Home
Search results “What is data mining all about”
What is Data Mining?
 
03:23
NJIT School of Management professor Stephan P Kudyba describes what data mining is and how it is being used in the business world.
Views: 396974 YouTube NJIT
Data Mining: How You're Revealing More Than You Think
 
11:13
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: 141612 SciShow
Last Minute Tutorials | Data mining | Introduction | Examples
 
04:13
Please feel free to get in touch with me :) If it helped you, please like my facebook page and don't forget to subscribe to Last Minute Tutorials. Thaaank Youuu. Facebook: https://www.facebook.com/Last-Minute-Tutorials-862868223868621/ Website: www.lmtutorials.com For any queries or suggestions, kindly mail at: [email protected]
Views: 38246 Last Minute Tutorials
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
36:36
( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using 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 #LogisticRegression #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 Please write back to us at [email protected] or call us at +918880862004 or 18002759730 for more information. Website: https://www.edureka.co/data-science 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. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 58608 edureka!
BADM 1.1: Data Mining Applications
 
11:59
This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining. It is part of a series of 37 videos, all of which are available on YouTube. For more information: www.dataminingbook.com twitter.com/gshmueli facebook.com/dataminingbook Here is the complete list of the videos: • Welcome to Business Analytics Using Data Mining (BADM) • BADM 1.1: Data Mining Applications • BADM 1.2: Data Mining in a Nutshell • BADM 1.3: The Holdout Set • BADM 2.1: Data Visualization • BADM 2.2: Data Preparation • BADM 3.1: PCA Part 1 • BADM 3.2: PCA Part 2 • BADM 3.3: Dimension Reduction Approaches • BADM 4.1: Linear Regression for Descriptive Modeling Part 1 • BADM 4.2 Linear Regression for Descriptive Modeling Part 2 • BADM 4.3 Linear Regression for Prediction Part 1 • BADM 4.4 Linear Regression for Prediction Part 2 • BADM 5.1 Clustering Examples • BADM 5.2 Hierarchical Clustering Part 1 • BADM 5.3 Hierarchical Clustering Part 2 • BADM 5.4 K-Means Clustering • BADM 6.1 Classification Goals • BADM 6.2 Classification Performance Part 1: The Naive Rule • BADM 6.3 Classification Performance Part 2 • BADM 6.4 Classification Performance Part 3 • BADM 7.1 K-Nearest Neighbors • BADM 7.2 Naive Bayes • BADM 8.1 Classification and Regression Trees Part 1 • BADM 8.2 Classification and Regression Trees Part 2 • BADM 8.3 Classification and Regression Trees Part 3 • BADM 9.1 Logistic Regression for Profiling • BADM 9.2 Logistic Regression for Classification • BADM 10 Multi-Class Classification • BADM 11 Ensembles • BADM 12.1 Association Rules Part 1 • BADM 12.2 Association Rules Part 2 • Neural Networks: Part I • Neural Nets: Part II • Discriminant Analysis (Part 1) • Discriminant Analysis: Statistical Distance (Part 2) • Discriminant Analysis: Misclassification costs and over-sampling (Part 3)
Views: 2648 Galit Shmueli
What is DATA MINING? What does DATA MINING mean? DATA MINING meaning, definition & explanation
 
03:43
What is DATA MINING? What does DATA MINING mean? DATA MINING meaning - DATA MINING definition - DATA MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps. The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.
Views: 6575 The Audiopedia
Introduction to Data Mining: Euclidean Distance & Cosine Similarity
 
04:51
Part two of our introduction to similarity and dissimilarity, we discuss euclidean distance and cosine similarity. -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8M8m0 See what our past attendees are saying here: https://hubs.ly/H0f8Lts0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... -- Vimeo: https://vimeo.com/datasciencedojo
Views: 17720 Data Science Dojo
DATA MINING | The Checkout | ABC1
 
05:43
Kirsten Drysdale finds out how retailers knew a teenager was pregnant before her parents did, in a story about the way our data is collected and used. How viewers can get involved in THE CHECKOUT: http://facebook.com/checkouttv http://twitter.com/checkouttv #thecheckout http://futube.net.au (where you can send in video complaints) [email protected] (email us directly)
Views: 110397 The Checkout
Data Analyst Job Description | What 4 Skills Will You Need To Be A Data Analyst?
 
04:38
In this video we are going to define the job description of a data analyst, what a data analyst does, and the best online course to become a data analyst. ► Full Playlist Explaining Data Jargon ( http://bit.ly/2mB4G0N ) ► Top 4 Best Laptops for Data Analysts ( https://youtu.be/Vtk50Um_yxA ) ► Break Into the Data Industry with the best data analytics online learning resources from Edureka! ( http://bit.ly/2yCbsac ) --- affiliate link to help support this channel!^ Currently the average pay for a data analyst is $76,419 on the button, according to glassdoor I receive a lot of questions about what it takes to become a data analyst and what is a data analyst. Clearing up what a data analyst does everyday and what that description means to someone looking to enter the data science industry What will you actually be asked to do on the day to day as a data analyst. ► Top 4 Responsibilities in the Daily Life of a Data Analyst: 1 ) Mathematics Although mathematics only makes up about 20% of the day to day life of a data analyst. It is still important to have a strong understanding of the foundations of mathematics. - Addition - Subtraction - Multiplication - Division - Most Importantly --- Statistics Data analytics is all about statistics. Most of the statistics will be handled by the tools you are working with, but in order to be a great data analyst it is best to know why the tools are producing specific results. A strong understanding of statistics will be useful to you. 2 ) Computer Programming You must be able to work proficiently in one or more computer programming languages. This make up for roughly 60%-70% of your daily work. in order to analyze data it must be queried (drawn) from a large data warehouse. You will use computer programming languages such as SQL, Python, and R to query data. Before we move on let me define the term Query, if it does not resonate with you. You need strong computer programming skills in order to accomplish this task. As a data analyst you will do a lot of drawing and analyzing data. ► For more info on databases, SQL, and other jargon check out our Video Series on Data Jargon ( https://www.youtube.com/playlist?list=PL_9qmWdi19yDhnzqVCAhA4ALqDoqjeUOr ) 3 ) Know the Tools of the Trade Once you query data from the database onto your workspace you will begin to utilize data analytics tools to process, scrub, and analyze data (data Jargon explained on our Video series ^^^). You will be able to perform these tasks by using tools like Hadoop, Open Refine, Tableau, Apache Spark, etc... As you process the data you will begin to see connections between the data sets. You will see some of the following errors and you will want to remove these in order to ensure that your data analysis is accurate: - Duplicated data - Improperly formatted data - Incomplete data - Inaccurate data - This data will corrupt your findings and could possibly lose you client or employer millions of dollars. Make sure you know how to use those data analytics tools WELL! 4 ) Communicate and Present Insights Data Analyst will also be called upon to clearly and consciously present your research to clients, managers, or executives. Ok, now I know you are curious if you are capable of learning all of these crucial skills. Yes, you can, but there is a clause. You have to learn from the best. The guys over at Edureka.co are the leading professionals in the big data training industry. Based out of India, home to over 101,000 individuals in the data science industry (at the time of this writing). They are eager to make a way for themselves in the new digital economy. They are on the cutting edge of data analytics and eager to teach it to anyone worldwide. Testimonies of increased salaries, new employment, and 597,089 (updated) satisfied learners make edureka the best choice to learn the skills you need in the data industry. Question is will you actually do it. Imagine deregulating yourself for the data industry. Right now, it is a black hole, you don't know what's inside, but it is screaming opportunity from the darkness. TURN ON THE LIGHT and break into the data industry. A future proof opportunity for the next decade and beyond. ► Edureka Big Data Masters Program ( http://bit.ly/2yCbsac ) affiliate link^ ------- SOCIAL Twitter ► @jobsinthefuture Facebook ►/jobsinthefuture Instagram ►@Jobsinthefuture WHERE I LEARN: (affiliate links) Lynda.com ► http://bit.ly/2rQB2u4 edX.org ► http://fxo.co/4y00 MY FAVORITE GEAR: (affiliate links) Camera ► http://amzn.to/2BWvE9o CamStand ► http://amzn.to/2BWsv9M Computer ► http://amzn.to/2zPeLvs Mouse ► http://amzn.to/2C0T9hq TubeBuddy ► https://www.tubebuddy.com/bengkaiser ► Download the Ultimate Guide Now! ( https://www.getdrip.com/forms/883303253/submissions/new ) Thanks for Supporting Our Channel!
Views: 85542 Ben G Kaiser
#FixCopyright:  Copyright & Research - Text & Data Mining (TDM) Explained
 
03:52
Read our blog post analysing the European Commission's (EC) text and data mining (TDM) exception and providing recommendations on how to improve it: http://bit.ly/2cE60sp Copy (short for Copyright) explains what text and data mining (TDM) is all about, and what hurdles researchers are currently facing. We also have a blog post on the TDM bits in the EC's Impact Assessment accompanying the proposal: http://bit.ly/2du9sYe Read more about the EC's copyright reform proposals in general: http://bit.ly/2cvAh0a
Views: 3187 FixCopyright
Introduction to Datawarehouse in hindi | Data warehouse and data mining Lectures
 
10:36
#datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://goo.gl/to1yMH if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 258345 Last moment tuitions
How Facebook Data Mining, And Your Info, Is Influencing The 2016 Election | TODAY
 
03:40
With the 2016 presidential election only 27 days away, we’re taking a look at how the campaigns are taking to social media in the hopes of trying to win the all-important millennial vote and how data mining on Facebook and other social platforms is influencing your view of the election. NBC News’ Jo Ling Kent reports for TODAY. Red, White and You is brought to you by Amazon. » Subscribe to TODAY: http://on.today.com/SubscribeToTODAY » Watch the latest from TODAY: http://bit.ly/LatestTODAY About: TODAY brings you the latest headlines and expert tips on money, health and parenting. We wake up every morning to give you and your family all you need to start your day. If it matters to you, it matters to us. We are in the people business. Subscribe to our channel for exclusive TODAY archival footage & our original web series. Connect with TODAY Online! Visit TODAY's Website: http://on.today.com/ReadTODAY Find TODAY on Facebook: http://on.today.com/LikeTODAY Follow TODAY on Twitter: http://on.today.com/FollowTODAY Follow TODAY on Google+: http://on.today.com/PlusTODAY Follow TODAY on Instagram: http://on.today.com/InstaTODAY Follow TODAY on Pinterest: http://on.today.com/PinTODAY How Facebook Data Mining, And Your Info, Is Influencing The 2016 Election | TODAY
Views: 5169 TODAY
Data Mining - Facebook part 1
 
09:19
This tutorial shows how to access,use and communicate with Facebook API using graph API explorer.It gives a brief idea about what kind of data we can retrieve from Facebook.
Views: 26118 Vikash Khairwal
INTRODUCTION TO DATA MINING IN HINDI
 
15:39
Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 106163 LearnEveryone
Noob's Guide To Bitcoin Mining - Super Easy & Simple
 
11:37
Some Helpful Links: • Buy Parts for a Mining Rig: http://amzn.to/2jSSsCz • Download NiceHash Miner: https://www.nicehash.com/?p=nhmintro • Choose a Wallet: https://goo.gl/d4QZVR • Coinbase: https://www.coinbase.com • Luno (in SA): https://www.luno.com/en/ • Kraken: https://www.kraken.com/ • Bitcoin.org: https://bitcoin.org/en/choose-your-wallet • Download Claymore Dual Eth Miner: https://goo.gl/SLNbDx • Download EWBF CUDA Zcash Miner: https://goo.gl/nqhuhL • Profitability Calculators: • https://www.nicehash.com/?p=calc • http://www.mycryptobuddy.com/ • https://www.cryptocompare.com/mining/calculator/ • Bitcoin News: http://www.coindesk.com/ • Support UFD via Bitcoin: 1G5n6qFafWSYf8CZikZTXXcThpoxKeXK1K Check out Wootware for all your PC rig needs: https://goo.gl/tTD16W Crosshair VI Hero: http://amzn.to/2rr7IWy Ryzen 7: http://amzn.to/2rHQnb5 Strix RX 580: http://amzn.to/2sKkZxk Dual RX 580: http://amzn.to/2rrnMrh Trident Z RGB: http://amzn.to/2prkDHI ASUS PCI WiFi Adapter: http://amzn.to/2teOAwm Enthoo Evolv: http://amzn.to/2oHZU5o HD120 RGB: http://amzn.to/2pfqYHQ Thermaltake Toughpower RGB 850W: http://amzn.to/2lSKNUv For the intro/outro music by Kalyptra: https://goo.gl/eVmyNVd Join the UFDisciple Discord server! - https://discord.gg/PApp82h My Twitter - http://www.twitter.com/ufdisciple My Facebook - http://www.facebook.com/ufdisciple My Instagram - http://www.instagram.com/ufdisciple
Views: 1963248 UFD Tech
Data Mining   KDD Process
 
03:08
KDD - knowledge discovery in Database. short introduction on Data cleaning,Data integration, Data selection,Data mining,pattern evaluation and knowledge representation.
What is Bitcoin Mining?
 
01:56
For more information: https://www.bitcoinmining.com and https://www.weusecoins.com What is Bitcoin Mining? Have you ever wondered how Bitcoin is generated? This short video is an animated introduction to Bitcoin Mining. Credits: Voice - Chris Rice (www.ricevoice.com) Motion Graphics - Fabian Rühle (www.fabianruehle.de) Music/Sound Design - Christian Barth (www.akkord-arbeiter.de) Andrew Mottl (www.andrewmottl.com)
Views: 6709005 BitcoinMiningCom
Introduction to Data Mining: Basic Data Types
 
04:29
Continuing with data fundamentals, we introduce you to the three data set types, Record, Ordered, and Graph. -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8Lkc0 See what our past attendees are saying here: https://hubs.ly/H0f8Lkk0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 10405 Data Science Dojo
Weka Data Mining Tutorial for First Time & Beginner Users
 
23:09
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: 440955 Brandon Weinberg
Linear Regression - Machine Learning Fun and Easy
 
07:47
Linear Regression - Machine Learning Fun and Easy ►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 Hi and welcome to a new lecture in the Fun and Easy Machine Learning Series. Today I’ll be talking about Linear Regression. We show you also how implement a linear regression in excel Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. Dependent Variable – Variable who’s values we want to explain or forecast Independent or explanatory Variable that Explains the other variable. Values are independent. Dependent variable can be denoted as y, so imagine a child always asking y is he dependent on his parents. And then you can imagine the X as your ex boyfriend/girlfriend who is independent because they don’t need or depend on you. A good way to remember it. Anyways Used for 2 Applications To Establish if there is a relation between 2 variables or see if there is statistically signification relationship between the two variables- • To see how increase in sin tax has an effect on how many cigarettes packs are consumed • Sleep hours vs test scores • Experience vs Salary • Pokemon vs Urban Density • House floor area vs House price Forecast new observations – Can use what we know to forecast unobserved values Here are some other examples of ways that linear regression can be applied. • So say the sales of ROI of Fidget spinners over time. • Stock price over time • Predict price of Bitcoin over time. Linear Regression is also known as the line of best fit The line of best fit can be represented by the linear equation y = a + bx or y = mx + b or y = b0+b1x You most likely learnt this in school. So b is is the intercept, if you increase this variable, your intercept moves up or down along the y axis. M is your slope or gradient, if you change this, then your line rotates along the intercept. Data is actually a series of x and y observations as shown on this scatter plot. They do not follow a straight line however they do follow a linear pattern hence the term linear regression Assuming we already have the best fit line, We can calculate the error term Epsilon. Also known as the Residual. And this is the term that we would like to minimize along all the points in the data series. So say if we have our linear equation but also represented in statisitical notation. The residual fit in to our equation as shown y = b0+b1x + e ------------------------------------------------------------ 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: 114460 Augmented Startups
What's difference?(Big data, predictive analytics, data science, data mining, business intelligence)
 
08:51
Download "Explore Me - Find everything nearby" App from playstore: https://play.google.com/store/apps/details?id=com.yogeshkorke.admin.exploreme This video describes the short difference between Big data analytics, predictive analytics, prescriptive analytics, descriptive analytics, business intelligence, data science, machine learning, data mining and their application with help of example store sales. Like, share and subscribe more such videos!
Views: 9070 Tech Storm
Data Mining Lecture - - Finding frequent item sets | Apriori Algorithm | Solved Example (Eng-Hindi)
 
13:19
In this video Apriori algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining in hindi, Finding frequent item sets, data mining, data mining algorithms in hindi, data mining lecture, data mining tools, data mining tutorial,
Views: 190945 Well Academy
Introduction to Data Mining: Basic Vocabulary
 
04:17
It all starts with the fundamentals! In this data mining session we give you all the background information, technical terminology, and basic knowledge that you will need to hit the ground running. In part 1 of this data mining video series, we cover what data is and the basic vocabulary associated with it. Topics: - Data and Data Types - Data Quality - Data Preprocessing - Similarity and Dissimilarity - Data Exploration and Visualization -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8LhN0 See what our past attendees are saying here: https://hubs.ly/H0f8LhR0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... -- Vimeo: https://vimeo.com/datasciencedojo
Views: 28322 Data Science Dojo
What is Text Mining?
 
01:49
An introduction to the basics of text and data mining. To learn more about text mining, view the video "How does Text Mining Work?" here: https://youtu.be/xxqrIZyKKuk
Views: 47692 Elsevier
Bitcoin Mining Explained
 
10:24
We are miners from 2013 looking to create community and help train and learn together as blockchain tech changes so quickly. Leave your thoughts in the comments, call in, ask us anything. Thank you for your time it means a lot to us. This is where you get that rig hosted and mine coins all day long! https://www.digie.io All the great Merchandise 10% off! http://allthingsdecentral.com?aff=650 ✨Bitcoin coins!✨ http://amzn.to/2u1VkRQ 💰To get Bitcoin start here!💰 https://www.coinbase.com/join/5822284a3239de006a91b885 📈Support the Channel with Crypto:📈 BTC- 1FUyrbMx3STdpkhJKpjNwLkn8kYBQFJQPA ETH- 0xe02135195519D35D16615bBE2fA2Aa40ce0baECE LTC- LUkeeDhPmmS9S2mMJCwNzt2xoYwgy3En9R 🦊Fellow Friend Channels🦊 Ben teaches you step by step what to do! https://www.youtube.com/channel/UChzLnWVsl3puKQwc5PoO6Zg All about your rig build https://www.youtube.com/user/BitsBeTrippin Crypto info news https://www.youtube.com/channel/UCHnmFRbEPiN0rX_0WaEfZsA Interviews https://www.youtube.com/channel/UClMX3Ln3JLo6CzGvA-JAE9A Trey’s Life https://www.youtube.com/channel/UCVH62tKvfMJS14Fc-6E1DPA/featured?view_as=public 📡Social Sites📡 Facebook Group- http://Facebook.com/groups/DigitalGoldGroup Twitch- https://www.twitch.tv/digitalgold_ Mixer- https://mixer.com/DigitalGold_llc Twitter- https://twitter.com/DigitalGold_LLC Website: https://www.digie.io Email us: [email protected] 📝Rules for the chat📝 Zero tolerance for foul language this is a family friendly place. Do not spam Stop posting addresses Respect fellow humans Use @digitalgold to get your question answered -~-~~-~~~-~~-~- Please watch: "Life of a Miner, From Altcoins to Bitcoins over 4 years " https://www.youtube.com/watch?v=heiPMY9gOWg -~-~~-~~~-~~-~-
Views: 507247 Digital Gold
Data Mining - Clustering
 
06:52
What is clustering Partitioning a data into subclasses. Grouping similar objects. Partitioning the data based on similarity. Eg:Library. Clustering Types Partitioning Method Hierarchical Method Agglomerative Method Divisive Method Density Based Method Model based Method Constraint based Method These are clustering Methods or types. Clustering Algorithms,Clustering Applications and Examples are also Explained.
All you should know about Big Data – Hadoop,Careers,Scope,Modules,Highpaid jobs
 
05:05
Get Recruitment Notifications of all private and govt jobs , Mock test details ,Previous year question papers only at Freshersworld.com – the No.1 jobsite for entry level candidates in India. (To register : http://freshersworld.com?src=Youtube ) – This video is all about “Careers and Training courses for Big Data There are a handful of working definitions for big data, but to me it is most simply put as data sets so large and complex that it becomes difficult or impossible to process them using traditional database management applications. Granted, the term may apply differently to different organizations. For a smaller company, facing hundreds of gigabytes of data for the first time may trigger a need to explore new tools. Other companies that generate tons of transactional data, like UPS, wouldn't flinch with their existing toolsets until they hit tens or hundreds of terabytes. For freshers who wants to start learning big data here are a few tips: 1. Begin with the basics: If you are looking at building a career in big data, you can start with developing the core aptitudes such as curiosity, agility, statistical fluency, research, scientific rigor and skeptical nature. You have to decide which facet of data investigation (data wrangling, management, exploratory analysis, prediction) are you looking at acquiring. The first step to learning big data is to develop basic level of familiarity with programming languages. 2. Experience in programming languages: Begin with developing basic data literacy and an analytic mindset by building knowledge of programming languages such as Java, C++, Pig Latin and HiveQL. Figure out where you want to apply your data analytics skills to describe, predict, and inform business decisions in the specific areas of marketing, human resources, finance, and operations. 3. Expertise in Hadoop: Developing knowledge about Hadoop Map-Reduce and Java is essential if you’re looking to be a high-performance data software engineer. 4. What are you looking for? If you are looking for a career switch to big data, begin with developing the skill sets required to work with Hadoop. A well-rounded understanding of Hadoop requires experience in large-scale distributed systems and knowledge of programming languages. 5. Data Analytics Skills: If you want to learn the fundamentals and want to get an indepth understanding of every aspect of Big Data, the resource material provided by Apache’s library is very useful. The Hadoop programme offered by Apache is an open-source software for reliable, scalable, distributed computing. Some of the other programmes offered are HBase Hive, Mahout, Pig ZooKeeper. 6. Online Courses: The big data universe is still very young, to get a well rounded expertise in big data it is important to learn and hone skills related to the subject. Decide on the course based on the skill set you're looking to get. Just by dedicating some time and energy, you can tackle learning big data with these free online classes. Applications of Big Data Big data includes problems that involve such large data sets and solutions that require a complex connecting the dots. You can see such things everywhere. 1. Quora and Facebook use Big data tools to understand more about you and provide you with a feed that you in theory should find it interesting. The fact that the feed is not interesting should show how hard the problem is. 2. Credit card companies analyze millions of transactions to find patterns of fraud. 3. There are similar problems in defense, retail, genomics, pharma, healthcare that requires a solution. The companies offering jobs on Big Data are : Qualcomm India Pvt Ltd, Accenture, Dev Solutions So let us summarize as Big Data is a group of problems and technologies related to the availability of extremely large volumes of data that businesses want to connect and understand. The reason why the sector is hot now is that the data and tools have reached a critical mass. This occurred in parallel with years of education effort that has convinced organizations that they must do something with their data treasure. 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 Download our app today to manage recruitment when ever and where ever you want : Link :https://play.google.com/store/apps/details?id=com.freshersworld.jobs&hl=en ***Disclaimer: This is just a training video for candidates and recruiters. The name, logo and properties mentioned in the video are proprietary property of the respective organizations. The Preparation tips and tricks are an indicative generalized information. In no way Freshersworld.com, indulges into direct or indirect promotion of the respective Groups or organizations.
Google & Facebook Use YOUR DATA for Cool/Creepy Things
 
06:38
- What are Google and Facebook doing behind the scenes? How often are we being tracked? Just how bad is the Facebook Messenger application? What else do these companies know about us? These answers are cool and creepy! Support us on Amazon! http://geni.us/RJ1nymB Newegg link for cool kids: http://bit.ly/2AkBuRt ---Our Studio Gear--- Panasonic Lumix G85 (Primary): http://amzn.to/2zoJ1AX StudioPRO 1000W Softboxes: http://amzn.to/2zCl5e5 Audio-Technica AT2035: http://amzn.to/2yblmk1 Yamaha MG10XU Mixer: http://amzn.to/2zCP2e6 Panasonic FZ1000 (Secondary): http://amzn.to/2iBoM8U Neewer Microphone Arm: http://amzn.to/2h9FEn3 Alphacool Build Mat: http://amzn.to/2zmXXzu PATREON: http://www.patreon.com/ScienceStudioYT TWITCH: http://www.twitch.tv/ScienceStudioYT FACEBOOK: http://www.facebook.com/ScienceStudioYT TWITTER: http://www.twitter.com/ScienceStudioYT INSTAGRAM: http://www.instagram.com/ScienceStudioYT Subscribe to the channel! MUSIC: 'Evening Aesthetics' by Veeshy Provided by Silk Music http://www.youtube.com/silkmusic DISCLOSURES: All Genius links are tied to our Amazon Associate account, from which we earn a small sales commission. Links containing a 'bit.ly' reference forwarding to Newegg are tied to our CJ account, from which we earn a small sales commission. All sponsored links and comments will contain the word "SPONSOR" or "AD." Any additional revenue stream will be disclosed with similar verbiage.
Views: 12515 Science Studio
Apriori Algorithm (Associated Learning) - Fun and Easy Machine Learning
 
12:52
Apriori Algorithm (Associated Learning) - 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 Limited Time - Discount Coupon Apriori Algorithm The Apriori algorithm is a classical algorithm in data mining that we can use for these sorts of applications (i.e. recommender engines). So It is used for mining frequent item sets and relevant association rules. It is devised to operate on a database containing a lot of transactions, for instance, items brought by customers in a store. It is very important for effective Market Basket Analysis and it helps the customers in purchasing their items with more ease which increases the sales of the markets. It has also been used in the field of healthcare for the detection of adverse drug reactions. A key concept in Apriori algorithm is that it assumes that: 1. All subsets of a frequent item sets must be frequent 2. Similarly, for any infrequent item set, all its supersets must be infrequent too. ------------------------------------------------------------ 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: 46545 Augmented Startups
Data Science vs Big Data vs Data Analytics | Simplilearn
 
01:40
Data is everywhere. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. According to IBM, 2.5 billion gigabytes (GB) of data was generated every day in 2012. An article by Forbes states that Data is growing faster than ever before and by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet. Which makes it extremely important to at least know the basics of the field. After all, here is where our future lies. In this video, we will differentiate between the Data Science, Big Data, and Data Analytics, based on what it is, where it is used, the skills you need to become a professional in the field, and the salary prospects in each field. For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 162495 Simplilearn
What is machine learning and how to learn it ?
 
12:09
http://www.LearnCodeOnline.in Machine learning is just to give trained data to a program and get better result for complex problems. It is very close to data mining. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with: The heavily hyped, self-driving Google car? The essence of machine learning. Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life. Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation. Fraud detection? One of the more obvious, important uses in our world today. fb: https://www.facebook.com/HiteshChoudharyPage homepage: http://www.hiteshChoudhary.com
Views: 723360 Hitesh Choudhary
How Artificial Neural Network (ANN) Algorithm Work | Data Mining | Introduction to Neural Network
 
09:58
#ArtificialNeuralNetwork | Beginners guide to how artificial neural network model works. Learn how neural network approaches the problem, why and how the process works in ANN, various ways errors can be used in creating machine learning models and ways to optimise the learning process. - Watch our new free Python for Data Science Beginners tutorial: https://greatlearningforlife.com/python - Visit https://greatlearningforlife.com our learning portal for 100s of hours of similar free high-quality tutorial videos on Python, R, Machine Learning, AI and other similar topics Know More about Great Lakes Analytics Programs: PG Program in Business Analytics (PGP-BABI): http://bit.ly/2f4ptdi PG Program in Big Data Analytics (PGP-BDA): http://bit.ly/2eT1Hgo Business Analytics Certificate Program: http://bit.ly/2wX42PD #ANN #MachineLearning #DataMining #NeuralNetwork About Great Learning: - Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. - Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM - What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U - Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670 - Want to know more about the careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU - For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1 - Learn More at: https://www.greatlearning.in/ For more updates on courses and tips follow us on: - Google Plus: https://plus.google.com/u/0/108438615307549697541 - Facebook: https://www.facebook.com/GreatLearningOfficial/ - LinkedIn: https://www.linkedin.com/company/great-learning/
Views: 67017 Great Learning
productronica 2017 - Process Optimizing through Data Mining and Machine Learning
 
04:24
Florian Schwarz: "A warm welcome to productronica 2017. The special shows here are a big highlight – because that's where you can experience electronics manufacturing live!" "Founded in April 2017: the research fab Microelectronics Germany. This is where research capacities all over the country are bundled together and connected, to give the fab more weight internationally as a centre for microelectronics."   "Ah, Dr. Olowinsky. Hello!"    "Laser microwelding. What exactly are we looking at here?"   Dr. Alexander Olowinsky: "Laser microwelding is an established method in electronics and precision engineering for creating electrical and mechanical connections.Here you can see a laser beam melting material – and that's what creates the connection. In this particular version, the laser head contains the beam guidance, beam forming and mechanical pressing combined, for a flexible manufacturing process."   Florian Schwarz: "And what are the areas of application?"   Dr. Alexander Olowinsky: "What you see here: classic battery technology, production of battery modules and of battery packs, production of electrical connections,all the way to printed circuit board technology, because we need to create connections there too."   Florian Schwarz: "Dr. Olowinsky, thanks a lot!" Florian Schwarz: "From microelectronics to the special show devoted to hardware data mining.With me now is Ulf Oestermann, business developer at Fraunhofer IZM.Good morning!"   FlorianSchwarz: "Mr. Oestermann, what's the connection between microelectronics and hardware data mining?"   Ulf Oestermann: "The research fab Microelectronics Germany supposed to develop technologies and processes for the future. And they then have to be ported into mass production and scaled, so that they're ready to use there. That's exactly what hardware data mining is all about – showing what data records accumulate at what location in the individual process steps, and how robust they have to be in order to be used."   Florian Schwarz: "So we're talking about 'digging' data? Can we take a closer look?"   Ulf Oestermann: "Sure. No problem."   Ulf Oestermann: "Based on the data matrix code, you can immediately establish when this subassembly was manufactured, at what temperature, and in what humidity, and then conclusions can be drawn about possible errors."   Florian Schwarz: "I guess it helps save on resources – only having to replace individual components?"   Ulf Oestermann: "It's showing how thick wire is bonded. A very, very large number of wires are needed to get a high current density in the contact."   Florian Schwarz: "Mr. Oestermann, thanks very much for the tour. Hardware data mining. I'm going to the VDMA now to see what's being done with the data. And you? Back to work?"   Ulf Oestermann: "That's right!"   Florian Schwarz: "Ok - thanks. Ciao! We've just mined and collected the data. The data has to go somewhere, it has to be processed. And that brings me to the special show of the VDMA: "Smart-Data-Future Manufacturing."   "With me now is Mr. Müller from the VDMA. I've just taken a look round your stand. There's a lot of data being generated here. What's going to be done with it?"    Daniel Müller: "In the next stage, it's simply stored in various cloud systems, to make the long-term data actually usable. For models, for instance – like predictive maintenance."   Florian Schwarz: "Smart Data. How do you see the future of that?"   Daniel Müller: "A very exciting future topic is machinelearning - where companies try to make machines learn. So they can avoid errors, or correct them, all by themselves."   Florian Schwarz: "Wow. Thank you very much, Mr. Müller! Smart Data Future Manufacturing – it's a topic we're going to keep a close eye on. Well, that's all from productronica 2017. I'm already looking forward to 2019! Goodbye!"
Views: 316 productronica
Data Mining Lecture -- Decision Tree | Solved Example (Eng-Hindi)
 
29:13
-~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 169534 Well Academy
data mining fp growth | data mining fp growth algorithm | data mining fp tree example | fp growth
 
14:17
In this video FP growth algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining algorithms in hindi, data mining in hindi, data mining lecture, data mining tools, data mining tutorial, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining fp growth, data mining fp growth algorithm, data mining fp tree example, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining, fp growth algorithm, fp growth algorithm example, fp growth algorithm in data mining, fp growth algorithm in data mining example, fp growth algorithm in data mining examples ppt, fp growth algorithm in data mining in hindi, fp growth algorithm in r, fp growth english, fp growth example, fp growth example in data mining, fp growth frequent itemset, fp growth in data mining, fp growth step by step, fp growth tree
Views: 122995 Well Academy
How to: Fortnite Datamining - Fortnite Datamining Tutorial (Working)
 
04:16
EDIT: UE4 Version 4.20 is much better for doing this, use 4.20 and NOT 4.16 instead. DATAMINER DOWNLOAD - http://www.gildor.org/en/projects/umodel WHERE TO LOCATE FILES - C:\Program Files\Epic Games\Fortnite AES KEY V6.10 - 0x47C3245CFAB0F785D4DB3FA8E9967F887ECD623FA51308F1BD6BDB58FCFC6583 REDDIT LINK - https://www.reddit.com/r/FortNiteBR/comments/8ijkll/how_to_datamine_fortnite/ If you enjoyed this video and want to see more guides on fortnite, go ahead and drop this video a thumbs up, and leave a comment asking what you want me to cover next. This video is going to go over how to datamine fortnite, and is a definitive guide of datamining fortnite.
Views: 57059 Vercyx
What Is DataMining In Malayalam Use Of Our Data? | നമ്മുടെ ഡാറ്റക്ക് ഇത്ര വിലയുണ്ടോ?
 
04:28
In this video I'm explaining about data mining what is data mining and what is the use of data mining in marketing and business and why applications use our data and why hackers steal our data what is the use of our data Answers to all these queries in Malayalam. ഞാൻ ഇതുപോലുള്ള technology വാർത്തയുമായി ഇനിയും വരും കണ്ടതിനു നന്ദി തുടർന്നും ഇൗ ചാനെൽ സന്ദർശിക്കുക നമുക്ക് ഒരുമിച്ച് മുന്നേറാം മലയാളി എന്ന നിലയിൽ പരസ്പരം support ചെയ്യുക നമുക്ക് പൊളിക്കാം ബ്രോ. #DataMining #dataminingexplained #useOfDataMining നമുക്ക് ഒരുമിച്ച് ചേരാം WhatsApp group link-https://chat.whatsapp.com/9xLD2cBJ6btAxUmkP4ypn9 Facebook page link-https://www.facebook.com/Techno-Shanik-Malayalam Twitter-Take a look at Techno Shanik (@Shanik60645129): https://twitter.com/Shanik60645129?s=09 ഇൗ ചാനലിനെ കുറിച്ച് Techno Shanik enna ee channel technology മായി ബന്ധപ്പെട്ട് നിങ്ങൾക്ക് പഠിപ്പിക്കാൻ ആഗ്രഹിക്കുന്നു ദിവസം ഒരു വിഡിയോ എന്നതാണ് uploading രീതി Technology പഠിക്കൂ മലയാളത്തിൽ.
What is Data Science? | Introduction to Data Science | Data Science for Beginners | Simplilearn
 
49:49
This Data Science tutorial will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about. This Data Science tutorial will cover the following topics: 1. Need for Data Science? ( 00:50 ) 2. What is Data Science? ( 05:55 ) 3. Data Science vs Business intelligence ( 11:44 ) 4. Prerequisites for learning Data Science ( 16:36 ) 5. What does a Data scientist do? ( 24:31 ) 6. Data Science life cycle with use case ( 30:17 ) 7. Demand for Data scientists ( 47:17 ) To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the Slide here: https://goo.gl/3d2pNv Read the full article here: https://www.simplilearn.com/career-in-data-science-ultimate-guide-article?utm_campaign=What-is-Data-Science-KxryzSO1Fjs&utm_medium=Tutorials&utm_source=youtube Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6 #DataScienceWithPython #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course. Why learn Data Science? Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data. You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to: 1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics. Install the required Python environment and other auxiliary tools and libraries 2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions 3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave 4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package 5. Gain expertise in machine learning using the Scikit-Learn package The Data Science with python is recommended for: 1. Analytics professionals who want to work with Python 2. Software professionals looking to get into the field of analytics 3. IT professionals interested in pursuing a career in analytics 4. Graduates looking to build a career in analytics and data science 5. Experienced professionals who would like to harness data science in their fields Learn more at: https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training?utm_campaign=What-is-Data-Science-KxryzSO1Fjs&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 107799 Simplilearn
Data mining within to discover your true self | Tirthankar Dash | TEDxBangalore
 
12:35
In today's world we seem more lost despite the abundance of information. Tirthankar Dash, story teller & design thinker for leading businesses around the world gives us a cheat sheet to discover the inner story in each one of us, towards living a more fulfilled life Dash is the founder of Quantum360, a design firm that takes a human-centered, design-based approach to helping organisations innovate and design for a more human future. He is also the co-founder of StoryCompany, a company that serves as a search engine to find meaning at work and in life. StoryCo. is devoted to humanizing workplaces and helping each person explore themselves, their beliefs, behavior patterns and indeed their own story to arrive at their own unique answer to a fundamental question - “What makes my life meaningful?” This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 3378 TEDx Talks
How to use WEKA software for data mining tasks
 
04:54
In this video, I'll guide you how to use WEKA software for preprocessing, classifying, clustering, association. WEKA is a collection of machine learning algorithms for performing data mining tasks. #RanjiRaj #WEKA #DataMining Follow me on Instagram 👉 https://www.instagram.com/reng_army/ Visit my Profile 👉 https://www.linkedin.com/in/reng99/ Support my work on Patreon 👉 https://www.patreon.com/ranjiraj Get WEKA from here : http://www.cs.waikato.ac.nz/ml/weka/
Views: 16352 Ranji Raj
Data mining  harvesting and analytics. ( All you  need to know)
 
07:51
There is a whirlwind of videos and info on this but none that explained it properly to me. I went online and found out everything I needed to know about the data breaches and the implications of those breaches! I provided some links below in case you wish to educate yourself on whats happening with YOUR data! https://www.quora.com/What-is-the-difference-between-data-analytics-and-data-mining-1 https://www.connotate.com/are-you-screen-scraping-or-data-mining/ http://searchdatamanagement.techtarget.com/definition/data-scrubbing What is the difference between data warehousing and data mining? The main difference between data warehousing and data mining is that data warehousing is the process of compiling and organizing data into one common database, whereas data mining is the process of extracting meaningful data from that database.
Views: 46 Elle's place
How Netflix Implements Big Data Is All about You
 
04:56
Netflix is a major player in the so-called "big data" game, as the success of programs like House of Cards demonstrates, but they don't envy larger companies like Google or Facebook for one simple reason. Read more at BigThink.com: http://bigthink.com/videos/todd-yellin-on-netflix-and-big-data Follow Big Think here: YouTube: http://goo.gl/CPTsV5 Facebook: https://www.facebook.com/BigThinkdotcom Twitter: https://twitter.com/bigthink Transcript - So it's funny, big data has been kind of a cliché in Silicon Valley for the last few years: big data this, big data that. Big data is really one big mountain of garbage with little gems buried it in this tremendous trash heap, and you want to find those gems — you really want to find out what's going to make the experience better. So there are a lot of sophisticated machine learning algorithms that Netflix and other companies deploy to really figure out what are the gems that are going to make a better experience, and what's the rubbish that you want to separate out and push to the side? Once you find those gems, it doesn't make it a more alienated, machine experience — it actually makes it a more personal experience. It becomes much more about the individual member. When I first got to Netflix we were looking at other companies that were doing personalization and leveraging the kinds of data they couldn't learn from. And one company that obviously wasn't competitive with Netflix was also doing some interesting things was Pandora, the music company. And Netflix is in Silicon Valley and they're up in Oakland, not too far away, and we're down in the South Bay. So we went up to - we had a meeting, a little powwow, this was many years ago, with Pandora. And they were really small then and Netflix was much smaller and we were just comparing notes. What was interesting about Pandora is Pandora had the Music Genome Project where they were tearing apart and deconstructing lots of music on all these different dimensions and trying to really understand the music. And I remember back in these days, and this was like ten years ago, they had their walls lined with CDs all over and they had a whole line of people in this cramped office with headphones on and they were listening to music with this big spreadsheet open and tagging everything about it. At that's time at Netflix we were all about rating our titles on a one to five star system and we were very much using a lot of behavioral - a lot of algorithms around the behaviors of what users were doing and based on a lot of clustering techniques. We weren't really deconstructing the titles yet, we would get to that soon after — they weren't really deploying a lot of the collaborating filtering models that we were using on our algorithms. So we compared notes and we influenced each other and we only met a couple of times with them and were paying attention to what other companies were doing in terms of personalization. And based on these learnings, everyone kind of evolved across, it's not just Netflix, other companies that are trying to leverage big data to make it much easier to find something great to watch, great to listen to, great to read, great to buy, and figure out how to use, when to use human created data, a lot of metadata, a lot of deconstructing of what the material is people are watching or listening to or reading and so forth and how to use a lot of behavioral clustering data, what kinds of people are watching these kinds of shows and movies? What kinds of people are watching these or listening to these and so forth? Read Full Transcript Here: http://goo.gl/LPNkrK.
Views: 29298 Big Think
Kenneth Cukier: Big data is better data
 
15:56
Self-driving cars were just the start. What's the future of big data-driven technology and design? In a thrilling science talk, Kenneth Cukier looks at what's next for machine learning — and human knowledge. TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes (or less). Look for talks on Technology, Entertainment and Design -- plus science, business, global issues, the arts and much more. Find closed captions and translated subtitles in many languages at http://www.ted.com/translate Follow TED news on Twitter: http://www.twitter.com/tednews Like TED on Facebook: https://www.facebook.com/TED Subscribe to our channel: http://www.youtube.com/user/TEDtalksDirector
Views: 310984 TED
Support Vector Machine (SVM) - Fun and Easy Machine Learning
 
07:28
Support Vector Machine (SVM) - 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 A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. To understand SVM’s a bit better, Lets first take a look at why they are called support vector machines. So say we got some sample data over here of features that classify whether a observed picture is a dog or a cat, so we can for example look at snout length or and ear geometry if we assume that dogs generally have longer snouts and cat have much more pointy ear shapes. So how do we decide where to draw our decision boundary? Well we can draw it over here or here or like this. Any of these would be fine, but what would be the best? If we do not have the optimal decision boundary we could incorrectly mis-classify a dog with a cat. So if we draw an arbitrary separation line and we use intuition to draw it somewhere between this data point for the dog class and this data point of the cat class. These points are known as support Vectors – Which are defined as data points that the margin pushes up against or points that are closest to the opposing class. So the algorithm basically implies that only support vector are important whereas other training examples are ‘ignorable’. An example of this is so that if you have our case of a dog that looks like a cat or cat that is groomed like a dog, we want our classifier to look at these extremes and set our margins based on these support vectors. ------------------------------------------------------------ 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: 138426 Augmented Startups
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 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: 109082 Augmented Startups
R tutorial: What is text mining?
 
03:59
Learn more about text mining: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words Hi, I'm Ted. I'm the instructor for this intro text mining course. Let's kick things off by defining text mining and quickly covering two text mining approaches. Academic text mining definitions are long, but I prefer a more practical approach. So text mining is simply the process of distilling actionable insights from text. Here we have a satellite image of San Diego overlaid with social media pictures and traffic information for the roads. It is simply too much information to help you navigate around town. This is like a bunch of text that you couldn’t possibly read and organize quickly, like a million tweets or the entire works of Shakespeare. You’re drinking from a firehose! So in this example if you need directions to get around San Diego, you need to reduce the information in the map. Text mining works in the same way. You can text mine a bunch of tweets or of all of Shakespeare to reduce the information just like this map. Reducing the information helps you navigate and draw out the important features. This is a text mining workflow. After defining your problem statement you transition from an unorganized state to an organized state, finally reaching an insight. In chapter 4, you'll use this in a case study comparing google and amazon. The text mining workflow can be broken up into 6 distinct components. Each step is important and helps to ensure you have a smooth transition from an unorganized state to an organized state. This helps you stay organized and increases your chances of a meaningful output. The first step involves problem definition. This lays the foundation for your text mining project. Next is defining the text you will use as your data. As with any analytical project it is important to understand the medium and data integrity because these can effect outcomes. Next you organize the text, maybe by author or chronologically. Step 4 is feature extraction. This can be calculating sentiment or in our case extracting word tokens into various matrices. Step 5 is to perform some analysis. This course will help show you some basic analytical methods that can be applied to text. Lastly, step 6 is the one in which you hopefully answer your problem questions, reach an insight or conclusion, or in the case of predictive modeling produce an output. Now let’s learn about two approaches to text mining. The first is semantic parsing based on word syntax. In semantic parsing you care about word type and order. This method creates a lot of features to study. For example a single word can be tagged as part of a sentence, then a noun and also a proper noun or named entity. So that single word has three features associated with it. This effect makes semantic parsing "feature rich". To do the tagging, semantic parsing follows a tree structure to continually break up the text. In contrast, the bag of words method doesn’t care about word type or order. Here, words are just attributes of the document. In this example we parse the sentence "Steph Curry missed a tough shot". In the semantic example you see how words are broken down from the sentence, to noun and verb phrases and ultimately into unique attributes. Bag of words treats each term as just a single token in the sentence no matter the type or order. For this introductory course, we’ll focus on bag of words, but will cover more advanced methods in later courses! Let’s get a quick taste of text mining!
Views: 23399 DataCamp
Synergent: Extraordinary Marketing with Data Mining
 
00:16
Synergent knows marketing is more than just words and images. We use data mining to make your marketing campaigns extraordinary.
Views: 23572 SynergentCorp
Data Mining with Weka (1.5: Using a filter )
 
07:34
Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 5: Using a filter 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: 65164 WekaMOOC
Introduction to Data Mining: Missing & Duplicated Data
 
03:55
In part three of the introduction to Data Quality, we discuss missing values and duplicated data -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8M3Z0 See what our past attendees are saying here: https://hubs.ly/H0f8M400 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... -- Vimeo: https://vimeo.com/datasciencedojo
Views: 4914 Data Science Dojo
Data Warehouse Tutorial For Beginners | Data Warehouse Concepts | Data Warehousing | Edureka
 
01:38:50
***** Data Warehousing & BI Training: https://www.edureka.co/data-warehousing-and-bi ***** This Data Warehouse Tutorial For Beginners will give you an introduction to data warehousing and business intelligence. You will be able to understand basic data warehouse concepts with examples. The following topics have been covered in this tutorial: 1. What Is The Need For BI? 2. What Is Data Warehousing? 3. Key Terminologies Related To DWH Architecture: a. OLTP Vs OLAP b. ETL c. Data Mart d. Metadata 4. DWH Architecture 5. Demo: Creating A DWH - - - - - - - - - - - - - - Check our complete Data Warehousing & Business Intelligence playlist here: https://goo.gl/DZEuZt. #DataWarehousing #DataWarehouseTutorial #DataWarehouseTraining Subscribe to our channel to get video updates. Hit the subscribe button above. - - - - - - - - - - - - - - How it Works? 1. This is a 5 Week Instructor led Online Course, 25 hours of assignment and 10 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course: Edureka's Data Warehousing and Business Intelligence Course, will introduce participants to create and work with leading ETL & BI tools like: 1. Talend 5.x to create, execute, monitor and schedule ETL processes. It will cover concepts around Data Replication, Migration and Integration Operations 2. Tableau 9.x for data visualization to see how easy and reliable data visualization can become for representation with dashboards 3. Data Modeling tool ERwin r9 to create a Data Warehouse or Data Mart - - - - - - - - - - - - - - Who should go for this course? The following professionals can go for this course: 1. Data warehousing enthusiasts 2. Analytics Managers 3. Data Modelers 4. ETL Developers and BI Developers - - - - - - - - - - - - - - Why learn Data Warehousing and Business Intelligence? All the successful companies have been investing large sums of money in business intelligence and data warehousing tools and technologies. Up-to-date, accurate and integrated information about their supply chain, products and customers are critical for their success. With the advent of Mobile, Social and Cloud platform, today's business intelligence tools have evolved and can be categorized into five areas, including databases, extraction transformation and load (ETL) tools, data quality tools, reporting tools and statistical analysis tools. This course will provide a strong foundation around Data Warehousing and Business Intelligence fundamentals and sophisticated tools like Talend, Tableau and ERwin. - - - - - - - - - - - - - - Please write back to us at [email protected] or call us at +91 90660 20866 for more information. Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka - - - - - - - - - - - - - - Customer Review: Kanishk says, "Underwent Mastering in DW-BI Course. The training material and trainer are up to the mark to get yourself acquainted to the new technology. Very helpful support service from Edureka."
Views: 203462 edureka!

Tamsulosin hcl 0.4 mg cap what it is used for
Propecia side effects 2014 nfl
Aldactone 100 mg tabletas electronicas
Mobic 15 mg generico riverdale
Spectrophotometric method estimation metformin hydrochloride 1000mg