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Feature Engineering in SAS Visual Data Mining & Machine Learning
 
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http://support.sas.com/software/products/visual-data-mining-machine-learning/index.html Presenter: Radhikha Myneni Radhikha Myneni discusses some feature engineering techniques available in SAS Visual Data Mining and Machine Learning 8.3. SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 1104 SAS Software
Data Visualization Types and Terms
 
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Please watch: "Master Excel Series Degree Function- ماسٹر ایکسل سیریز ڈگری فارمولہ" https://www.youtube.com/watch?v=P3BJYzWusL0 --~-- Data visualization terminology including types i.e scientific visualization, information visualization, data visualization, informative art, informatics, information dashboard etc it is part of lecture 1
Views: 249 TheQLGConsultants
Data Structures: Crash Course Computer Science #14
 
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Today we’re going to talk about on how we organize the data we use on our devices. You might remember last episode we walked through some sorting algorithms, but skipped over how the information actually got there in the first place! And it is this ability to store and access information in a structured and meaningful way that is crucial to programming. From strings, pointers, and nodes, to heaps, trees, and stacks get ready for an ARRAY of new terminology and concepts. Ps. Have you had the chance to play the Grace Hopper game we made in episode 12. Check it out here! http://thoughtcafe.ca/hopper/ Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios Want to know more about Carrie Anne? https://about.me/carrieannephilbin The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list=PL1mtdjDVOoOqJzeaJAV15Tq0tZ1vKj7ZV Want to find Crash Course elsewhere on the internet? Facebook - https://www.facebook.com/YouTubeCrash... Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 413670 CrashCourse
Basic Machine Learning Algorithms Overview - Data Science Crash Course Mini-series
 
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A high-level overview of common, basic Machine Learning algorithms by Robert Hryniewicz (@RobHryniewicz). Thanks for watching and make sure to subscribe! More videos coming soon!
Views: 39977 Hortonworks
Decision Tree Algorithm | Decision Tree in Python | Machine Learning Algorithms | Edureka
 
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** Machine Learning with Python : https://www.edureka.co/machine-learning-certification-training ** This Edureka video on Decision Tree Algorithm in Python will take you through the fundamentals of decision tree machine learning algorithm concepts and its demo in Python. Below are the topics covered in this tutorial: 1. What is Classification? 2. Types of Classification 3. Classification Use Case 4. What is Decision Tree? 5. Decision Tree Terminology 6. Visualizing a Decision Tree 7 Writing a Decision Tree Classifier fro Scratch in Python using CART Algorithm Subscribe to our channel to get video updates. Hit the subscribe button above. Check out our Python Machine Learning Playlist: https://goo.gl/UxjTxm #decisiontree #decisiontreepython #machinelearningalgorithms - - - - - - - - - - - - - - - - - About the Course Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience. After completing this Machine Learning Certification Training using Python, you should be able to: Gain insight into the 'Roles' played by a Machine Learning Engineer Automate data analysis using python Describe Machine Learning Work with real-time data Learn tools and techniques for predictive modeling Discuss Machine Learning algorithms and their implementation Validate Machine Learning algorithms Explain Time Series and it’s related concepts Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Machine Learning with Python? Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 76132 edureka!
How does a blockchain work - Simply Explained
 
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What is a blockchain and how do they work? I'll explain why blockchains are so special in simple and plain English! 💰 Want to buy Bitcoin or Ethereum? Buy for $100 and get $10 free (through my affiliate link): https://www.coinbase.com/join/59284524822a3d0b19e11134 📚 Sources can be found on my website: https://www.savjee.be/videos/simply-explained/how-does-a-blockchain-work/ 🐦 Follow me on Twitter: https://twitter.com/savjee ✏️ Check out my blog: https://www.savjee.be ✉️ Subscribe to newsletter: https://goo.gl/nueDfz 👍🏻 Like my Facebook page: https://www.facebook.com/savjee
Views: 2928707 Simply Explained - Savjee
Scales of Measurement - Nominal, Ordinal, Interval, Ratio (Part 1) - Introductory Statistics
 
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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: 385943 Quantitative Specialists
Stick Figures
 
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Retrieval and Visualization of Human Motion Data via Stick Figures. Myung Geol Choi, Kyungyong Yang, Jehee Lee, Jun Mitani, Takeo Igarashi, Motion Comics: Visualization, Browsing and Searching of Human Motion, Data. Computer Graphics Forum (Pacific Graphics 2012), Volume 31, Number 7, 2057-2065, September 2012. Best Paper Aword. http://mrl.snu.ac.kr/~mingle/projects/StickFigures/
Views: 1808 myunggeol
Making sense of the confusion matrix
 
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How do you interpret a confusion matrix? How can it help you to evaluate your machine learning model? What rates can you calculate from a confusion matrix, and what do they actually mean? In this video, I'll start by explaining how to interpret a confusion matrix for a binary classifier: 0:49 What is a confusion matrix? 2:14 An example confusion matrix 5:13 Basic terminology Then, I'll walk through the calculations for some common rates: 11:20 Accuracy 11:56 Misclassification Rate / Error Rate 13:20 True Positive Rate / Sensitivity / Recall 14:19 False Positive Rate 14:54 True Negative Rate / Specificity 15:58 Precision Finally, I'll conclude with more advanced topics: 19:10 How to calculate precision and recall for multi-class problems 24:17 How to analyze a 10-class confusion matrix 28:26 How to choose the right evaluation metric for your problem 31:31 Why accuracy is often a misleading metric == RELATED RESOURCES == My confusion matrix blog post: https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/ Evaluating a classifier with scikit-learn (video): https://www.youtube.com/watch?v=85dtiMz9tSo&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=9 ROC curves and AUC explained (video): https://www.youtube.com/watch?v=OAl6eAyP-yo == DATA SCHOOL INSIDERS == Join "Data School Insiders" on Patreon for bonus content: https://www.patreon.com/dataschool == WANT TO GET BETTER AT MACHINE LEARNING? == 1) WATCH my scikit-learn video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A 2) SUBSCRIBE for more videos: https://www.youtube.com/dataschool?sub_confirmation=1 3) ENROLL in my Machine Learning course: https://www.dataschool.io/learn/ 4) 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: 18751 Data School
MDX Query Basics (Analysis Services 2012)
 
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This video is part of LearnItFirst's SQL Server 2012: A Comprehensive Introduction course. More information on this video and course is available here: http://www.learnitfirst.com/Course170 In this video, we walk through the basics of the MDX Query language. It is a very logical language, however, is somewhat large in syntax. If you enjoy writing Transact-SQL, you will really enjoy the MDX language. The AdventureWorks2012 multidimensional models need to be installed on your SSAS Multidimensional mode instance from the CodePlex web site. Highlights from this video: - The basics of an MDX query - What is the basic format of the MDX query language? - Is it necessary to have a WHERE clause in an MDX query? - How to signal the end of a statement in the MDX query language - Using the Internet Order Count and much more...
Views: 110264 LearnItFirst.com
Large-Scale Graph Data Mining with MapReduce a Bag of Tricks, by Nima Sarshar, Ph.D. Intuit 20130624
 
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Speaker: Nima Sarshar, Ph.D. Intuit Event Details Many modern large-scale data mining problems are defined on graphs (think of People you May Know) , or have a graph representation (think of Collaborative filtering and it's bi-partite graph representation). This makes Hadoop and the MapReduce framework natural candidates to tackle them. Some graph processing algorithms, e.g. global PageRank, can be ported into the MapReduce framework rather straightforwardly. Others require various degrees of combinatorial tricks. In this talk, we review several fundamental graph processing algorithms that require careful, and often beautiful, tricks to scale when dealing with very large graphs. These include enumerating triangles and rectangles (e.g., to find Friends in Common at scale) , creating induced latent networks and collaborative filtering on bi-partite graphs, Personalized PageRank and more. We will describe some of the applications of these algorithms at Intuit. Speaker Bio Nima is a Senior Data Scientist at Intuit. Before Intuit he was the co-founder and CTO of Haileo Inc, a Santa Clara based star up specializing in context-based video advertisement. Before that, he was an Associate Prof. of Software Engineering at the University of Regina, Canada.
Views: 2585 San Francisco Bay ACM
Molecular Dynamics, The Shovel for Data Mining Neutron Scattering Data
 
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Some of the uses of neutron scattering experiments of disordered, biologically relevant systems as a test for molecular dynamics simulations. Also covered are how molecular dynamics simulations can be used as interpretive tools for neutron scattering data.
Views: 2714 thunderf00tCC
SQL Database Fundamentals Tutorial
 
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Social Network for Developers ☞ https://morioh.com Aiodex’s Referral Program  will give you 20% -80% commission from their transaction fee for 7 years. The value will be calculated starting from the date the member you invite sign up ☞ http://vrl.to/c4099b4d9f Next Generation Shorten Platform: Optimal choice to make a profit and analyze traffic sources on the network. Shorten URLs and Earn Big Money ☞ https://viralroll.com/ Get Free 15 Geek ☞ https://geekcash.org/ Developers Chat Channel ☞ https://discord.gg/KAe3AnN Playlists Video Tutorial ☞ http://dev.edupioneer.net/f086e182ab Learn to code for free and get a developer job ☞ https://codequs.com/ The Ultimate MySQL Bootcamp: Go from SQL Beginner to Expert ☞ http://wp.me/p8HH5D-17D Retrieving Data from Oracle Database with SQL ☞ http://edusavecoupon.net/?p=14285 SQL, SSAS & Data Mining Query Languages - T-SQL MDX DAX DMX ☞ https://wp.me/p8iOGF-N6 Would you like to learn the basics of relational databases? Join us for this look at SQL Database fundamentals, along with those of database management systems and database components. Get an in-depth introduction to the terminology, concepts, and skills you need to understand database objects, administration, security, and management tools. Plus, explore T-SQL scripts, database queries, and data types. Start with a look at creating tables, inserting data, and querying data in tables. Then, learn about data manipulation, optimize database performance, and work with non-relational data. Get practical help on basic database administration, including installation and configuration, backup and restore, security, monitoring, and maintenance. Take this SQL Database tutorial to prepare for additional online courses for database administrators (DBAs), developers, data scientists, and big data specialists. Check it out! 1 | Introduction to Databases View a course introduction, and get started with databases. 2 | Getting Started with Tables Get an introduction to concepts and techniques for creating tables, inserting data, and querying data in tables. 3 | Working with Data in Tables Learn about data manipulation using Transact-SQL (T-SQL), including INSERT, UPDATE, and DELETE. Explore wrapper objects, such as views and stored procedures. 4 | Optimizing Database Performance Get an introduction to terminology and concepts for optimizing database performance by using indexes. 5 | Working with Non-Relational Data Explore additional types of data that can be used in modern databases, including XML and JSON. 6 | Basic Database Administration Learn about terminology and concepts for basic database administration, including installation and configuration, backup and restore, security, monitoring, and maintenance. Video source via: MVA ---------------------------------------------------- Website: https://goo.gl/RBymXD Playlist: https://goo.gl/hnwbLS Fanpage: https://goo.gl/4C2pj9 Wordpress: https://goo.gl/znpKQ2 Twitter: https://goo.gl/6XgzWJ
Views: 22903 coderschool
Nominal, ordinal, interval and ratio data: How to Remember the differences
 
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Learn the difference between Nominal, ordinal, interval and ratio data. http://youstudynursing.com/ Research eBook on Amazon: http://amzn.to/1hB2eBd Check out the links below and SUBSCRIBE for more youtube.com/user/NurseKillam For help with Research - Get my eBook "Research terminology simplified: Paradigms, axiology, ontology, epistemology and methodology" here: http://www.amazon.com/dp/B00GLH8R9C Related Videos: http://www.youtube.com/playlist?list=PLs4oKIDq23AdTCF0xKCiARJaBaSrwP5P2 Connect with me on Facebook Page: https://www.facebook.com/NursesDeservePraise Twitter: @NurseKillam https://twitter.com/NurseKillam Facebook: https://www.facebook.com/laura.killam LinkedIn: http://ca.linkedin.com/in/laurakillam Quantitative researchers measure variables to answer their research question. The level of measurement that is used to measure a variable has a significant impact on the type of tests researchers can do with their data and therefore the conclusions they can come to. The higher the level of measurement the more statistical tests that can be run with the data. That is why it is best to use the highest level of measurement possible when collecting information. In this video nominal, ordinal, interval and ratio levels of data will be described in order from the lowest level to the highest level of measurement. By the end of this video you should be able to identify the level of measurement being used in a study. You will also be familiar with types of tests that can be done with each level. To remember these levels of measurement in order use the acronym NOIR or noir. The nominal level of measurement is the lowest level. Variables in a study are placed into mutually exclusive categories. Each category has a criteria that a variable either has or does not have. There is no natural order to these categories. The categories may be assigned numbers but the numbers have no meaning because they are simply labels. For example, if we categorize people by hair color people with brown hair do not have more or less of this characteristic than those with blonde hair. Nominal sounds like name so it is easy to remember that at a nominal level you are simply naming categories. Sometimes researchers refer to nominal data as categorical or qualitative because it is not numerical. Ordinal data is also considered categorical. The difference between nominal and ordinal data is that the categories have a natural order to them. You can remember that because ordinal sounds like order. While there is an order, it is also unknown how much distance is between each category. Values in an ordinal scale simply express an order. All nominal level tests can be run on ordinal data. Since there is an order to the categories the numbers assigned to each category can be compared in limited ways beyond nominal level tests. It is possible to say that members of one category have more of something than the members of a lower ranked category. However, you do not know how much more of that thing they have because the difference cannot be measured. To determine central tendency the categories can be placed in order and a median can now be calculated in addition to the mode. Since the distance between each category cannot be measured the types of statistical tests that can be used on this data are still quite limited. For example, the mean or average of ordinal data cannot be calculated because the difference between values on the scale is not known. Interval level data is ordered like ordinal data but the intervals between each value are known and equal. The zero point is arbitrary. Zero simply represents an additional point of measurement. For example, tests in school are interval level measurements of student knowledge. If you scored a zero on a math test it does not mean you have no knowledge. Yet, the difference between a 79 and 80 on the test is measurable and equal to the difference between an 80 and an 81. If you know that the word interval means space in between it makes remembering what makes this level of measurement different easy. Ratio measurement is the highest level possible for data. Like interval data, Ratio data is ordered, with known and measurable intervals between each value. What differentiates it from interval level data is that the zero is absolute. The zero occurs naturally and signifies the absence of the characteristic being measured. Remember that Ratio ends in an o therefore there is a zero. Typically this level of measurement is only possible with physical measurements like height, weight and length. Any statistical tests can be used with ratio level data as long as it fits with the study question and design.
Views: 339773 NurseKillam
Oil Drilling | Oil & Gas Animations
 
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- Like our Facebook: https://www.facebook.com/oilvips - Geologists and geophysicists have agreed on the existence of a "prospect", a potential field. In order to find out if hydrocarbons are indeed trapped in the reservoir rock, we must drill to hit them. Bearing in mind the knowledge acquired about the substratum and the topography of the land, the best position for the installation of the drilling equipment is determined. Generally it is vertically above the point of maximum thickness of the geological layer suspected of containing hydrocarbons. The drillers then make a hole in conditions that are sometimes difficult. Of small diameter (from 20 to 50 cm) this hole will generally go down to a depth of between 2000 and 4000 meters. Exceptionally, certain wells exceed 6000 m. One of them has even exceeded 11 000 m! Certain fields can be buried at a depth equivalent to the height of 12 Eiffel Towers ... The derrick is the visible part of the drilling rig. It is a metal tower several tens of meters high. It is used to vertically introduce the drill strings down the hole. These drill strings are made up of metallic tubes screwed end to end. They transmit a rotating movement (rotary drilling) to the drilling tool (the drill bit) and help circulate a liquid called "mud" (because of its appearance) down to the bottom of the well. The drilling rig works like an enormous electric hand-drill of which the derrick would be the body, the drill strings the drive and the drilling tool the drill bit. The most usual tool is an assembly of three cones -- from which comes the name "tri cone" -- in very hard steel, which crushes the rock. Sometimes when the rock being drilled is very resistant, a single- block tool encrusted with diamonds is used. This wears down the rock by abrasion. Through the drill pipes, at the extremity of which the drill bit rotates, a special mud is injected, which the mud engineer prepares and controls. This mud cools the drill bit and consolidates the sides of the borehole. Moreover it avoids a gushing of oil, gas or water from the layer being drilled, by equilibrating the pressure. Finally, the mud cleans the bottom of the well. As it makes its way along the pipes, it carries the rock fragments (cuttings) to the surface. The geologist examines these cuttings to discover the characteristics of the rocks being drilled and to detect eventual shows of hydrocarbons. The cuttings, fragments of rock crushed by the drill bit, are brought back up to the surface by the mud. To obtain information on the characteristics of the rock being drilled, a core sample is taken. The drill bit is replaced by a hollow tool called a core sampler, which extracts a cylindrical sample of several meters of rock. This core supplies data on the nature of the rock, the inclination of the layers, the structure, permeability, porosity, fluid content and the fossils present. After having drilled a few hundred of meters, the explorers and drillers undertake measurements down the hole called loggings, by lowering electronic tools into the well to measure the physical parameters of the rock being drilled. These measures validate, or invalidate, or make more precise the hypotheses put forward earlier about the rocks and the fluids that they contain. The log engineer is responsible for the analysis of the results of the various loggings. The sides of the well are then reinforced by steel tubes screwed end to end. These tubes (called casings) are cemented into the ground. They isolate the various layers encountered. When hydrocarbons are found, and if the pressure is sufficient to allow them come to the surface naturally, the drillers do a flow check. The oil is allowed to come to the surface during several hours or several days through a calibrated hole. The quantity recovered is measured, as are the changes in pressure at the bottom of the well. In this way, a little more knowledge is gained about the probable productivity of the field. If the field seems promising, the exploration team ends the first discovery well and goes on to drill a second, even several others, several hundred or thousand meters further away. In this way, the exploration team is able to refine its knowledge about the characteristics of the field. The decision to stop drilling is made only when all these appraisal wells have provided sufficient information either to give up the exploration or to envisage future production. --------------------------------------------------------------------------------------- Like our Facebook: https://www.facebook.com/oilvips Twitter: https://twitter.com/oilvips And Don't forget to subscribe to our channel
Views: 794255 Oil & Gas Videos
Linear Regression - Machine Learning Fun and Easy
 
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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 ►MACHIN LEARNING COURSE - http://augmentedstartups.info/machine-learning-courses ---------------------------------------------------------------------------- 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: 151088 Augmented Startups
How to perform predictive analysis on your web analytics tool data - 2013 06 19
 
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When: June 19th, 2013 Education Level: Advanced What: It is widely known that traditional web analytics have been a great way to optimize/analyse your website visitor metrics. Many analytics tools, like Google Analytics, are available there and enable you to track basic metrics from your website with ease. Although, most of the time these tools provide data at aggregate level which limits the understanding of interplay amongst different variables. With predictive analytics, you can explore the hidden relationship between many independent variables (e.g. pageviews per visit, quality score of keyword, time of visit), in order to see how these variables affect your KPIs like revenue & transactions. During the webinar we help you to understand the real value of predictive analytics when applied on web analytics data to help improve your understanding relationship between different variables. From this webinar, you will get to know: Part 1 (02m00s) - Analytics disciplines Part 2 (06m02s) - What is R and why should you use this tool? Part 3 (13m01s) - How to extract your Web Analytics data into R? Part 4 (18m35s) - How to build a predictive model using web analytics data with the help of R? How predictive modelling can take your analysis to the next level? Part 3 (46m34s) - How to carry out insightful analysis through visualization? Part 4 (54m28s) - Q&A Round Who should watch: Every web analyst who wants to take his analysis to the next level. Website: www.tatvic.com/webinars
Views: 9471 Tatvic Analytics
100 Most Commonly Used computer Full Forms
 
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100 Computer Related Short Forms & Full Forms Abbreviations 1. FULL FORM OF COMPUTER - COMMONLY OPERATED MACHINE PARTICULARLY USED IN TECHNICAL AND EDUCATIONAL RESEARCH 2. FULL FORM OF 3D - THREE DIMENSIONAL 3. FULL FORM OF 3G - 3RD GENERATION 4. FULL FORM OF AAC - ADVANCED AUDIO CODING 5. FULL FORM OF AC97 - AUDIO CODEC 97 6. FULL FORM OF AMR - ADAPTIVE MULTI-RATE 7. FULL FORM OF ASI - ASYNCHRONOUS SERIAL INTERFACE 8. FULL FORM OF ASP - ACTIVE SERVER PAGES 9. FULL FORM OF ATM - AUTOMATED TELLER MACHINE 10. FULL FORM OF BASIC - BEGINNER'S ALL-PURPOSE SYMBOLIC INSTRUCTION CODE 11. FULL FORM OF BCC - BLIND CARBON COPY 12. FULL FORM OF BIOS - BASIC INPUT OUTPUT SYSTEM 13. FULL FORM OF BPO - BUSINESS PROCESS OUTSOURCING 14. FULL FORM OF FAT - FILE ALLOCATION TABLE 15. FULL FORM OF NTFS - NEW TECHNOLOGY FILE SYSTEM 16. FULL FORM OF SMPS - SWITCH MODE POWER SUPPLY 17. FULL FORM OF PDF - PORTABLE DOCUMENT FORMAT 18. FULL FORM OF COBOL - COMMON BUSINESS-ORIENTED LANGUAGE 19. FULL FORM OF CODEC - CODER-DECODER 20. FULL FORM OF CPU - CENTRAL PROCESSING UNIT 21. FULL FORM OF CSS - CASCADING STYLE SHEETS 22. FULL FORM OF DIVX - NAMED AS A PARADOY TO DIVX SYSTEM 23. FULL FORM OF DNS - DOMAIN NAME SYSTEM 24. FULL FORM OF DVI - DIGITAL VIDEO INTERACTIVE 25. FULL FORM OF ET - EXABYTE 26. FULL FORM OF FLAC - FREE LOSSLESS AUDIO CODEC 27. FULL FORM OF FTP - FILE TRANSFER PROTOCOL 28. FULL FORM OF GB - GIGABYTE 29. FULL FORM OF GIF - GRAPHICS INTERCHANGE FORMAT 30. FULL FORM OF GOOGLE - GLOBAL ORGANIZATION OF ORIENTED GROUP LANGUAGE OF EARTH 31. FULL FORM OF GPRS - GENERAL PACKET RADIO SERVICE 32. FULL FORM OF GPS - GLOBAL POSITIONING SYSTEM 33. FULL FORM OF GSM - GLOBAL SYSTEM FOR MOBILE COMMUNICATIONS 34. FULL FORM OF HD - HIGH DEFINITION 35. FULL FORM OF HTML - HYPERTEXT MARKUP LANGUAGE 36. FULL FORM OF HTTPS - HYPERTEXT TRANSFER PROTOCOL 37. FULL FORM OF IMEI - INTERNATIONAL MOBILE EQUIPMENT IDENTITY 38. FULL FORM OF IP - INTERNET PROTOCOL 39. FULL FORM OF ISP - INTERNET SERVICE PROVIDER 40. FULL FORM OF IT - INFORMATION TECHNOLOGY 41. FULL FORM OF JAD - JAVA APPLICATION DESCRIPTOR 42. FULL FORM OF JPEG- JOINT PHOTOGRAPHIC EXPERTS GROUP 43. FULL FORM OF KB - KILOBYTE 44. FULL FORM OF LCD - LIQUID CRYSTAL DISPLAY 45. FULL FORM OF LED - LIQUID ELECTRONIC DISPLAY 46. FULL FORM OF MB - MEGABYTE 47. FULL FORM OF MBPS - MEGA BITS PER SECOND 48. FULL FORM OF MICR - MAGNETIC INK CHARACTER RECOGNITION 49. FULL FORM OF MIS - MANAGEMENT INFORMATION SYSTEM 50. FULL FORM OF MMS - MULTIMEDIA MESSAGING SERVICE 51. FULL FORM OF MP3 - MPEG LAYER-3 52. FULL FORM OF MP4 - MPEG LAYER-4 53. FULL FORM OF MPEG - MOVING PICTURE EXPERTS GROUP 54. FULL FORM OF OSS - OPEN SOUND SYSTEM 55. FULL FORM OF PC - PERSONAL COMPUTER 56. FULL FORM OF PDF - PORTABLE DOCUMENT FORMAT 57. FULL FORM OF PERL - PRACTICAL EXTRACTION AND REPORT LANGUAGE 58. FULL FORM OF PING - PACKET INTERNET GROPER 59. FULL FORM OF PROLOG - PROGRAMMING IN LOGIC 60. FULL FORM OF PT - PETABYTE 61. FULL FORM OF QIF - QUICKEN INTERCHANGE FORMAT 62. FULL FORM OF QRCODE - QUICK RESPONSE CODE 63. FULL FORM OF RAM - RANDOM ACCESS MEMORY 64. FULL FORM OF RIP - REST IN PEACE 65. FULL FORM OF RSS- REALLY SIMPLE SYNDICATION 66. FULL FORM OF SATA - SERIAL ADVANCED TECHNOLOGY ATTACHMENT 67. FULL FORM OF SEO - SEARCH ENGINE OPTIMIZATION 68. FULL FORM OF SIM - SUBSCRIBER IDENTITY MODULE 69. FULL FORM OF SMIL - SYNCHRONIZED MULTIMEDIA INTEGRATION LANGUAGE 70. FULL FORM OF SMS - SHORT MESSAGE SERVICE 71. FULL FORM OF SOS - SEND OUT SUCCOUR 72. FULL FORM OF SQL - STRUCTURED QUERY LANGUAGE 73. FULL FORM OF SSL - SECURE SOCKETS LAYER 74. FULL FORM OF TB - TERABYTE 75. FULL FORM OF TFT - THIN FILM TRANSISTER 76. FULL FORM OF TIFF - TAGGED IMAGE FILE FORMAT 78. FULL FORM OF UPS - UNINTERRUPTED POWER SUPPLY 79. FULL FORM OF URL - UNIFORM RESOURCE LOCATOR 80. FULL FORM OF USB - UNIVERSAL SERIAL BUS 81. FULL FORM OF VB - VISUAL BASIC 82. FULL FORM OF VBR - VARIABLE BIT RATE 83. FULL FORM OF VBS - VISUAL BASIC SCRIPT 84. FULL FORM OF VCD - VIDEO COMPAQ DISK 85. FULL FORM OF VIRUS - VITAL INFORMATION RESOURCES UNDER SEIZE 86. FULL FORM OF VLC - VIDEO LAN CLIENT 87. FULL FORM OF WAP - WIRELESS APPLICATION PROTOCOL 88. FULL FORM OF WIFI - WIRELESS FIDELITY 89. FULL FORM OF WMA - WINDOWS MEDIA AUDIO 90. FULL FORM OF WMV - WINDOWS MEDIA VIDEO 91. FULL FORM OF WWW - WORLD WIDE WEB 92. FULL FORM OF XBL - XML BINDING LANGUAGE 93. FULL FORM OF XML - EXTENSIBLE MARKUP LANGUAGE 94. FULL FORM OF YT - YOTTABYTE 95. FULL FORM OF ZB - ZETTABYTE 96. FULL FORM OF ZIP- ZONE IMPROVEMENT PLAN 97. FULL FORM OF CD - COMPAQ DISK 98. FULL FORM OF DVD - DIGITAL VERSATILE DISK AND DIGITAL VIDEO DISC 99. FULL FORM OF FORTRAN - FORMULA TRANSLATION 100. FULL FORM OF LAN - Local Area Network == x == General Knowledge (GK) Question & Answer SUBSCRIBE : http://bit.ly/2wrZqn4
Views: 390895 General Knowledge GK Q&A
Three principles for data science: predictability, stability, and computability
 
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Speaker: Bin Yu, Chancellor’s Professor of Statistics at the University of California at Berkeley Berkeley Distinguished Lectures in Data Science, Fall 2017 https://bids.berkeley.edu/news/berkeley-distinguished-lectures-data-science Title: Three principles for data science: predictability, stability, and computability Date: September 12, 2017 Time: 4:10pm to 5:00pm Locations: BIDS, 190 Doe Library, UC Berkeley ABSTRACT In this talk, I'd like to discuss the intertwining importance and connections of three principles of data science in the title in data-driven decisions. Making prediction as its central task and embracing computation as its core, machine learning has enabled wide-ranging data-driven successes. Prediction is a useful way to check with reality. Good prediction implicitly assumes stability between past and future. Stability (relative to data and model perturbations) is also a minimum requirement for interpretability and reproducibility of data driven results (cf. Yu, 2013). It is closely related to uncertainty assessment. Obviously, both prediction and stability principles can not be employed without feasible computational algorithms, hence the importance of computability. The three principles will be demonstrated in the context of two neuroscience collaborative projects with the Gallant Lab and through analytical connections. In particular, the first project adds stability to predictive modeling used for reconstruction of movies from fMRI brain signlas to gain interpretability of the predictive model. The second project uses predictive transfer learning that combines AlexNet, GoogleNet and VGG with single V4 neuron data for state-of-the-art prediction performance. Moreover, it provides stable function characterization of neurons via (manifold) deep dream images from the predictive models in the difficult primate visual cortex V4. Our V4 results lend support, to a certain extent, to the resemblance of these CNNs to a primate brain. SPEAKER Bin Yu is Chancellor’s Professor in the Departments of Statistics and of Electrical Engineering & Computer Science at the University of California at Berkeley and a former Chair of Statistics at Berkeley. She is founding co-director of the Microsoft Joint Lab at Peking University on Statistics and Information Technology. Her group at Berkeley is engaged in interdisciplinary research with scientists from genomics, neuroscience, and medicine. In order to solve data problems in these domain areas, her group employs quantitative critical thinking and develops statistical and machine learning algorithms and theory. She has published more than 100 scientific papers in premier journals in statistics, machine learning, information theory, signal processing, remote sensing, neuroscience, genomics, and networks. She is a member of the U.S. National Academy of Sciences and fellow of the American Academy of Arts and Sciences. She was a Guggenheim Fellow in 2006, an invited speaker at ICIAM in 2011, the Tukey Memorial Lecturer of the Bernoulli Society in 2012, and an invited speaker at the Rietz Lecture of Institute of Mathematical Statistics (IMS) in 2016. She was IMS president in 2013–2014, and she is a fellow of IMS, ASA, AAAS, and IEEE. She has served or is serving on leadership committees of NAS-BMSA, SAMSI, IPAM, and ICERM and on editorial boards for the Journal of Machine Learning, Annals of Statistics, and Annual Review of Statistics. BERKELEY DISTINGUISHED LECTURES IN DATA SCIENCE https://bids.berkeley.edu/news/berkeley-distinguished-lectures-data-science The Berkeley Distinguished Lectures in Data Science, co-hosted by the Berkeley Institute for Data Science (BIDS) and the Berkeley Division of Data Sciences, features faculty doing visionary research that illustrates the character of the ongoing data, computational, inferential revolution. In this inaugural Fall 2017 "local edition," we bring forward Berkeley faculty working in these areas as part of enriching the active connections among colleagues campus-wide. All campus community members are welcome and encouraged to attend. Arrive at 3:30pm for tea, coffee, and discussion.
GOTO 2016 • Mining Repository Data to Debug Software Development Teams • Elmar Juergens
 
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This presentation was recorded at GOTO Berlin 2016 http://gotober.com Elmar Juergens - Consultant at CQSE GmbH ABSTRACT If the team architecture and the technical architecture do not fit together, problems arise. Both architectures evolve, however, often causing misalignment. How can we notice such mismatches and react in time? In this talk, I present modern [...] Download slides and read the full abstract here: https://gotocon.com/berlin-2016/presentations/show_talk.jsp?oid=8030 https://twitter.com/gotober https://www.facebook.com/GOTOConference http://gotocon.com
Views: 1515 GOTO Conferences
Neural Networks Explained - Machine Learning Tutorial for Beginners
 
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If you know nothing about how a neural network works, this is the video for you! I've worked for weeks to find ways to explain this in a way that is easy to understand for beginners. Past Videos: Intro to Machine Learning with Javascript: https://www.youtube.com/watch?v=9Hz3P1VgLz4&list=PLoYCgNOIyGABWLy_XoLSxTVRe2bltV8GM&index=2&t=0s Machine Learning 2 - Building a Recommendation Engine: https://www.youtube.com/watch?v=lvzekeBQsSo&list=PLoYCgNOIyGABWLy_XoLSxTVRe2bltV8GM&index=3&t=0s Machine learning and neural networks are awesome. This video provides beginners with an easy tutorial explaining how a neural network works - what math is involved, and a step by step explanation of how the data moves through the network. The example used will be a feed forward neural network with back propagation. It explains the difference between linear and non linear data, the importance of the activation function, learning rate, and momentum configurations. -~-~~-~~~-~~-~- Also watch: "Responsive Design Tutorial - Tips for making web sites look great on any device" https://www.youtube.com/watch?v=fgOO9YUFlGI -~-~~-~~~-~~-~-
Views: 140984 LearnCode.academy
About the Articles Database on BrentBrookbush.com
 
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To call what we are offering “articles” is almost unfair. These are integrated, multi-media lessons, designed to deliver education in the format you want it, or the format that presents the information with the most clarity. We can integrate so much into every article, and for this reason, the articles database is the soul of BrentBrookbush.com. We know that information is only useful, when you can find it… and this is dependent on organization and search-ability (two of our continued efforts at the Brookbush Institute). To make locating the article you wish to find, easy… we have added 3 ways to search. You can use the search bar and browse articles just as you would use “Google” to browse the web. You can use the category filter (my favorite way to search articles), and refine your selection to topics you are interested in, and when you select an article, the category filter column becomes a list of articles in the same category/sub-category… this comes in handy when searching through larger sections of the site. Last, and perhaps most important for inspiring curiosity and enjoyment of the learning process, every article is full of hyperlinks to related text. This use of hyperlinks is purposeful, linking information that may help you understand a concept, may help you understand a concept deeper, may help you get past a concept you did not understand, or just help you get lost in education as one article you enjoy… leads you to a hyperlink of another article you enjoy, which leads you to another, etc. We believe this use of hyperlinks reinforces learning information by association rather than attempting to memorize desperate facts and concepts… which research has been shown to be a more natural and effective way to learn. Now, we know that every concept is not worth reading an entire article, so we have created and continue to develop our pop-up glossary. If you see a word, phrase or terminology with a dashed underline, click on it, and the definition will pop-up. You can also browse the glossary at any time, by clicking the glossary button, which always appears at the bottom of the left-hand column. Every article is full of illustrations (especially the anatomy articles). We are picture hounds, and geek out when we find a picture that shows us some new vantage point of a structure, or answers some burning question about muscle, or just does a beautiful job of highlighting a little form intricacy of an exercise. We use illustrations where ever we can, because we know that most of us are visual learners, and would gladly go back to the picture books we had in elementary school if they contained the information we needed. We are big fans of headers, tables, bullets and summaries..... Help us caption & translate this video! http://amara.org/v/6sE8/
Views: 691 Brent Brookbush
Heterogeneous data integration and Reverse Engineering | Alexandre DURUPT | TEDxUTCompiègne
 
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What is the Reverse Engineering ? What is it useful for ? What are the main challenges to perform an efficient reverse engineering ? Alexandre DURUPT is an assistant professor at the University of Technology of Compiegne, UMR CNRS 7337 Roberval Laboratory since 2011. He works on Heterogeneous data integration and Reverse Engineering in mechanical context. He is the person in charge of a common laboratory DIMEXP (DIgital mock-up for Multi-EXPertises Integration, http://dimexp.utc.fr). 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: 1307 TEDx Talks
Data visualization ki basics- urdu mein
 
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Please watch: "Master Excel Series Degree Function- ماسٹر ایکسل سیریز ڈگری فارمولہ" https://www.youtube.com/watch?v=P3BJYzWusL0 --~-- yeh video urdu version hai basics of data visualization course ka. Is video mein hum visualization goals discuss krien ge or yeh smjhne ki koshish krien ge k visualizations kaise banty hai or uski basics kya hoty hein hamare channel ko subscribe krien. Isk ilawa hum apko yeh training apki location par bhi akar de skte hein.
Views: 996 TheQLGConsultants
What is an API?
 
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What exactly is an API? Finally learn for yourself in this helpful video from MuleSoft, the API experts. https://www.mulesoft.com/platform/api The textbook definition goes something like this: “An application programming interface (API) is a set of routines, protocols, and tools for building software applications. An API expresses a software component in terms of its operations, inputs, outputs, and underlying types. An API defines functionalities that are independent of their respective implementations, which allows definitions and implementations to vary without compromising each other. A good API makes it easier to develop a program by providing all the building blocks. APIs often come in the form of a library that includes specifications for routines, data structures, object classes, and variables. In other cases, notably SOAP and REST services, an API is simply a specification of remote calls exposed to the API consumers. An API specification can take many forms, including an International Standard, such as POSIX, vendor documentation, such as the Microsoft Windows API, or the libraries of a programming language, e.g., the Standard Template Library in C++ or the Java APIs. An API differs from an application binary interface (ABI) in that an API is source code-based while an ABI is a binary interface. For instance POSIX is an API, while the Linux Standard Base provides an ABI”. To speak plainly, an API is the messenger that runs and delivers your request to the provider you’re requesting it from, and then delivers the response back to you. To give you a familiar example, think of an API as a waiter in a restaurant. Imagine you’re sitting at the table with a menu of choices to order from, and the kitchen is the provider who will fulfill your order. What’s missing is the critical link to communicate your order to the kitchen and deliver your food back to your table. That’s where the waiter (or API) comes in. ”AHEM” The waiter takes your order, delivers it to the kitchen, and then delivers the food (or response) back to you. (Hopefully without letting your order crash if designed correctly) Now that we’ve whetted your appetite, let’s apply this to a real API example. In keeping with our theme, let’s book a flight to a culinary capital – Paris. You’re probably familiar with the process of searching for airline flights online. Just like at a restaurant, you have a menu of options to choose from ( a dropdown menu in this case). You choose a departure city and date, a return city and date, cabin class, and other variables (like meal or seating, baggage or pet requests) In order to book your flight, you interact with the airline’s website to access the airline’s database to see if any seats are available on those dates, and what the cost might be based on certain variables. But, what if you are not using the airline’s website, which has direct access to the information? What if you are using online travel service that aggregates information from many different airlines? Just like a human interacts with the airline’s website to get that information, an application interacts with the airline’s API. The API is the interface that, like your helpful waiter, runs and and delivers the data from that online travel service to the airline’s systems over the Internet. It also then takes the airline’s response to your request and delivers right back to the online travel service . And through each step of the process it facilitates that interaction between the travel service and the airline’s systems - from seat selection to payment and booking. So now you can see that it’s APIs that make it possible for us all to use travel sites. They interface with with airlines’ APIs to gather information in order to present options back to us The same goes for all interactions between applications, data and devices - they all have API’s that allow computers to operate them, and that's what ultimately creates connectivity. API’s provide a standard way of accessing any application, data or device whether it is shopping from your phone, or accessing cloud applications at work. So, whenever you think of an API, just think of it as your waiter running back and forth between applications, databases and devices to deliver data and create the connectivity that puts the world at our fingertips. And whenever you think of creating an API, think MuleSoft.
Views: 2295036 MuleSoft Videos
How to create a Data Dictionary
 
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Heyy guys here is a tutorial on how to create a basic Data Dictionary!
Views: 58672 Brandon The Crab
Eulalia Veny - Recipe for text analysis in social media
 
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Recipe for text analysis in social media: [EuroPython 2018 - Talk - 2018-07-25 - PyCharm [PyData]] [Edinburgh, UK] By Eulalia Veny The analysis of text data in social media is gaining more and more importance every day. The need for companies to know what people think and want is key to invest money in providing customers what they want. The first approach to text analysis was mainly statistical, but adding linguistic information has been proven to work well for improving the results. One of the problems that you need to address when analyzing social media is time. People are constantly exchanging information, users write comments every day about what they think of a product, what they do or the places they visit. It is difficult to keep track of everything that happens. Moreover, information is sometimes expressed in short sentences, keywords, or isolated ideas, such as in Tweets. Language is usually unstructured because it is composed of isolated ideas, or without context. I will talk about the problem of text analysis in social media. I will also explain briefly Naïve Bayes classifiers, and how you can easily take advantage of them to analyse sentiment in social media, and I will use an example to show how linguistic information can help improve the results. I will also evaluate the pros and cons of supervised vs unsupervised learning. Finally, I will introduce opinion lexicons, both dictionary based and corpus-based, and how lexicons can be used in semi-supervised learning and supervised learning. If I have time left, I will explain about other use cases of text analysis. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2018.europython.eu/en/speaker-release-agreement/
What is a dataset?
 
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Scientists collect all sorts of information in all different kinds of ways. One of the most common ways scientists collect information is through the Rectangular Long Form Dataset. If at this point I’ve lost you, it’s okay; this video explains what that is and how it’s a scientist’s picture of reality.
Views: 14770 Elon University Poll
Text mining 2
 
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In this video, we are going to continue to use Text Mining widgets in Orange. In order to download the datasets please go to: https://github.com/RezaKatebi/Crash-course-in-Object-Oriented-Programming-with-Python
Views: 224 DataWiz
Harnessing vehicle safety data with Watson Explorer Content Analytics
 
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Discover how IBM Watson Explorer Content Analytics is helping safety analysts solve real-life problems by identifying previously unseen correlations within unstructured data. Thanks to Watson Explorer Content Analytics, manufacturers can take advantage of user feedback to help them address consumer safety issues at the point of failure. Subscribe to the IBM Analytics Channel: https://www.youtube.com/subscription_... The world is becoming smarter every day, join the conversation on the IBM Big Data & Analytics Hub: http://www.ibmbigdatahub.com https://www.youtube.com/user/ibmbigdata https://www.facebook.com/IBManalytics https://www.twitter.com/IBMAnalytics https://www.linkedin.com/company/ibm-... https://www.slideshare.net/IBMBDA
Views: 2694 IBM Analytics
The Oxford 3000 Words - English Words List - Learn English Words
 
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Learn English words list, the Oxford 3000 words audio and subtitle. ▶ Link download Oxford 3000 Words PDF: http://goo.gl/rEO67K ☞ Thanks for watching! ☞ Please share and like if you enjoyed the video :) thanks so much ♥ ─────────────────── ▶ Please subscribe to update new videos. Subscribe To Update New Lesson: https://www.youtube.com/channel/UCV1h_cBE0Drdx19qkTM0WNw?sub_confirmation=1
Visual and Audio Data Collection Skegness 17_02_2013
 
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Visual and Audio Data Collection, Skegness Transect walk 17.02.2013
Views: 109 Karolina
Big Data Specialist: MapReduce
 
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Jigsaw Academy (http://www.jigsawacademy.com and http://www.analyticstraining.com) presents a video on analytics. Jigsaw Academy is an award winning premier online analytics training institute that aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training to develop business-ready professionals.Jigsaw Academy has been acknowledged by blue chip companies for quality training Follow us on: https://www.facebook.com/jigsawacademy https://twitter.com/jigsawacademy http://jigsawacademy.com/
Views: 3473 Jigsaw Academy
The Power of Cognitive Probability Graphs
 
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Graphs and Artificial Intelligence have long been a focus for Franz Inc. and currently we are collaborating with Montefiore Health System, Intel, Cloudera, and Cisco to improve a patient's ability to understand the probabilities of their future health status. By combining artificial intelligence, semantic technologies, big data, graph databases and dynamic visualizations we are deploying a Cognitive Probability Graph concept as a means to help predict future medical events. The power of Cognitive Probability Graphs stems from the capability to combine the probability space (statistical patient data) with a knowledge base of comprehensive medical codes and a unified terminology system. Cognitive Probability Graphs are remarkable not just because of the possibilities they engender, but also because of their practicality. The confluence of machine learning, semantics, visual querying, graph databases, and big data not only displays links between objects, but also quantifies the probability of their occurrence. We believe this approach will be transformative for the healthcare field and we see numerous possibilities that exist across business verticals. During the presentation we will describe the Cognitive Probability Graph concepts using a distributed graph database on top of Hadoop along with the query language SPARQL to extract feature vectors out of the data, applying R and SPARK ML, and then returning the results for further graph processing.
Views: 10917 AllegroGraph
But what *is* a Neural Network? | Deep learning, chapter 1
 
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Home page: https://www.3blue1brown.com/ Brought to you by you: http://3b1b.co/nn1-thanks Additional funding provided by Amplify Partners For any early-stage ML entrepreneurs, Amplify would love to hear from you: [email protected] Full playlist: http://3b1b.co/neural-networks Typo correction: At 14:45, the last index on the bias vector is n, when it's supposed to in fact be a k. Thanks for the sharp eyes that caught that! For those who want to learn more, I highly recommend the book by Michael Nielsen introducing neural networks and deep learning: https://goo.gl/Zmczdy There are two neat things about this book. First, it's available for free, so consider joining me in making a donation Nielsen's way if you get something out of it. And second, it's centered around walking through some code and data which you can download yourself, and which covers the same example that I introduce in this video. Yay for active learning! https://github.com/mnielsen/neural-networks-and-deep-learning I also highly recommend Chris Olah's blog: http://colah.github.io/ For more videos, Welch Labs also has some great series on machine learning: https://youtu.be/i8D90DkCLhI https://youtu.be/bxe2T-V8XRs For those of you looking to go *even* deeper, check out the text "Deep Learning" by Goodfellow, Bengio, and Courville. Also, the publication Distill is just utterly beautiful: https://distill.pub/ Lion photo by Kevin Pluck ------------------ Animations largely made using manim, a scrappy open source python library. https://github.com/3b1b/manim If you want to check it out, I feel compelled to warn you that it's not the most well-documented tool, and has many other quirks you might expect in a library someone wrote with only their own use in mind. Music by Vincent Rubinetti. Download the music on Bandcamp: https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown Stream the music on Spotify: https://open.spotify.com/album/1dVyjwS8FBqXhRunaG5W5u If you want to contribute translated subtitles or to help review those that have already been made by others and need approval, you can click the gear icon in the video and go to subtitles/cc, then "add subtitles/cc". I really appreciate those who do this, as it helps make the lessons accessible to more people. ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: http://3b1b.co/recommended Various social media stuffs: Website: https://www.3blue1brown.com Twitter: https://twitter.com/3Blue1Brown Patreon: https://patreon.com/3blue1brown Facebook: https://www.facebook.com/3blue1brown Reddit: https://www.reddit.com/r/3Blue1Brown
Views: 4457297 3Blue1Brown
Sr Data Warehouse Solutions Architect
 
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This local Health Organization is hiring a Senior Data Warehouse Solutions Architect! This company is a global leader in encouraging healthy living and scientific research! They utilize cutting-edge technologies and the latest Business Intelligence dashboard tools. They have excellent salaries, outstanding benefits, and challenging opportunities! You will Impact the company by: • Leading complex Data Warehouse and Business Intelligence analysis tasks that require advanced techniques in the forefront of technology. • Partnering with business and IT leadership to identify, design, and implement strategic Business Intelligence Solutions. • Define a Data Management Strategy and create the required data architecture. • Fosters use of self-service analytics, data governance and visual discovery. • If you have over 9+ years experience in Microsoft Business Intelligence Development; 7+ years experience in data architecture and ETL development; and skilled in BI solutions… Please contact me! • Feel free to forward this e-mail to anyone who may be of interest. To find out more about this position and other roles, send your resume to [email protected]
Relational Database Concepts
 
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Basic Concepts on how relational databases work. Explains the concepts of tables, key IDs, and relations at an introductory level. For more info on Crow's Feet Notation: http://prescottcomputerguy.com/tmp/crows-foot.png
Views: 603430 Prescott Computer Guy
'Quality Assurance' Vs "Quality Control' .सिर्फ 10 मिनट में सीखें (हिंदी)
 
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In just 10 minutes understand difference between 'Quality Assurance' Vs "Quality Control . सिर्फ 10 मिनट में सीखें (हिंदी) Explained difference in 9 categories. Watch other videos from ‘Quality HUB India’- https://www.youtube.com/channel/UCdDEcmELwWVr_77GpqldKmg/videos • Subscribe to my channel ‘Quality HUB India’ for getting notification. • Like, comment & Share the video with your colleague and friends Link to buy My books 1. Mistake-Proofing Simplified: An Indian Perspective: https://www.amazon.in/gp/product/8174890165/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=8174890165&linkCode=as2&tag=qhi-21 2. Management Thoughts on Quality for Every Manager: https://www.amazon.in/gp/product/B0075MCLTO/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B0075MCLTO&linkCode=as2&tag=qhi-21 Gadgets I use and Link to buy 1. OnePlus 5 - Mobile https://www.amazon.in/gp/product/B01MXZW51M/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B01MXZW51M&linkCode=as2&tag=qhi-21 2. HP 14-AM122TU 14-inch Laptop https://www.amazon.in/gp/product/B06ZYLLT8G/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B06ZYLLT8G&linkCode=as2&tag=qhi-21 3. Canon EOS 700D 18MP Digital SLR Camera https://www.amazon.in/gp/product/B00VT61IKA/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B00VT61IKA&linkCode=as2&tag=qhi-21 4. Sonia 9 Feet Light Stand LS-250 https://www.amazon.in/gp/product/B01K7SW2OQ/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B01K7SW2OQ&linkCode=as2&tag=qhi-21 5. Sony MDR-XB450 On-Ear EXTRA BASS Headphones https://www.amazon.in/gp/product/B00NFJGUPW/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B00NFJGUPW&linkCode=as2&tag=qhi-21 6. QHM 602 USB MINI SPEAKER https://www.amazon.in/gp/product/B00L393EXC/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B00L393EXC&linkCode=as2&tag=qhi-21 7. Photron Tripod Stedy 400 with 4.5 Feet Pan Head https://www.amazon.in/gp/product/B00UBUMCNW/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B00UBUMCNW&linkCode=as2&tag=qhi-21 8. Tie Clip Collar mic Lapel https://www.amazon.in/gp/product/B00ITOD6NM/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B00ITOD6NM&linkCode=as2&tag=qhi-21 9. Hanumex Generic Green BackDrop Background 8x12 Ft for Studio Backdrop https://www.amazon.in/gp/product/B06W53TMDR/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B06W53TMDR&linkCode=as2&tag=qhi-21 10. J 228 Mini Tripod Mount + Action Camera Holder Clip Desktop Self-Tripod For Camera https://www.amazon.in/gp/product/B072JXX9DB/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B072JXX9DB&linkCode=as2&tag=qhi-21 11. Seagate Backup Plus Slim 1TB Portable External Hard Drive https://www.amazon.in/gp/product/B00GASLJK6/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B00GASLJK6&linkCode=as2&tag=qhi-21 Watch other Videos from ‘Quality HUB India’ 1. Process Capability Study (Cp,Cpk, Pp & Ppk) - https://www.youtube.com/watch?v=5hBRE0uji5w 2. What is Six Sigma ?Learn Six Sigma in 30 minutes- https://www.youtube.com/watch?v=1oiKYydbrSw 3. Failure Mode and Effects Analysis (FMEA) - https://www.youtube.com/watch?v=UxSBUHgb1V0&t=25s 4. Statistical Process Control (SPC) in Hindi – https://www.youtube.com/watch?v=WiVjjoeIrmc&t=115s 5. Measurement System Analysis (MSA) (Part 1) - https://www.youtube.com/watch?v=GGwaZeMmZS8&t=25s 6. Advanced Product Quality Planning(APQP) - https://www.youtube.com/watch?v=FaawYoPsUYE&t=35s 7. ‘Quality Circles' - https://www.youtube.com/watch?v=kRp9OIANgG8&t=25s 8. What is 'Cost of Quality' and 'Cost of Poor Quality' - https://www.youtube.com/watch?v=IsCRylbHni0&t=25s 9. How to perfectly define a problem ? 5W and 1H approach - https://www.youtube.com/watch?v=JXecodDxBfs&t=55s 10. What is 'Lean Six Sigma' ? Learn the methodology with benefits. - https://www.youtube.com/watch?v=86XJqf1IhQM&t=41s 11. What is KAIZEN ? 7 deadly Waste (MUDA) and benefit of KAIZEN - https://www.youtube.com/watch?v=TEcE-cKk1qI&t=115s 12. What is '5S' Methodology? (Hindi)- https://www.youtube.com/watch?v=dW8faNOX91M&t=25s 13. 7 Quality Control Tools - (Part 1) Hindi - https://www.youtube.com/watch?v=bQ9t3zoM0NQ&t=88s 14. "KAIZEN" in HINDI- https://www.youtube.com/watch?v=xJpbHTc3wmo&t=25s 15. 'PDCA' or 'Deming Cycle'. Plan-DO-Check-Act cycle - https://www.youtube.com/watch?v=Kf-ax6qIPVc 16. Overall Equipment Effectiveness (OEE) - https://www.youtube.com/watch?v=5OM5-3WVtd0&feature=youtu.be 17. Why-Why Analysis? - Root Cause Analysis Tool - https://www.youtube.com/watch?v=Uxn6N6OJvwA
Views: 532890 Quality HUB India
Tableau Tutorial for Beginners | Data Visualisation Tableau Training Introduction | Great Learning
 
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#TableauTutorial | Also watch Tableau Advanced Part 2 for Free: https://greatlearningforlife.com/tableau WHAT YOU WILL LEARN IN THIS VIDEO: When you finish this tutorial, you will be a highly proficient Tableau user, confident to apply Tableau to solve real-life problems. Access 100s of hours of similar high-quality FREE learning content at: http://greatlearningforlife.com PREREQUISITES: We assume no prior knowledge of data science, statistics or programming. This course is designed carefully to take you step by step from getting familiar with the interface, learning basic concepts to tackling more advanced topics. YOU WILL LEARN HOW TO: - Navigate the Tableau interface and perform basic operations - Import and connect to your data - Edit and save a data source - Understand Tableau terminology - Use the Tableau interface/paradigm to create powerful visualizations effectively - Create basic calculations including basic arithmetic calculations, custom aggregations and ratios, date math, and quick table calculations - Build dashboards and storyboards to share visualizations ------------------------------------------- WHY LEARN TABLEAU THROUGH THIS COURSE: - Step by Step Learning Path - Learn by Doing - Easy to follow and helps prepare for Tableau Certification - Concepts explained by solving real-life industry problems - Practical tips and tricks to save time - Taught by Industry Professionals and Tableau experts To install Tableau Public go to: http://public.tableau.com ----------------------------------------------------------------- WHY LEARN TABLEAU?: Some of you probably know exactly why you want to learn Tableau and want to get right into it. Others might still have a few questions. So, what is Tableau actually? How can it help you? How is it different from say Excel or Powerpoint? What will you actually be able to do after learning Tableau? All valid questions. A lack of data is no longer the problem. Data is everywhere. The real challenge is finding out what data is really important to your organization, being able to identify trends, causes, patterns, how to maximize your revenues or profit margins from data – so it is about being able to quickly extract actionable business insights from data. Whether you are a business intelligence analyst, a manager needing to create and analyse reports quickly or a data scientist you need to convey the great story that your data analysis is telling you. So how do you do that? Reams of numbers or pages of technical analysis won’t work – especially when your boss or higher management don’t have a background in data science or advanced statistics. What is scientifically proven is that the human brain understands large amounts of complex data best when it is presented visually – charts, graphs, plots these visual tools help to summarise and convey even the most complicated of your findings to others Sure you can do basic reports in Excel or you can make a PowerPoint presentation. But they are limited in what you can do. Or it is difficult and it will take you a lot of time and tinkering. ------------------------------------------- SO WHAT IS DIFFERENT ABOUT TABLEAU? Easy to Learn Yet Powerful – even for non-technical folks: Tableau is a software program which helps you use a drag and drop, highly visual and easy to understand interface and tools to quickly create professional level compelling visual dashboards and stories of your data. It’s easy to learn, yet is extremely powerful. ----------------------------------------- #tableau #tableauTraining #dataviz #PowerBI #BigData #Analytics #BI #infographic 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 thepillars 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: 299574 Great Learning
Ethics of Artificial Intelligence - Part 1 :: Machine Intelligence Course, Lecture 23
 
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SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo) Target Audience: Senior Undergraduate Engineering Students Instructor: Professor H.R.Tizhoosh (http://kimia.uwaterloo.ca/) Course Outline - The objective of this course is to introduce the students to the main concepts of machine intelligence as parts of a broader framework of “artificial intelligence”. An overview of different learning, inference and optimization schemes will be provided, including Principal Component Analysis, Support Vector Machines, Self-Organizing Maps, Decision Trees, Backpropagation Networks, Autoencoders, Convolutional Networks, Fuzzy Inferencing, Bayesian Inferencing, Evolutionary algorithms, and Ant Colonies. Lecture 1 – Introduction (Definition of Intelligence, terminology, history of AI, Turing Test, Chinese Room) Lecture 2 – Principal Components Analysis (PCA) Lecture 3 – Linear Discriminant Analysis (LDA), t-distributed Stochastic Neighbor Embeddings (t-SNE) Lecture 4 – AI and Vision, feature extraction, Harris corners, Fisher Vector, VLAD, SIFT, Bag of Visual Words Lecture 5 – AI and data, generalization and memorization, K-fold cross-validation, leave-one-out, regularization, overfitting, underfitting Lecture 6 – Clustering, K-means, self-organizing maps (SOM) Lecture 7 – Classification, support vector machines (SVM) Lecture 8 – Cluster validity, SSW, SSB, Dunn’s Index, WB Index, Fuzzy sets and fuzzy c-means (FCM) Lecture 9 – Linear regression, artificial neurons, abstractions of neurons, weight adjustment for plasticity Lecture 10 – Artificial Neural Networks, XOR problem, hidden layers, learning algorithm, multi-layer perceptrons (MLPs), Delta Rule Lecture 11 – Backpropagation networks (incremental and batch-wise), stopping criteria, autoencoders Lecture 12 – Restricted Boltzmann Machines (RBMs), training deep autoencoders Lecture 13 – Neocognitron, Convolutional Neural Networks (CNNs), overfitting in deep learning Lecture 14 – Reinforcement Learning, reward and punishment Lecture 15 – Designing Reinforcement Learning agents, Temporal differencing, Q-learning Lecture 16 – Decision Trees, entropy, information gain Lecture 17 – Fuzzy Logic, modus ponens, modus tollens, inference, fuzzy control, inverted pendulum Lecture 18 – Bayesian Learning, Bayes Theorem, probability rules Lecture 19 - Naïve Bayes classifier Lecture 20 – Evolutionary algorithms, genetic algorithms Lecture 21 – Genetic algorithms: encoding, crossover and mutation, different models, differential evolution, opposition-based learning Lecture 22 – Swarm Intelligence, Ant Colony Optimization (ACO) Lecture 23 – Ethics of Artificial Intelligence, Part 1 (Philosophy) Lecture 24 – Ethics of Artificial Intelligence, Part 2 (Practical Cases)
Views: 397 Kimia Lab
Autodesk Vault Product Data Management Profile
 
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Vault Product Manager, Jeremy Lambert, gives an overview of the benefits you can experience with Product Data Management using Autodesk Vault software, highlighting customer examples from Joy Mining Machinery and Reilly Windows and Doors. Learn more at http://www.autodesk.com/vault
Views: 5911 TheVaultKnowsAll
Australian Private Investigator Police Data base breach
 
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Australian Private Investigator Police Data base breach LRD and QPol
Views: 70 Michael Evans
1  Introduction to Data Science & Data Analytics - DataHills Srinivas
 
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Online Classes on DATA SCIENCE / DATA ANALYTICS / MACHINE LEARNING with R, PYTHON & WEKA. Good Value for Money - Charged less than any other training institutes. For details Contact: +91 9292005440 or [email protected] INTRODUCTION TO DATA SCIENCE: ============================= What is Data Science? Who is Data Scientist? Who can be Data Scientist? Data Science Process Modern Data Scientist Data Science Workflow Technologies used in Data Science What is DATA SCIENCE : --------------------------------------- Data science is a "concept to statistics, data analysis, machine learning and their related methods" in order to "understand and analyze” with data. Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining. Data Science is also called as "The Sexiest Job of the 21st Century". DATA ANALYSIS: -------------------------- Data analysis is the process of extracting information from data. It involves multiple stages including establishing a data set, preparing the data for processing, applying models, identifying key findings and creating reports. The goal of data analysis is to find actionable insights that can inform decision making. Data analysis can involve data mining, descriptive and predictive analysis, statistical analysis, business analytics and big data analytics. Who is Data Scientist: ------------------------------------ Statistician + Software Engineer A person who is better at statistics than any software engineer or a person who is better at software engineering than any statistician is a data scientist. Who can be Data Scientist: ------------------------------------------ Computing Skills + Mathematics, Probability & Statistical Knowledge + Domain Expertise can be a data scientist Data Science Process: ------------------------------------ Real World - Raw data collected - Data is processed - Clean Data set - Exploratory Data Analysis - Models & Algorithms - Communicate visual report (Making Decisions) - Data Product - Real World Modern Data Scientist: -------------------------------------- Math & Statistics Programming & Database Domain Knowledge & Soft Skills Communication & Visualization Data Science Workflow: -------------------------------------- Problem definition Data Collection & Preparing Model Development Model Deployment Performance Improvement Technologies used in Data Science: --------------------------------------------------------- R Python Weka etc.......
Views: 1515 Data Hills7
Christos Faloutsos: How to find patterns in large graphs
 
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http://www.linkedin.com/techtalks
Views: 7457 LinkedInTechTalks
Voyant Tools Tutorial
 
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A brief overview of how to use open source text analysis software to teach literature.
Views: 6658 Tom Liam Lynch
What is the Fundamental theorem of Algebra, really? | Abstract Algebra Math Foundations 217
 
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Here we give restatements of the Fundamental theorems of Algebra (I) and (II) that we critiqued in our last video, so that they are now at least meaningful and correct statements, at least to the best of our knowledge. The key is to abstain from any prior assumptions about our understanding of continuity and "real " or "complex" numbers, and state everything in terms of rational numbers. For this we briefly first review some rational complex arithmetic, crucially the concept of quadrance of a complex number which ought to be a core definition in undergraduate courses. These restatements were first proposed some years ago in my AlgTop series of videos. It should be emphasized that we do NOT currently have proofs for these "theorems", so there is a huge opportunity here for people to make a significant contribution to mathematics. But new and deeper understanding is required, at least I believe so, and hopefully we can aspire to computationally oriented proofs, that actually tell us how to go about finding approximate zeroes to a prescribed level of accuracy. Working this out satisfactorily will be as significant an accomplishment as any 20th century mathematical achievement.
Graph using Adjacency List in Java
 
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Implement Graph in Java.. using Adjacency List. Part I An Adjacency List is Nothing but and Array of Linked List which is more memory efficient than Adjacency Matrix for sparse graph. Here is the code : https://github.com/arpanpathak/Data-Structures-and-Algorithm/blob/master/Data%20Structures/Graph/java%20implementaion/GraphExample.java
Views: 50086 Arpan Pathak
web mining
 
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Views: 195 Hama Awat