPERATOR. No matter what your educational background is, data entry operator jobs are available for all fresh candidates. People usually do not seek this position thinking that this is a low-level job. As a matter of fact, it is not lower than any other entry level position in corporate world. The main job of a data entry operator is to update, add and maintain data in a system or managing databases. The data entry operator is expected to insert or add data related to the company (both text and numerical) from a source file provided by the company. The candidate should also verify and sort the information as per given instruction. Other operational work includes generating routine reports and filing documents related to their work. Mostly, freshers with bachelor degree in arts and science are sought for this position. Even diploma candidates are opted for this position by many companies. Usually, candidates with professional degree, master degree or doctorate would not be sought for this position. The basic requirements are a) Knowledge and savvy in computer operation b) Expertise in MS-Office and other related software c) High typing speed – minimum market requirement is 40 WPM with 95% accuracy. d) Basic communication skills in English Usually, the candidates with good computer skill would be sought without regards to their educational background. Rotational shifts are rare and both male and female are sought. This job is also available in working-from-home option in some companies. There are short terms courses with certification for data entry offered by many institutions. Though it is not an essential certification, it would give a competitive edge over other candidates. Those who have working knowledge of Tally are sought for accountancy related data entry with a slightly higher pay. The same goes for those with commerce related educational background. With an increase in growth of BPO industry in India, there is a very high demand for data entry specialists. With one to three years experience in data entry, one can apply for jobs related to data management, document imaging, data mining, data processing and other related fields. If you want to grow in the same field, with three or more years of experience in data entry job, you can apply for senior data entry position or data analyzer positions. With more experience, you can apply for managerial positions like transaction processor, document processor and many others. Your scope is not restricted to back office operations. Candidates with a few years of experience in data entry can take up operational related jobs in KPO and customer service department. Yet, they would be considered as fresher in the new department. This job is for those who do not have a fancy degree and yet, want to take up corporate job. With this job, entry into corporate world becomes easy for all kinds of candidates. The academic excellence is not an important qualification for this job. Thus, candidates with backlog and those with moderate communication skill can apply for this position if, their typing skill is excellent. For more jobs & career information and daily job alerts, subscribe to our channel and support us. You can also install our Mobile app for govt jobs for getting regular notifications on your mobile. Freshersworld.com is the No.1 job portal for freshers jobs in India. Check Out website for more Jobs & Careers. http://www.freshersworld.com?src=Youtube - - ***Disclaimer: This is just a career guidance video for fresher candidates. The name, logo and properties mentioned in the video are proprietary property of the respective companies. The career and job information mentioned are an indicative generalised information. In no way Freshersworld.com, indulges into direct or indirect recruitment process of the respective companies.
Data Analytics Webinar
 
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The webinar covers - Tableau Demo - Text Mining and Sentiment Analysis in R
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: 176933 Rushdi Shams
Association analysis: Frequent Patterns, Support, Confidence and Association Rules
 
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This lecture provides the introductory concepts of Frequent pattern mining in transnational databases.
Views: 71216 StudyKorner
Lecture #08 - Indexing (OLAP) [CMU Database Systems Spring 2016]
 
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Annotated Video: http://cmudb.io/15721-s16-lect08 Slides PDF: http://15721.courses.cs.cmu.edu/spring2016/slides/08.pdf Reading List: http://15721.courses.cs.cmu.edu/spring2016/schedule.html#08 Andy Pavlo (http://www.cs.cmu.edu/~pavlo/) 15-721 Database Systems (Spring 2016) Carnegie Mellon University
Views: 903 CMU Database Group
SQL Data Discovery and Classification with SQL Server Management Studio - THR2166
 
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Are you challenged with data privacy regulations like GDPR? Or are you just trying to get an understanding of potentially sensitive data within your applications? In this theater session, learn about a new feature of SQL Server Management Studio called Data Discovery and Classification, which will help you both identify and classify the sensitive data in your database. See this feature in action in a live demo scenario--including labeling, discovery, and reporting.
Views: 609 Microsoft Ignite
XML Generator Transformation in INFORMATICA
 
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XML Generator transformation used to generate XML from relational tables. etl basics, etl testing, etl informatica, etl tools, etl informatica tutorial, etl informatica training, data warehousing and data mining, data warehousing pdf, informatica for beginners, informatica basic videos, informatica basics for beginners, basics to learn informatica, basic informatica interview question, basic etl concepts, basic etl testing concepts, learn informatica, how to learn informatica, tutorial informatica powercenter, transformation in informatica, transformation in informatica with example, informatica transformation, informatica training,
Views: 588 InformaticaTutorial
Transform Unstructured Data - H2L video
 
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In Informatica Developer, create a Data Processor transformation with a parser to transform a flat file source in PDF or text format to a flat file target in XML format. In this demo, we create a Data Processor transformation, create and configure a Script with a parser, preview the example source, defined the parser, preview the Data Processor transformation, and then add the Data Processor transformation to a mapping.
Views: 15714 Informatica Support
Convoy Effect in FCFS(First Come First Serve) Algorithm| Operating System
 
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Convoy effect in Operating System has been discussed with proper examples. It comes in FCFS(First Come First Serve) CPU Scheduling algorithm. Difference between Convoy effect and Starvation has also been discussed with a real-life example. 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 science outside 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 a free 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]l.com), 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 Quora profile: https://www.quora.com/profile/Jayanti-Khatri-Lamba Find me on Instagram: https://www.instagram.com/jayantikhatrilamba/
Executive Briefing: What Is Fast Data And Why Is It Important?
 
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Streaming data systems, so called Fast Data, promise accelerated access to information, leading to new innovations and competitive advantages. These systems, however, aren’t just faster versions of Big Data; they force architecture changes to meet new demands for reliability and dynamic scalability, more like microservices. This means new challenges for your organization. Whereas a batch job might run for hours, a stream processing application might run for weeks or months. This raises the bar for making these systems resilient against traffic spikes, hardware and network failures, and so forth. The good news is that there is a strong history of facing these demands in the world of microservices. In this webinar by Dr. Dean Wampler, VP of Fast Data Architecture at Lightbend, Inc., we will cut through the buzz around Fast Data and explore how to successfully exploit this new opportunity for innovation in how your organization leverages data. Specifically, Dean will review: - The business justification for transitioning from batch-oriented big data to stream-oriented fast data - The architectural and organizational changes that streaming systems require to meet their higher demands for reliability, resiliency, dynamic scalability, etc. - How some of these requirements can be met by leveraging what your organization already knows about microservice architectures
Views: 517 Lightbend
Medical Records Text Analytics Solution for Health Plans
 
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Medical Record Text Analytics for Health Plans - Delivers the capability to read free form text in medical records such as chart notes, operatory notes, physician correspondence, admission notes, discharge notes, consult notes, nuclear medicine reports, pathology lab reports, and so forth to discover both content and context, then analyze the results and transform those findings into actionable, context based structured data. This data can then be used to provide a dashboard to streamline medical review of claims pended for additional information and be loaded into a data warehouse and combined with existing claims data to enhance insight into medical management, provider quality, pay for performance, wellness programs, etc.. As part of the process, the solution can also be used to assign Snomed CT, LOINC, ICD, & CPT/HCPCS coding to free form text in medical records. Randall Wilcox IBM Text Analytics Group: Health Care Principle [email protected]
Views: 12121 IBMTAG
Deadlock | Necessary conditions for Deadlock | Operating Systems
 
13:31
Introduction to DEADLOCK with real life examples as well as Necessary (Coffman) conditions for Deadlock. 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 science outside 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 a Free 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 Quora profile: https://www.quora.com/profile/Jayanti-Khatri-Lamba Find me on Instagram: https://www.instagram.com/jayantikhatrilamba/
Parameter File in informatica
 
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Views: 1193 InformaticaTutorial
NFIRS Data Warehouse Application and Report Workflow
 
15:42
An overview of the National Fire Incident Reporting System (NFIRS) Data Warehouse application and the workflow for running a report.
Data Reconciliation and MIS Reporting using a Spreadsheet (MS Excel)
 
07:09
This video presents two simple functions in Microsoft Excel, that can be used to create meaningful Management Information System reports. The same functions and procedure can also be utilized for reconciling data.
Large-scale Text Mining for Biological Data
 
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http://togotv.dbcls.jp/20110307.html#p01  In this video, Goran Nenadic who is a Senior Lecturer (Associate Professor) in the School of Computer Science, University of Manchester and a group leader in the Manchester Interdisciplinary BioCenter talks about text mining from biomedical literature. The talk has been at Workshop on Parallel and Distributed Processing of Large Genome Data organized by GCOE Program: Deciphering Genome Sphere from Genome Big Bang.
Views: 1547 togotv
Data Modeling for Power BI
 
48:29
A data model is like the foundation for your house, get it right and everything else goes better. Join the Power BI desktop team in this session to learn about the key steps, and best practices, you need to take to ensure a good data model.
Views: 116917 Microsoft Power BI
Technical Sagar Not Leaving The Youtube | 100% Confirm News | Love You Technical Sagar😘😘
 
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Views: 63 Tech Hacks
How to Read an OTDR Trace - from Corning Cable Systems
 
04:49
http://www.fiberoptics4sale.com/c/Fiber-Optic-OTDR.html http://www.fiberoptics4sale.com An optical time-domain reflectometer (OTDR) is an optoelectronic instrument used to characterize an optical fiber. An OTDR injects a series of optical pulses into the fiber under test. It also extracts, from the same end of the fiber, light that is scattered (Rayleigh backscatter) or reflected back from points along the fiber. (This is equivalent to the way that an electronic time-domain reflectometer measures reflections caused by changes in the impedance of the cable under test.) The strength of the return pulses is measured and integrated as a function of time, and is plotted as a function of fiber length. An OTDR may be used for estimating the fiber's length and overall attenuation, including splice and mated-connector losses. It may also be used to locate faults, such as breaks, and to measure optical return loss. To measure the attenuation of multiple fibers, it is advisable to test from each end and then average the results, however this considerable extra work is contrary to the common claim that testing can be performed from only one end of the fiber. In addition to required specialized optics and electronics, OTDRs have significant computing ability and a graphical display, so they may provide significant test automation. However, proper instrument operation and interpretation of an OTDR trace still requires special technical training and experience. OTDRs are commonly used to characterize the loss and length of fibers as they go from initial manufacture, through to cabling, warehousing while wound on a drum, installation and then splicing. The last application of installation testing is more challenging, since this can be over extremely long distances, or multiple splices spaced at short distances, or fibers with different optical characteristics joined together. OTDR test results are often carefully stored in case of later fiber failure or warranty claims. Fiber failures can be very expensive, both in terms of the direct cost of repair, and consequential loss of service. OTDRs are also commonly used for fault finding on installed systems. In this case, reference to the installation OTDR trace is very useful, to determine where changes have occurred. Use of an OTDR for fault finding may require an experienced operator who is able to correctly judge the appropriate instrument settings to locate a problem accurately. This is particularly so in cases involving long distance, closely spaced splices or connectors, or PONs. OTDRs are available with a variety of fiber types and wavelengths, to match common applications. In general, OTDR testing at longer wavelengths, such as 1550 nm or 1625 nm, can be used to identify fiber attenuation caused by fiber problems, as opposed to the more common splice or connector losses. The optical dynamic range of an OTDR is limited by a combination of optical pulse output power, optical pulse width, input sensitivity, and signal integration time. Higher optical pulse output power, and better input sensitivity, combine directly to improve measuring range, and are usually fixed features of a particular instrument. However optical pulse width and signal integration time are user adjustable, and require trade-offs which make them application specific.
Views: 111998 FOSCO CONNECT
Data Mining For Accounting Part 3
 
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Dr. Edward Balli discusses the use of data mining as insight for accounting. This is part 1 of 4 that were presented at the 2009 Salford Analytics and Data Mining Conference in San Diego. To view the complete series of conference videos, please visit http://www.salford-systems.com/video/conference.html.
Views: 72 Salford Systems
How to Pass SNMR  | Final year IT | Importance of SNMR
 
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#SNMR#Lastmomenttuitions #lmt Video Credit Goes to Saurabh Solved Sum PDF : https://drive.google.com/open?id=17w9MR4R6OYpz8_lIsgSXX9vlCN4DnnCD Subscribe jaroor karna taki next importance ki notification aa jaye Other subject Course Analysis of Algoeithm : https://bit.ly/2L1HaJu COA lecture:https ://bit.ly/2DxZsfc Computer Graphics :https://bit.ly/2W56dg1 Operating system :https://bit.ly/2UWo6RG Comiler :https://bit.ly/2IVFsqu Distributed System :https://bit.ly/2vlummT Computer Networks :https://bit.ly/2W67za8 Machine Learning :https://bit.ly/2GHhq0X Discrete Mathematics :https://bit.ly/2UG5m3D DBMS Lectures :https://bit.ly/2VltsW3 Mobile Computing and Communication :https://bit.ly/2UREId8 DLDA Lecture :https://bit.ly/2XIbbQ2 Software Engineering :https://bit.ly/2J0L4j8 Placement Interview Series:http://tiny.cc/1kb55y Aptitude Lectures :https://bit.ly/2UUxup0 Python tutorial for Beginners :https://bit.ly/2UVuUPt SQL placement Series for Beginners :https://bit.ly/2XFN2c Motivation Student Life Series : http://tiny.cc/b8b55y
Views: 1625 Last moment tuitions
Hierarchical Clustering | Hierarchical Clustering in R |Hierarchical Clustering Example |Simplilearn
 
44:05
This hierarchical clustering video will help you understand what is clustering, what is hierarchical clustering, how does hierarchical clustering work, what is distance measure, what is agglomerative clustering, what is divisive clustering and you will also see a demo on how to group states based on their sales using clustering method. Clustering is the method of dividing the objects into clusters which are similar between them and are dissimilar to the objects belonging to another cluster. It is used to find data clusters such that each cluster has the most closely matched data. Prototype-based clustering, hierarchical clustering and density-based clustering are the three types of clustering algorithms. Lets us discuss hierarchical clustering in this video. In simple terms, Hierarchical clustering is separating data into different groups based on some measure of similarity. Now, let us get started and understand hierarchical clustering in detail. Below topics are explained in this "Hierarchical Clustering" video: 1. What is clustering? (00:33) 2. What is hierarchical clustering (04:28) 3. How hierarchical clustering works? (05:52) 4. Distance measure ( 07:24) 5. What is agglomerative clustering (11:03) 6. What is divisive clustering ( 16:14) 7. Demo: to group states based on their sales (18:32) Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 To access the slides, check this link: https://www.slideshare.net/Simplilearn/hierarchical-clustering-hierarchical-clustering-in-r-hierarchical-clustering-example-simplilearn/Simplilearn/hierarchical-clustering-hierarchical-clustering-in-r-hierarchical-clustering-example-simplilearn Watch more videos on Machine Learning: https://www.youtube.com/watch?v=7JhjINPwfYQ&list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=hierarchical-clustering-9U4h6pZw6f8&utm_medium=Tutorials&utm_source=youtube For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 8484 Simplilearn
Data Mining with Weka (4.2: Linear regression)
 
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Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 2: Linear 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: 44852 WekaMOOC
Monarch Data Migration
 
09:58
Copy and merge data between Salesforce orgs and archive data sets for back up, reporting, or compliance.
AI Is The New BI! Texifter Does Unstructured Data
 
27:13
Cloud-based software tools to quickly evaluate large amounts of text, survey, and Twitter data. Combines data science methods with e-discovery text analytics tools to shorten a process that used to last weeks or months. Subscribe to AI is The New BI! channel: https://www.youtube.com/channel/UCQfNRKMf9DVZoUn_gzC-6VQ?sub_confirmation=1
Views: 208 Inside Analysis
Data Science Full Course | Learn Data Science in 3 Hours | Data Science for Beginners | Edureka
 
02:53:05
** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification ** This Edureka video on "Data Science Full Course" provides an end to end, detailed and comprehensive knowledge on Data Science. This Data Science video will start with basics of Statistics and Probability and then moves to Machine Learning and Finally ends the journey with Deep Learning and AI. For Data-sets and Codes discussed in this video, drop a comment. This Data Science tutorial will be covering the following topics: 1:23 Evolution of Data 2:14 What is Data Science? 3:02 Data Science Careers 3:36 Who is a Data Analyst 4:20 Who is a Data Scientist 5:14 Who is a Machine Learning Engineer 5:44 Data Scientist Salary Trends 6:37 Data Scientist Road Map 9:06 Data Analyst Skills 10:41 Data Scientist Skills 11:47 Machine Learning Engineer Skills 12:53 Data Science Peripherals 13:17 What is Data ? 15:23 Variables & Research 17:28 Population & Sampling 20:18 Measures of Center 20:29 Measures of Spread 21:28 Skewness 21:52 Confusion Matrix 22:56 Probability 25:12 What is Machine Learning? 25:45 Features of Machine Learning 26:22 How Machine Learning works? 27:11 Applications of Machine Learning 34:57 Machine Learning Market Trends 36:05 Machine Learning Life Cycle 39:01 Important Python Libraries 40:56 Types of Machine Learning 41:07 Supervised Learning 42:27 Unsupervised Learning 43:27 Reinforcement Learning 46:27 Supervised Learning Algorithms 48:01 Linear Regression 58:12 What is Logistic Regression? 1:01:22 What is Decision Tree? 1:11:10 What is Random Forest? 1:18:48 What is Naïve Bayes? 1:30:51 Unsupervised Learning Algorithms 1:31:55 What is Clustering? 1:34:02 Types of Clustering 1:35:00 What is K-Means Clustering? 1:47:31 Market Basket Analysis 1:48:35 Association Rule Mining 1:51:22 Apriori Algorithm 2:00:46 Reinforcement Learning Algorithms 2:03:22 Reward Maximization 2:06:35 Markov Decision Process 2:08:50 Q-Learning 2:18:19 Relationship Between AI and ML and DL 2:20:10 Limitations of Machine Learning 2:21:19 What is Deep Learning ? 2:22:04 Applications of Deep Learning 2:23:35 How Neuron Works? 2:24:17 Perceptron 2:25:12 Waits and Bias 2:25:36 Activation Functions 2:29:56 Perceptron Example 2:31:48 What is TensorFlow? 2:37:05 Perceptron Problems 2:38:15 Deep Neural Network 2:39:35 Training Network Weights 2:41:04 MNIST Data set 2:41:19 Creating a Neural Network 2:50:30 Data Science Course Masters Program Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS Machine Learning Podcast: https://castbox.fm/channel/id1832236 Instagram: https://www.instagram.com/edureka_learning Slideshare: https://www.slideshare.net/EdurekaIN/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka #edureka #DataScienceEdureka #whatisdatascience #Datasciencetutorial #Datasciencecourse #datascience - - - - - - - - - - - - - - About the Master's Program This program follows a set structure with 6 core courses and 8 electives spread across 26 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands on expertise. By the end of the program you will be ready for seasoned Data Science job roles. - - - - - - - - - - - - - - Topics Covered in the curriculum: Topics covered but not limited to will be : Machine Learning, K-Means Clustering, Decision Trees, Data Mining, Python Libraries, Statistics, Scala, Spark Streaming, RDDs, MLlib, Spark SQL, Random Forest, Naïve Bayes, Time Series, Text Mining, Web Scraping, PySpark, Python Scripting, Neural Networks, Keras, TFlearn, SoftMax, Autoencoder, Restricted Boltzmann Machine, LOD Expressions, Tableau Desktop, Tableau Public, Data Visualization, Integration with R, Probability, Bayesian Inference, Regression Modelling etc. - - - - - - - - - - - - - - For more information, Please write back to us at [email protected] or call us at: IND: 9606058406 / US: 18338555775 (toll free)
Views: 83445 edureka!
Naïve Bayes Classifier -  Fun and Easy Machine Learning
 
11:59
Naive Bayes Classifier- Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS COURSE - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML ►MACHINE LEARNING COURSES - http://augmentedstartups.info/machine-learning-courses -------------------------------------------------------------------------------- Now Naïve Bayes is based on Bayes Theorem also known as conditional Theorem, which you can think of it as an evidence theorem or trust theorem. So basically how much can you trust the evidence that is coming in, and it’s a formula that describes how much you should believe the evidence that you are being presented with. An example would be a dog barking in the middle of the night. If the dog always barks for no good reason, you would become desensitized to it and not go check if anything is wrong, this is known as false positives. However if the dog barks only whenever someone enters your premises, you’d be more likely to act on the alert and trust or rely on the evidence from the dog. So Bayes theorem is a mathematic formula for how much you should trust evidence. So lets take a look deeper at the formula, • We can start of with the Prior Probability which describes the degree to which we believe the model accurately describes reality based on all of our prior information, So how probable was our hypothesis before observing the evidence. • Here we have the likelihood which describes how well the model predicts the data. This is term over here is the normalizing constant, the constant that makes the posterior density integrate to one. Like we seen over here. • And finally the output that we want is the posterior probability which represents the degree to which we believe a given model accurately describes the situation given the available data and all of our prior information. So how probable is our hypothesis given the observed evidence. So with our example above. We can view the probability that we play golf given it is sunny = the probability that we play golf given a yes times the probability it being sunny divided by probability of a yes. This uses the golf example to explain Naive Bayes. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 185740 Augmented Startups
Dedicated Database Walkthrough with iForm Mobile Platform
 
10:20
It's all about the data, and a dedicated database makes your data collection easier and more secure. This basic walkthrough will give a quick overview of the merirts of a dedicated database.
Views: 798 Zerion Software
Intelligent Invoice Capture for Netsuite: Intelligent OCR w/ChronoScan
 
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http://echovera.ca/solutions/intelligent-ocr-for-netsuite-netsuite-ap-automation/ Intelligent Data Capture for NetSuite integrates NetSuite with ChronoScan, one of the world’s leading (and cost-effective) Intelligent OCR platforms. How are you capturing invoice information for NetSuite? Historically invoice data capture is performed in two ways: manually keying in data from paper invoices; or the invoices are scanned with OCR (Optical Character Recognition) software and then manually mapped in order to capture essential information from relevant fields in the document. Intelligent OCR changes the playing field dramatically. It intuitively performs field mapping and data collection, freeing up time and making the entire process more efficient. It’s “next generation” OCR! ChronoScan is Intelligent OCR for NetSuite that: • Processes invoices directly from email – eliminating the need to print • Scans paper invoices, capturing relevant invoice fields • Gets high marks for ease of use and an intuitive interface • Integrates seamlessly with NetSuite • Most affordable intelligent OCR on the market today What’s the Advantage of Intelligent OCR Over Regular OCR? With standard OCR software, much time is spent on field-mapping, as a staff member must give the OCR software instructions as to where data is located on each scanned invoice. Time is also spent on data collection, as a staff member has to manually select the data zones on all of the emailed or scanned supplier invoices the organization receives. Intelligent OCR technology dramatically reduces the manual steps involved in both field mapping and data collection. Intelligent OCR is capable of automatically and intuitively detecting/learning the particular supplier invoices based on their respective layouts, allowing the scanning and/or importing emailed invoices in more efficiently. Intelligent OCR software also allows the user to search typical values from any document, “tagging” it, using a built-in database. EchoVera Inc. provides Intelligent OCR for NetSuite, and Purchase to Pay for NetSuite to companies and organizations looking to reduce costs and increase the efficiency of their accounts payable operations. Customers worldwide.
Views: 1452 EchoVera
T14 - Data Warehousing dan Decision Support System
 
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Teori Basis Data Lanjut - Minggu 14 - Politeknik Elektronika Negeri Surabaya - Bab Data Warehousing dan Decision Support System
Acquire unstructured data using the Mongo DB data access extension: SAP Lumira 1.28
 
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SAP Lumira enables us to acquire non-traditional data sources by taking advantage of data access extensions. In this video, we’ll install the extension for MongoDB, which stores data in documents, and acquire a dataset based on bitcoin transactions.
Views: 1402 SAPAnalyticsTraining
Introduction to Metadata Application Profiles (Karen Coyle)
 
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Successful data sharing requires that users of your data understand the data format, the data semantics, and the rules that govern your particular use of terms and values. Sharing often means the creation of “cross-walks” that transfer data from one schema to another using some or all of this information. However, cross-walks are time-consuming because the information that is provided is neither standardized nor machine-readable. Application profiles aim to make sharing data more efficient and more effective. They can also do much more than facilitate sharable data: APs can help metadata developers clarify and express design options; they can be a focus for consensus within a community; they can drive user interfaces; and they can be the basis for quality control. Machine-actionable APs could become a vital tool in the metadata toolbox and there is a clear need for standardization. Communities such as Dublin Core and the World Wide Web Consortium are among those working in this area.
Analyzing Big Data in less time with Google BigQuery
 
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Most experienced data analysts and programmers already have the skills to get started. BigQuery is fully managed and lets you search through terabytes of data in seconds. It’s also cost effective: you can store gigabytes, terabytes, or even petabytes of data with no upfront payment, no administrative costs, and no licensing fees. In this webinar, we will: - Build several highly-effective analytics solutions with Google BigQuery - Provide a clear road map of BigQuery capabilities - Explain how to quickly find answers and examples online - Share how to best evaluate BigQuery for your use cases - Answer your questions about BigQuery Qwiklabs: https://goo.gle/2JgSTQv
Views: 87713 Google Cloud Platform
ETL Microservices using Kafka for Fast Big Data - DataTorrent AppFactory
 
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DataTorrent AppFactory (https://www.datatorrent.com/appfactory) has a series of application templates. These templates reduce your total cost of ownership & time to market (launch). This is achieved as the bulk of the functional development is done and certified, specifically in terms of connectors to and from Cloud. The code is cloud-agnostic and can run on-prem too. All you need is to put in your custom logic. The templates include visualization dashboards for system and application metrics. The templates are certified from an operational point of view. Benchmarking, end-to-end exactly once, scale, fault tolerance is certified for Cloudera, Hortonworks, MapR, & Cloud (AWS, and soon for Azure). Using these templates thus reduces your total cost of ownership and reduce your time to value. We have had customers go to production under 3 months. This talk will be about how to download and launch ETL app with microservice architecture using Kafka in DataTorrent AppFactory. This would cover how to customize apps to suit your use-case. We would also show how to visualize the dashboard of the running application and how to add new widgets to the dashboard. In future sessions, we will present templates for Azure. Presenter: Yogi /Devendra Vyavahare is a committer at Apache Apex and an engineer at DataTorrent. He has 11+ years experience in building software on Big data, Business intelligence, Informal extraction, Text mining, Microscopy imaging, Pattern recognition, Bio-informatics.
Views: 346 DataTorrent
Power BI -  ETL na Prática
 
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Olá pessoal, sejam todos bem vindos a nossa primeira aula do Canal! Nesta primeira aula, eu mostro como juntar dados de 3 bases de dados diferentes, para montar um único e simples Dashboard. Link para acessar o Painel: https://app.powerbi.com/view?r=eyJrIjoiNmRhMzA0ZTgtYjA4Mi00YjBkLWE4NjEtZDQxZjhiM2QyMDg4IiwidCI6ImRhZjVhZmIyLTIxYTItNDM3NC1hY2E5LTFhYWJkMjExZWQ2ZCJ9