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Take the Full Course of Artificial Intelligence What we Provide 1) 28 Videos (Index is given down) 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in Artificial Intelligence Sample Notes : https://goo.gl/aZtqjh To buy the course click https://goo.gl/H5QdDU if you have any query related to buying the course feel free to email us : [email protected] Other free Courses Available : Python : https://goo.gl/2gftZ3 SQL : https://goo.gl/VXR5GX Arduino : https://goo.gl/fG5eqk Raspberry pie : https://goo.gl/1XMPxt Artificial Intelligence Index 1)Agent and Peas Description 2)Types of agent 3)Learning Agent 4)Breadth first search 5)Depth first search 6)Iterative depth first search 7)Hill climbing 8)Min max 9)Alpha beta pruning 10)A* sums 11)Genetic Algorithm 12)Genetic Algorithm MAXONE Example 13)Propsotional Logic 14)PL to CNF basics 15) First order logic solved Example 16)Resolution tree sum part 1 17)Resolution tree Sum part 2 18)Decision tree( ID3) 19)Expert system 20) WUMPUS World 21)Natural Language Processing 22) Bayesian belief Network toothache and Cavity sum 23) Supervised and Unsupervised Learning 24) Hill Climbing Algorithm 26) Heuristic Function (Block world + 8 puzzle ) 27) Partial Order Planing 28) GBFS Solved Example
Views: 224192 Last moment tuitions

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Web Ninja Jon shows you how to extract table data from any website in under three minutes using CloudScrape. If you have any questions visit our website. http://WebDeveloperNinja.com
Views: 981 Web Developer Ninja

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An ROC curve is the most commonly used way to visualize the performance of a binary classifier, and AUC is (arguably) the best way to summarize its performance in a single number. As such, gaining a deep understanding of ROC curves and AUC is beneficial for data scientists, machine learning practitioners, and medical researchers (among others). SUBSCRIBE to learn data science with Python: https://www.youtube.com/dataschool?sub_confirmation=1 JOIN the "Data School Insiders" community and receive exclusive rewards: https://www.patreon.com/dataschool RESOURCES: - Transcript and screenshots: https://www.dataschool.io/roc-curves-and-auc-explained/ - Visualization: http://www.navan.name/roc/ - Research paper: http://people.inf.elte.hu/kiss/13dwhdm/roc.pdf LET'S CONNECT! - Newsletter: https://www.dataschool.io/subscribe/ - Twitter: https://twitter.com/justmarkham - Facebook: https://www.facebook.com/DataScienceSchool/ - LinkedIn: https://www.linkedin.com/in/justmarkham/
Views: 297889 Data School

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Introduction to how gains and lift can be applied to a direct marketing application. Score the sample dataset and plot the Gains curve. Define: base rate and lift. Similarities and differences between ROC and Gains curve. http://www.salford-systems.com
Views: 22254 Salford Systems

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Download workbook: http://people.highline.edu/mgirvin/ExcelIsFun.htm Learn the basics of Data Analysis at Highline Community College Professional Development Day 2012: Topics in Video: 1. What is Data Analysis? ( 00:53 min mark) 2. How Data Must Be Setup ( 02:53 min mark) Sort: 3. Sort with 1 criteria ( 04:35 min mark) 4. Sort with 2 criteria or more ( 06:27 min mark) 5. Sort by color ( 10:01 min mark) Filter: 6. Filter with 1 criteria ( 11:26 min mark) 7. Filter with 2 criteria or more ( 15:14 min mark) 8. Filter by color ( 16:28 min mark) 9. Filter Text, Numbers, Dates ( 16:50 min mark) 10. Filter by Partial Text ( 20:16 min mark) Pivot Tables: 11. What is a PivotTable? ( 21:05 min mark) 12. Easy 3 step method, Cross Tabulation ( 23:07 min mark) 13. Change the calculation ( 26:52 min mark) 14. More than one calculation ( 28:45 min mark) 15. Value Field Settings (32:36 min mark) 16. Grouping Numbers ( 33:24 min mark) 17. Filter in a Pivot Table ( 35:45 min mark) 18. Slicers ( 37:09 min mark) Charts: 19. Column Charts from Pivot Tables ( 38:37 min mark) Formulas: 20. SUMIFS ( 42:17 min mark) 21. Data Analysis Formula or PivotTables? ( 45:11 min mark) 22. COUNTIF ( 46:12 min mark) 23. Formula to Compare Two Lists: ISNA and MATCH functions ( 47:00 min mark) Getting Data Into Excel 24. Import from CSV file ( 51:21 min mark) 25. Import from Access ( 54:00 min mark) Highline Community College Professional Development Day 2012 Buy excelisfun products: https://teespring.com/stores/excelisfun-store
Views: 1551338 ExcelIsFun

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Whereas traditional data mining software can analyze only one flat table, Dataconda can analyze an entire relational database.
Views: 465 Dataconda

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This tutorial is an introduction to hash tables. A hash table is a data structure that is used to implement an associative array. This video explains some of the basic concepts regarding hash tables, and also discusses one method (chaining) that can be used to avoid collisions. Wan't to learn C++? I highly recommend this book http://amzn.to/1PftaSt Donate http://bit.ly/17vCDFx STILL NEED MORE HELP? Connect one-on-one with a Programming Tutor. Click the link below: https://trk.justanswer.com/aff_c?offer_id=2&aff_id=8012&url_id=238 :)
Views: 786351 Paul Programming

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Brief discussion on application of ROC curve and cut off value Discuss important aspects like sensitivity and specificity.

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Data Desk is sometimes thought of as that clever little program that does those interactive graphics. But there’s muscle under the hood. Here’s a little demo that exercises Data Desk’s computing power to analyze and display 10,000,000 cases. On an older Macintosh computer. In real time. Enjoy!

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In this video you will learn about the different performance matrix used for model evaludation such as Receiver Operating Charateristics, Confusion matrix, Accuracy. This is used very well in evauating classfication models like deicision tree, Logistic regression, SVM ANalytics Study Pack : https://analyticuniversity.com Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 16902 Big Edu

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Time series motifs are approximately repeated patterns found within the data. Such motifs have utility for many data mining algorithms, including rule-discovery, novelty-detection, summarization and clustering. In this video we demonstrate that when looking for time series motifs one needs to account for uniform scaling, the speed at which the patterns develop.
Views: 934 dyankov

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Views: 138324 gu_productions

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A quick video to understand standard Datawarehouse architecture. It consists of following layers 1. Data Source layer 2. ETL 3. Staging Area 4. Datawarehouse - Metadata, Summary and Raw Data 5. OLAP, Reporting and Data Mining Data warehouse is populated from multiple sources for an organisation. All these source system comes under Data Source layer. Some of the source systems are listed below: 1. Operations Systems -- such as Sales, HR, Inventory relational database. 2. ERP (SAP) and CRM (SalesForce.com) Systems. 3. Web server logs and Internal market research data. 4. Third-party data - such as census data, demographics data, or survey data. ETL Tools: Talend Open Studio, Jaspersoft ETL, Ab initio, Informatica, Datastage, Clover ETL, Pentaho ETL, Kettle For more details visit http://www.vikramtakkar.com/2015/09/data-warehouse-architecture-overview.html Datawarehouse Playlist: https://www.youtube.com/playlist?list=PLJ4bGndMaa8FV7nrvKXeHCLRMmIXVCyOG
Views: 89788 Vikram Takkar

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Includes an example with, - logistic regression model - confusion matrix - misclassification rate - rocr package - accuracy versus cutoff curve - identifying best cutoff values for best accuracy - roc curve - true positive rate (tpr) or sensitivity - false positive rate (fpr) or '1-specificity' - area under curve (auc) Machine Learning videos: https://goo.gl/WHHqWP roc curve is an important model evaluation tool related to analyzing big data or working in data science field. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 42579 Bharatendra Rai

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Views: 411731 CodingEntrepreneurs

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The experiment database for machine learning - http://expdb.cs.kuleuven.be - allows you to browse the results of millions of data mining experiments, with hundreds of data mining algorithms. This tutorial video explains how to use the graphical query interface and simple visualizations.
Views: 971 Joaquin Vanschoren

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Step-by-step instructions for calculating the correlation coefficient (r) for sample data, to determine in there is a relationship between two variables.
Views: 467592 Eugene O'Loughlin

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Tutorial on calculating the standard deviation and variance for statistics class. The tutorial provides a step by step guide. Like us on: http://www.facebook.com/PartyMoreStudyLess Related Videos: How to Calculate Mean and Standard Deviation Using Excel http://www.youtube.com/watch?v=efdRmGqCYBk Why are degrees of freedom (n-1) used in Variance and Standard Deviation http://www.youtube.com/watch?v=92s7IVS6A34 Playlist of z scores http://www.youtube.com/course?list=EC6157D8E20C151497 David Longstreet Professor of the Universe Like us on: http://www.facebook.com/PartyMoreStudyLess Professor of the Universe: David Longstreet http://www.linkedin.com/in/davidlongstreet/ MyBookSucks.Com
Views: 1662680 statisticsfun

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This StatQuest focuses on the machine learning topic "Decision Trees". Decision trees are a simple way to convert a table of data that you have sitting around your desk into a means to predict and classify new data as it comes. For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/

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Also called Classification and Regression Trees (CART) or just trees. R file: https://goo.gl/Kx4EsU Data file: https://goo.gl/gAQTx4 Includes, - Illustrates the process using cardiotocographic data - Decision tree and interpretation with party package - Decision tree and interpretation with rpart package - Plot with rpart.plot - Prediction for validation dataset based on model build using training dataset - Calculation of misclassification error Decision trees are an important tool for developing classification or predictive analytics models related to analyzing big data or data science. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 53771 Bharatendra Rai

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Spatial Analysis means to manipulate geographic data to extract new meaningful information. Interpolation is one of such geostatistical methods in which we use known values at sampled points to generate a continuous surface giving us prediction of values at unknown points. IDW is an interpolation technique in which values of cells are predicted by averaging known point values while processing each neighborhood cell. Points which are closer to the estimated cell have more weightage in the averages. IDW is preferred over Kriging in situations when sampled points are densely distributed over the surface. How to perform Spatial Interpolation in ArcGIS: 1. Open ArcGIS. 2. Add XY data in ArcMap. In this case, we have an Excel spreadsheet of Monthly Average Precipitation Data in .XLS format. 3. Convert XY data to Shapefile (.shp format). 4. Add boundary over data. 5. Select points which lie within the boundary. 6. Export selected points to new Shapefile. 7. Search for the IDW tool within the Interpolation toolset inside Spatial Analyst toolbox. 8. Choose the column of known point values as Z value field. 9. Mask the output of Raster Analysis to the given boundary in the Environments Settings. The Interpolated surface is obtained which can also be exported as a Raster Dataset for further analysis.
Views: 60669 Geospatial Geeks

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Views: 228948 Technofare

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Hey guys! In this episode we work on getting an Enchanting table, Explore a bit, and tackle that Guardian Temple! Minecraft Down Under PlayList http://goo.gl/W96HT2 Data's Website http://www.dataless822.net Data's Twitch www.twitch.tv/Dataless822 Data's Twitter https://twitter.com/Dataless822 Data's Facebook http://goo.gl/qhNvkZ World Download Link -Coming Soon- World Seed Unavailable at this time. Sorry guys no ones getting the seed until the first world download...you can thank all those people that spoiled my first lets play by telling me where everything was. Data's FAQs pc specs,recording software,age, ect, hhttp://goo.gl/cD9446 Plotz voxel sphere & ellipsoid guide http://www.plotz.co.uk/plotz.php Texture Pack / Resource pack: is my own and its called "Datacraft" http://goo.gl/e3beSZ Intro Music Carousel - Let's Go Home (Sound Remedy Remix) http://goo.gl/7bMdDD Outro Music "Hope" by Tobu is released on NoCopyrightSounds, the record label dedicated to releasing free music which can be freely used in media creations.http://goo.gl/ryJIX9 Special Thanks to our Animator Tewtiy Please check him out leave him a Thanks and Subscribe http://goo.gl/aZ3Koc
Views: 32246 Dataless822

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*** IMPROVED VERSION of this video here: https://youtu.be/tDLcBrLzBos I describe the standard normal distribution and its properties with respect to the percentage of observations within each standard deviation. I also make reference to two key statistical demarcation points (i.e., 1.96 and 2.58) and their relationship to the normal distribution. Finally, I mention two tests that can be used to test normal distributions for statistical significance. normal distribution, normal probability distribution, standard normal distribution, normal distribution curve, bell shaped curve
Views: 1079825 how2stats

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Cambridge Analytica improperly obtained data from as many as 50 million people. That's put Mark Zuckerberg on the defensive. The Verge's Silicon Valley editor Casey Newton reports. Subscribe: https://goo.gl/G5RXGs Check out our full video catalog: https://goo.gl/lfcGfq Visit our playlists: https://goo.gl/94XbKx Like The Verge on Facebook: https://goo.gl/2P1aGc Follow on Twitter: https://goo.gl/XTWX61 Follow on Instagram: https://goo.gl/7ZeLvX Read More: http://www.theverge.com
Views: 612828 The Verge

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In this tutorial you will learn how to create heatmaps. Sample data: https://drive.google.com/file/d/0BwuQp6JuQTsFb3dSX001eHRTUzQ/view?usp=sharing Data provided by: http://data.london.gov.uk/ Please subscribe.
Views: 3022 VisionZ

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Views: 1711 Dataless822

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It covers in details the meaning of Multiple Regression, various methods of framing Multiple Regression Equations and Standard Error of Estimate in Multiple Regression. Lecture by: Prof. Rajinder Kumar Arora, Head of Department (Commerce & Management)

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Using Visual Web Ripper to extract table data from web site
Views: 1696 sequentum

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Full lecture: http://bit.ly/D-Tree Which attribute do we select at each step of the ID3 algorithm? The attribute that results in the most pure subsets. We can measure purity of a subset as the entropy (degree of uncertainty) about the class within the subset.
Views: 178605 Victor Lavrenko

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This video reviews the scales of measurement covered in introductory statistics: nominal, ordinal, interval, and ratio (Part 1 of 2). Scales of Measurement Nominal, Ordinal, Interval, Ratio YouTube Channel: https://www.youtube.com/user/statisticsinstructor Subscribe today! Lifetime access to SPSS videos: http://tinyurl.com/m2532td Video Transcript: In this video we'll take a look at what are known as the scales of measurement. OK first of all measurement can be defined as the process of applying numbers to objects according to a set of rules. So when we measure something we apply numbers or we give numbers to something and this something is just generically an object or objects so we're assigning numbers to some thing or things and when we do that we follow some sort of rules. Now in terms of introductory statistics textbooks there are four scales of measurement nominal, ordinal, interval, and ratio. We'll take a look at each of these in turn and take a look at some examples as well, as the examples really help to differentiate between these four scales. First we'll take a look at nominal. Now in a nominal scale of measurement we assign numbers to objects where the different numbers indicate different objects. The numbers have no real meaning other than differentiating between objects. So as an example a very common variable in statistical analyses is gender where in this example all males get a 1 and all females get a 2. Now the reason why this is nominal is because we could have just as easily assigned females a 1 and males a 2 or we could have assigned females 500 and males 650. It doesn't matter what number we come up with as long as all males get the same number, 1 in this example, and all females get the same number, 2. It doesn't mean that because females have a higher number that they're better than males or males are worse than females or vice versa or anything like that. All it does is it differentiates between our two groups. And that's a classic nominal example. Another one is baseball uniform numbers. Now the number that a player has on their uniform in baseball it provides no insight into the player's position or anything like that it just simply differentiates between players. So if someone has the number 23 on their back and someone has the number 25 it doesn't mean that the person who has 25 is better, has a higher average, hits more home runs, or anything like that it just means they're not the same playeras number 23. So in this example its nominal once again because the number just simply differentiates between objects. Now just as a side note in all sports it's not the same like in football for example different sequences of numbers typically go towards different positions. Like linebackers will have numbers that are different than quarterbacks and so forth but that's not the case in baseball. So in baseball whatever the number is it provides typically no insight into what position he plays. OK next we have ordinal and for ordinal we assign numbers to objects just like nominal but here the numbers also have meaningful order. So for example the place someone finishes in a race first, second, third, and so on. If we know the place that they finished we know how they did relative to others. So for example the first place person did better than second, second did better than third, and so on of course right that's obvious but that number that they're assigned one, two, or three indicates how they finished in a race so it indicates order and same thing with the place finished in an election first, second, third, fourth we know exactly how they did in relation to the others the person who finished in third place did better than someone who finished in fifth let's say if there are that many people, first did better than third and so on. So the number for ordinal once again indicates placement or order so we can rank people with ordinal data. OK next we have interval. In interval numbers have order just like ordinal so you can see here how these scales of measurement build on one another but in addition to ordinal, interval also has equal intervals between adjacent categories and I'll show you what I mean here with an example. So if we take temperature in degrees Fahrenheit the difference between 78 degrees and 79 degrees or that one degree difference is the same as the difference between 45 degrees and 46 degrees. One degree difference once again. So anywhere along that scale up and down the Fahrenheit scale that one degree difference means the same thing all up and down that scale. OK so if we take eight degrees versus nine degrees the difference there is one degree once again. That's a classic interval scale right there with those differences are meaningful and we'll contrast this with ordinal in just a few moments but finally before we do let's take a look at ratio.
Views: 355727 Quantitative Specialists

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Our Excel training videos on YouTube cover formulas, functions and VBA. Useful for beginners as well as advanced learners. New upload every Thursday. For details you can visit our website: http://www.familycomputerclub.com You can scrape, pull or get data from websites into Excel by performing a few simple steps. 1. record a macro to find out how one or many tables or data can be scraped from the website 2. Study the code carefully 3. Create an Excel sheet containing the links that get you the data from the appropriate web pages 4. Automate the process using a loop that creates a) New worksheets b) Automatically changes the link to the web pages that have the required data You can view the complete Excel VBA code here: http://www.familycomputerclub.com/scrpae-pull-data-from-websites-into-excel.html http://www.familycomputerclub.com/get-web-page-data-int-excel-using-vba.html Interesting Links: http://www.tushar-mehta.com/publish_train/xl_vba_cases/vba_web_pages_services/index.htm Get the book Excel 2016 Power Programming with VBA: http://amzn.to/2kDP35V If you are from India you can get this book here: http://amzn.to/2jzJGqU
Views: 523128 Dinesh Kumar Takyar

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A description of the concepts behind Analysis of Variance. There is an interactive visualization here: http://demonstrations.wolfram.com/VisualANOVA/ but I have not tried it, and this: http://rpsychologist.com/d3-one-way-anova has another visualization
Views: 523005 J David Eisenberg

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Which2 is an instance based learner written in lisp, designed to discover rules that predict for given target classes. Which2Gui is a graphic interface written in python/gtk that sits on top of Which2 and provides a pretty box to look at while datamining. We recommend viewing this video at the highest resolution available as the program text is almost completely illegible at lower resolutions.
Views: 421 laneseniordesign

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It Knows You Better Than You Know Yourself. Where do we go from here? Data has become the tail that wags the dog; and we are pets, not free individuals in this configuration. And things are growing Darker. Please help support us on Patreon, read our goals here: https://www.patreon.com/truthstreammedia Truthstream Can Be Found Here: Our Film: TheMindsofMen.net Site: http://TruthstreamMedia.com Twitter: @TruthstreamNews DONATE: http://bit.ly/2aTBeeF Newsletter: http://eepurl.com/bbxcWX ~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*­~*~*~*~*~ Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "fair use" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.
Views: 468131 Truthstream Media

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Views: 681 Alice

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This video will show you how to find the regression line by hand with an example. The regression line is y=a +bx (a is the constant and b is the slope) Thanks for learning ! Visit our website www.i-hate-math.com
Views: 302537 I Hate Math Group, Inc

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Google Tech Talk (more info below) March 30, 2011 Presented by Raffael Marty. ABSTRACT In this two part presentation we will explore log analysis and log visualization. We will have a look at the history of log analysis; where log analysis stands today, what tools are available to process logs, what is working today, and more importantly, what is not working in log analysis. What will the future bring? Do our current approaches hold up under future requirements? We will discuss a number of issues and will try to figure out how we can address them. By looking at various log analysis challenges, we will explore how visualization can help address a number of them; keeping in mind that log visualization is not just a science, but also an art. We will apply a security lens to look at a number of use-cases in the area of security visualization. From there we will discuss what else is needed in the area of visualization, where the challenges lie, and where we should continue putting our research and development efforts. Speaker Info: Raffael Marty is COO and co-founder of Loggly Inc., a San Francisco based SaaS company, providing a logging as a service platform. Raffy is an expert and author in the areas of data analysis and visualization. His interests span anything related to information security, big data analysis, and information visualization. Previously, he has held various positions in the SIEM and log management space at companies such as Splunk, ArcSight, IBM research, and PriceWaterhouse Coopers. Nowadays, he is frequently consulted as an industry expert in all aspects of log analysis and data visualization. As the co-founder of Loggly, Raffy spends a lot of time re-inventing the logging space and - when not surfing the California waves - he can be found teaching classes and giving lectures at conferences around the world. http://about.me/raffy

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One of the most interesting things I have seen at #CES2019. This is a PC/Data Rack that is completely under liquid with a non conductive 3M liquid cooling! Can you imagine this pc cooling system coming to homes? Check out Unboxed here: http://unboxed.tv/signup How exactly does this pc cooling fluid work? Is this the future of pc technology and cooling? While this is more geared towards servers and data centers lets hope this comes to the best pc cooling setups and be available to the public as the future of liquid cooling. Is this future pc technology or do you think we won't see this? Is 3m novec and mineral oil the real water cooling? #pccooling #pc
Views: 15637 Danny Winget

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Table of Contents Act 1 - The Possibilities are Endless 01:53 Act 2 - NLP to the Rescue (aka The Hype) 05:14 Act 3 - A Peek Under the Hood (aka The Reality) 16:40 Act 4 - You Can Do It! 25:22 Q&A - 40:10 Gathering insight from clinical notes remains one of the areas of untapped healthcare intelligence with tremendous potential. But extracting that value is difficult. Still, a few organizations across the country are demonstrating success using advanced technology tied to intuitive processes and procedures. Leading one such organizational effort is Wendy Chapman, PhD, chair of the Department of Biomedical Informatics at the University of Utah. Dr. Chapman’s research has driven discovery in new ways to disseminate resources for modeling and understanding information described in narrative clinical reports. Her teams have demonstrated phenotyping for precision medicine, quality improvement, and decision support. Joining Dr. Chapman in a shared discussion is Mike Dow who leads the Natural Language Processing (NLP) technology team at Health Catalyst. Mike and team have several years of experience engaging with a variety of health system organizations across the country who are realizing statistical insight by incorporating text notes along with discrete data analysis. Together, Mike and Dr. Chapman will provide an NLP primer sharing principle-driven stories so you can get going with NLP whether you are just beginning or considering processes, tools or how to build support with key leadership. Learning Objectives: - Understand NLP, both its challenges, and potential to drive clinical insight using social determinants of health - Gain insight into the technology that makes NLP possible - Consider the future potential of NLP View this webin to better understand the potential of NLP through existing applications, the challenges of making NLP a real and scalable solution, and walk away with concrete actions you can take to use NLP for the good of your organization.
Views: 229 Health Catalyst

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Confusion Matrix for Multiple Classes www.imperial.ac.uk/people/n.sadawi

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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag introduces supervised learning with nearest neighbor classification using feature scaling and decision trees. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 39024 MIT OpenCourseWare

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