In the bayesian classification The final ans doesn't matter in the calculation Because there is no need of value for the decision you have to simply identify which one is greater and therefore you can find the final result. -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 208348 Well Academy
23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 471723 Brandon Weinberg
** RPA Training: https://www.edureka.co/robotic-process-automation-training ** This session on UiPath PDF Data Extraction will cover all the concepts on how to extract data from PDFs using UiPath. Below are the topics covered in the video: 02:03 Extracting Large Texts 16:49 Extracting Specific Elements Subscribe to our Edureka YouTube channel to get video updates: https://goo.gl/6ohpTV 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 How it Works? 1. This is a 4 Week Instructor led Online Course, 25 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training, you will have to work on a project, based on which we will provide you a Grade and a Verifiable Certificate! -------------------------------------------------------------------------------------- About the Robotic Process Automation Course Edureka’s RPA training helps you to understand the concepts around Robotic Process Automation using the leading RPA tool named ‘UiPath’. Robotic Process Automation is the automation of repetitive and rule based human tasks working with the software applications at the presentation/UI level i.e. no software integrations are needed at middleware, server or database levels. In this course, you will learn about the RPA concepts and will gain in-depth knowledge on UiPath tool using which you will be able to automate real-world processes at the enterprise level such as Insurance Claims Processing, Accounts Payable / Purchase Orders Processing, Invoice Processing, Complaints Management, Customer Feedback Analysis, Employee Onboarding, Compliance Reporting, and many more. -------------------------------------------------------------------------------------- What are the Objectives of our Robotic Process AutomationTraining? After completing this course, you will be able to: 1. Know about Robotics Process Automations and their working 2. Assess the key considerations while designing an RPA solution 3. Work proficiently with the leading RPA tool ‘UiPath 4. Have practical knowledge on designing RPA solutions using UiPath 5. Perform Image and Text automation 6. Learn Data Manipulation using variables and arguments 7. Create automations with applications running in Virtual Environments 8. Debug and handle exceptions in workflow automations ------------------------------------------------------------------------------------------------------- Why learn Robotic Process Automation? Robotic Process Automation (RPA) is an automation technology for making smart software by applying intelligence to do high volume and repeatable tasks that are time-consuming. RPA is automating the tasks of wide variety of industries, hence reducing the time and increasing the output. Some of facts about RPA includes: 1. A 2016 report by McKinsey and Co. predicts that the robotic process automation market could be worth $6.7 trillion by 2025 2. A major global wine business, after implementing RPA, increased the order accuracy from 98% to 99.7% while costs reduced to Rs. 5.2 Crore 3. A global dairy company used RPA to automate the processing and validation of delivery claims, reduced goodwill write-offs by Rs. 464 Million ------------------------------------------------------------------------------------------------------- What are the pre-requisites for this course? To master the concept of RPA, you need to have basic understanding of : 1.Basic programming knowledge of using if/else and switch conditions 2.Logical thinking ability Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 16628 edureka!
#datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 331510 Last moment tuitions
( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Website: https://www.edureka.co/data-science Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 78388 edureka!
In this video FP growth algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining algorithms in hindi, data mining in hindi, data mining lecture, data mining tools, data mining tutorial, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining fp growth, data mining fp growth algorithm, data mining fp tree example, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining, fp growth algorithm, fp growth algorithm example, fp growth algorithm in data mining, fp growth algorithm in data mining example, fp growth algorithm in data mining examples ppt, fp growth algorithm in data mining in hindi, fp growth algorithm in r, fp growth english, fp growth example, fp growth example in data mining, fp growth frequent itemset, fp growth in data mining, fp growth step by step, fp growth tree
Views: 165675 Well Academy
Computer Education for all provides complete lectures series on Data Structure and Applications which covers Introduction to Data Structure and its Types including all Steps involves in Data Structures:- Data Structure and algorithm Linear Data Structures and Non-Linear Data Structure on Stack Data Structure on Arrays Data Structure on Queue Data Structure on Linked List Data Structure on Tree Data Structure on Graphs Abstract Data Types Introduction to Algorithms Classifications of Algorithms Algorithm Analysis Algorithm Growth Function Array Operations Two dimensional Arrays Three Dimensional Arrays Multidimensional arrays Matrix operations Operations on linked lists Applications of linked lists Doubly linked lists Introductions to stacks Operations on stack Array based implementation of stack Queue Data Structures Operations on Queues Linked list based implementation of queues Application of Trees Binary Trees Types of Binary Trees Implementation of Binary Trees Binary Tree Traversal Preorder Post order In order Binary Search Tree Introduction to Sorting Analysis of Sorting Algorithms Bubble Sort Selection Sort Insertion Sort Shell Sort Heap Sort Merge Sort Quick Sort Applications of Graphs Matrix representation of Graphs Implementations of Graphs Breadth First Search Topological Sorting Subscribe for More https://www.youtube.com/channel/UCiV37YIYars6msmIQXopIeQ Find us on Facebook: https://web.facebook.com/Computer-Education-for-All-1484033978567298 Java Programming Complete Tutorial for Beginners to Advance | Complete Java Training for all https://youtu.be/gg2PG3TwLx4
Views: 631908 Computer Education For all
Updated video 2018: SPSS for Beginners - Introduction https://youtu.be/_zFBUfZEBWQ This video provides an introduction to SPSS/PASW. It shows how to navigate between Data View and Variable View, and shows how to modify properties of variables.
Views: 1573887 Research By Design
Decision tree represents decisions and decision Making. Root Node,Internal Node,Branch Node and leaf Node are the Parts of Decision tree Decision tree is also called Classification tree. Examples & Advantages for decision tree is explained. Data mining,text Mining,information Extraction,Machine Learning and Pattern Recognition are the fileds were decision tree is used. ID3,c4.5,CART,CHAID, MARS are some of the decision tree algorithms. when Decision tree is used for classification task, it is also called classification tree.
Views: 24730 IT Miner - Tutorials & Travel
Dr. Manishika Jain in this lecture explains the meaning of Sampling & Types of Sampling Research Methodology Population & Sample Systematic Sampling Cluster Sampling Non Probability Sampling Convenience Sampling Purposeful Sampling Extreme, Typical, Critical, or Deviant Case: Rare Intensity: Depicts interest strongly Maximum Variation: range of nationality, profession Homogeneous: similar sampling groups Stratified Purposeful: Across subcategories Mixed: Multistage which combines different sampling Sampling Politically Important Cases Purposeful Sampling Purposeful Random: If sample is larger than what can be handled & help to reduce sample size Opportunistic Sampling: Take advantage of new opportunity Confirming (support) and Disconfirming (against) Cases Theory Based or Operational Construct: interaction b/w human & environment Criterion: All above 6 feet tall Purposive: subset of large population – high level business Snowball Sample (Chain-Referral): picks sample analogous to accumulating snow Advantages of Sampling Increases validity of research Ability to generalize results to larger population Cuts the cost of data collection Allows speedy work with less effort Better organization Greater brevity Allows comprehensive and accurate data collection Reduces non sampling error. Sampling error is however added. Population & Sample @2:25 Sampling @6:30 Systematic Sampling @9:25 Cluster Sampling @ 11:22 Non Probability Sampling @13:10 Convenience Sampling @15:02 Purposeful Sampling @16:16 Advantages of Sampling @22:34 #Politically #Purposeful #Methodology #Systematic #Convenience #Probability #Cluster #Population #Research #Manishika #Examrace For IAS Psychology postal Course refer - http://www.examrace.com/IAS/IAS-FlexiPrep-Program/Postal-Courses/Examrace-IAS-Psychology-Series.htm For NET Paper 1 postal course visit - https://www.examrace.com/CBSE-UGC-NET/CBSE-UGC-NET-FlexiPrep-Program/Postal-Courses/Examrace-CBSE-UGC-NET-Paper-I-Series.htm types of sampling types of sampling pdf probability sampling types of sampling in hindi random sampling cluster sampling non probability sampling systematic sampling
Views: 388283 Examrace
Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 2: Linear regression http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/augc8F https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 43999 WekaMOOC
Whenever we look at a map, it is natural for us to organize, group, differentiate, and cluster what we see to help us make better sense of it. This session will explore the powerful Spatial Statistics techniques designed to do just that: Hot Spot Analysis and Cluster and Outlier Analysis. We will demonstrate how these techniques work and how they can be used to identify significant patterns in our data. We will explore the different questions that each tool can answer, best practices for running the tools, and strategies for interpreting and sharing results. This comprehensive introduction to cluster analysis will prepare you with the knowledge necessary to turn your spatial data into useful information for better decision making.
Views: 30525 Esri Events
** NLP Using Python: - https://www.edureka.co/python-natural-language-processing-course ** This Edureka video will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and much more along with a demo on each one of the topics. The following topics covered in this video : 1. The Evolution of Human Language 2. What is Text Mining? 3. What is Natural Language Processing? 4. Applications of NLP 5. NLP Components and Demo Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV --------------------------------------------------------------------------------------------------------- Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ --------------------------------------------------------------------------------------------------------- - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Natural Language Processing using Python Training focuses on step by step guide to NLP and Text Analytics with extensive hands-on using Python Programming Language. It has been packed up with a lot of real-life examples, where you can apply the learnt content to use. Features such as Semantic Analysis, Text Processing, Sentiment Analytics and Machine Learning have been discussed. This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the important concepts and techniques used in Natural Language Processing using Python Programming Language. You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases of NLP in python programming language to enhance your learning experience. -------------------------- Who Should go for this course ? Edureka’s NLP Training is a good fit for the below professionals: From a college student having exposure to programming to a technical architect/lead in an organisation Developers aspiring to be a ‘Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Text Mining Techniques 'Python' professionals who want to design automatic predictive models on text data "This is apt for everyone” --------------------------------- Why Learn Natural Language Processing or NLP? Natural Language Processing (or Text Analytics/Text Mining) applies analytic tools to learn from collections of text data, like social media, books, newspapers, emails, etc. The goal can be considered to be similar to humans learning by reading such material. However, using automated algorithms we can learn from massive amounts of text, very much more than a human can. It is bringing a new revolution by giving rise to chatbots and virtual assistants to help one system address queries of millions of users. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Human language, developed over thousands and thousands of years, has become a nuanced form of communication that carries a wealth of information that often transcends the words alone. NLP will become an important technology in bridging the gap between human communication and digital data. --------------------------------- For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 52475 edureka!
How to enter and analyze questionnaire (survey) data in SPSS is illustrated in this video. Lots more Questionnaire/Survey & SPSS Videos here: https://www.udemy.com/survey-data/?couponCode=SurveyLikertVideosYT Check out our next text, 'SPSS Cheat Sheet,' here: http://goo.gl/b8sRHa. Prime and ‘Unlimited’ members, get our text for free. (Only 4.99 otherwise, but likely to increase soon.) Survey data Survey data entry Questionnaire data entry Channel Description: https://www.youtube.com/user/statisticsinstructor For step by step help with statistics, with a focus on SPSS. Both descriptive and inferential statistics covered. For descriptive statistics, topics covered include: mean, median, and mode in spss, standard deviation and variance in spss, bar charts in spss, histograms in spss, bivariate scatterplots in spss, stem and leaf plots in spss, frequency distribution tables in spss, creating labels in spss, sorting variables in spss, inserting variables in spss, inserting rows in spss, and modifying default options in spss. For inferential statistics, topics covered include: t tests in spss, anova in spss, correlation in spss, regression in spss, chi square in spss, and MANOVA in spss. New videos regularly posted. Subscribe today! YouTube Channel: https://www.youtube.com/user/statisticsinstructor Video Transcript: In this video we'll take a look at how to enter questionnaire or survey data into SPSS and this is something that a lot of people have questions with so it's important to make sure when you're working with SPSS in particular when you're entering data from a survey that you know how to do. Let's go ahead and take a few moments to look at that. And here you see on the right-hand side of your screen I have a questionnaire, a very short sample questionnaire that I want to enter into SPSS so we're going to create a data file and in this questionnaire here I've made a few modifications. I've underlined some variable names here and I'll talk about that more in a minute and I also put numbers in parentheses to the right of these different names and I'll also explain that as well. Now normally when someone sees this survey we wouldn't have gender underlined for example nor would we have these numbers to the right of male and female. So that's just for us, to help better understand how to enter these data. So let's go ahead and get started here. In SPSS the first thing we need to do is every time we have a possible answer such as male or female we need to create a variable in SPSS that will hold those different answers. So our first variable needs to be gender and that's why that's underlined there just to assist us as we're doing this. So we want to make sure we're in the Variable View tab and then in the first row here under Name we want to type gender and then press ENTER and that creates the variable gender. Now notice here I have two options: male and female. So when people respond or circle or check here that they're male, I need to enter into SPSS some number to indicate that. So we always want to enter numbers whenever possible into SPSS because SPSS for the vast majority of analyses performs statistical analyses on numbers not on words. So I wouldn't want and enter male, female, and so forth. I want to enter one's, two's and so on. So notice here I just arbitrarily decided males get a 1 and females get a 2. It could have been the other way around but since male was the first name listed I went and gave that 1 and then for females I gave a 2. So what we want to do in our data file here is go head and go to Values, this column, click on the None cell, notice these three dots appear they're called an ellipsis, click on that and then our first value notice here 1 is male so Value of 1 and then type Label Male and then click Add. And then our second value of 2 is for females so go ahead and enter 2 for Value and then Female, click Add and then we're done with that you want to see both of them down here and that looks good so click OK. Now those labels are in here and I'll show you how that works when we enter some numbers in a minute. OK next we have ethnicity so I'm going to call this variable ethnicity. So go ahead and type that in press ENTER and then we're going to the same thing we're going to create value labels here so 1 is African-American, 2 is Asian-American, and so on. And I'll just do that very quickly so going to Values column, click on the ellipsis. For 1 we have African American, for 2 Asian American, 3 is Caucasian, and just so you can see that here 3 is Caucasian, 4 is Hispanic, and other is 5, so let's go ahead and finish that. Four is Hispanic, 5 is other, so let's go to do that 5 is other. OK and that's it for that variable. Now we do have it says please state I'll talk about that next that's important when they can enter text we have to handle that differently.
Views: 637494 Quantitative Specialists
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: 388946 Quantitative Specialists
Check out http://www.engineer4free.com for more free engineering tutorials and math lessons! Project Management Tutorial: Use forward and backward pass to determine project duration and critical path. Please support my work: PATREON | https://www.patreon.com/Engineer4Free Every dollar is seriously appreciated and enables me to continue making more tutorials
Views: 881723 Engineer4Free
In this video you will learn what are the differences between Supervised Learning & Unsupervised learning in the context of Machine Learning. Linear regression, Logistic regression, SVM, random forest are the supervised learning algorithms. For all videos and Study packs visit : http://analyticuniversity.com/ 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
Views: 57327 Analytics University
The main types of probability sampling methods are simple random sampling, stratified sampling, cluster sampling, multistage sampling, and systematic random sampling. The key benefit of probability sampling methods is that they guarantee that the sample chosen is representative of the population
Views: 151825 Manager Sahab
Understand what is Statistical Process Control (SPC) in Hindi. This is part 1 of the video, watch concluding part 2 , to cover rest of the content. SPC क्या है हिंदी में सीखे। ये पहला भाग है , प्रोग्राम को पूरा समझने के लिए इसका दूसरा भाग भी अवस्य देखे। 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: 227257 Quality HUB India
The FP-Growth Algorithm, proposed by Han, is an efficient and scalable method for mining the complete set of frequent patterns by pattern fragment growth, using an extended prefix-tree structure for storing compressed and crucial information about frequent patterns named frequent-pattern tree (FP-tree).
Views: 126571 StudyKorner
Methodology is the systematic, theoretical analysis of the methods applied to a field of study. A research method is a systematic plan for conducting research. Sociologists draw on a variety of both qualitative and quantitative research methods, including experiments, survey research, participant observation, and secondary data.
Views: 163381 Manager Sahab
http://www.ted.com With the drama and urgency of a sportscaster, statistics guru Hans Rosling uses an amazing new presentation tool, Gapminder, to present data that debunks several myths about world development. Rosling is professor of international health at Sweden's Karolinska Institute, and founder of Gapminder, a nonprofit that brings vital global data to life. (Recorded February 2006 in Monterey, CA.) TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes. TED stands for Technology, Entertainment, Design, and TEDTalks cover these topics as well as science, business, development and the arts. Closed captions and translated subtitles in a variety of languages are now available on TED.com, at http://www.ted.com/translate. Follow us on Twitter http://www.twitter.com/tednews Checkout our Facebook page for TED exclusives https://www.facebook.com/TED
Views: 2917802 TED
HPLC chromatography lecture - This lecture explains about the HPLC chromatography technique in a nutshell by Suman Bhattacharjee. HPLC is performed to separate organic and biological compounds using solid stationary phase. High Performance Liquid Chromatography (HPLC) is a form of column chromatography that pumps a sample mixture or analyte in a solvent which is known as the mobile phase at high pressure through a column with chromatographic packing material known as stationary phase. The sample is carried by a moving carrier gas stream of helium or nitrogen. HPLC has the ability to separate, and identify compounds that are present in any sample that can be dissolved in a liquid in trace concentrations as low as parts per trillion. Because of this versatility, HPLC is used in a variety of industrial and scientific applications, such as pharmaceutical, environmental, forensics, and chemicals. Sample retention time will vary depending on the interaction between the stationary phase, the molecules being analyzed, and the solvent, or solvents used. As the sample passes through the column it interacts between the two phases at different rate, primarily due to different polarities in the analytes. Analytes that have the least amount of interaction with the stationary phase or the most amount of interaction with the mobile phase will exit the column faster. This lecture explains the following things about Hplc chromatography - 1. Hplc chromatography principle 2. Hplc chromatography instrumentation 3. Hplc chromatography types High-Performance Liquid Chromatography - Other HPLC Types Ultra High Performance Liquid Chromatography (uHPLC): Where standard HPLC typically uses column particles with sizes from 3 to 5µm and pressures of around 400 bar, uHPLC use specially designed columns with particles down to 1.7µm in size, at pressures in excess of 1000 bar. The main advantage of an uHPLC is speed. These systems are faster, more sensitive, and rely on smaller volumes of organic solvents than standard HPLC, resulting in the ability to run more samples in less time. Article source: http://hiq.linde-gas.com/en/analytical_methods/liquid_chromatography/high_performance_liquid_chromatography.html For more information, log on to- http://www.shomusbiology.com/ Get Shomu's Biology DVD set here- http://www.shomusbiology.com/dvd-store/ Download the study materials here- http://shomusbiology.com/bio-materials.html Remember Shomu’s Biology is created to spread the knowledge of life science and biology by sharing all this free biology lectures video and animation presented by Suman Bhattacharjee in YouTube. All these tutorials are brought to you for free. Please subscribe to our channel so that we can grow together. You can check for any of the following services from Shomu’s Biology- Buy Shomu’s Biology lecture DVD set- www.shomusbiology.com/dvd-store Shomu’s Biology assignment services – www.shomusbiology.com/assignment -help Join Online coaching for CSIR NET exam – www.shomusbiology.com/net-coaching We are social. Find us on different sites here- Our Website – www.shomusbiology.com Facebook page- https://www.facebook.com/ShomusBiology/ Twitter - https://twitter.com/shomusbiology SlideShare- www.slideshare.net/shomusbiology Google plus- https://plus.google.com/113648584982732129198 Thank you for watching HPLC lecture
Views: 568963 Shomu's Biology
📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 34679 5 Minutes Engineering
Job Roles For DATA ENTRY OPERATOR : Know more about job roles and responsibility in DATA ENTRY . Coming to DATA ENTRY OPERATOR opportunities for freshers in India,Visit http://www.freshersworld.com?src=Youtube for detailed information,Job Opportunities,Education details and Career growth of DATA ENTRY OPERATOR. No matter what your educational background is, data entry operator jobs are available for all fresh candidates. People usually do not seek this position thinking that this is a low-level job. As a matter of fact, it is not lower than any other entry level position in corporate world. The main job of a data entry operator is to update, add and maintain data in a system or managing databases. The data entry operator is expected to insert or add data related to the company (both text and numerical) from a source file provided by the company. The candidate should also verify and sort the information as per given instruction. Other operational work includes generating routine reports and filing documents related to their work. Mostly, freshers with bachelor degree in arts and science are sought for this position. Even diploma candidates are opted for this position by many companies. Usually, candidates with professional degree, master degree or doctorate would not be sought for this position. The basic requirements are a) Knowledge and savvy in computer operation b) Expertise in MS-Office and other related software c) High typing speed – minimum market requirement is 40 WPM with 95% accuracy. d) Basic communication skills in English Usually, the candidates with good computer skill would be sought without regards to their educational background. Rotational shifts are rare and both male and female are sought. This job is also available in working-from-home option in some companies. There are short terms courses with certification for data entry offered by many institutions. Though it is not an essential certification, it would give a competitive edge over other candidates. Those who have working knowledge of Tally are sought for accountancy related data entry with a slightly higher pay. The same goes for those with commerce related educational background. With an increase in growth of BPO industry in India, there is a very high demand for data entry specialists. With one to three years experience in data entry, one can apply for jobs related to data management, document imaging, data mining, data processing and other related fields. If you want to grow in the same field, with three or more years of experience in data entry job, you can apply for senior data entry position or data analyzer positions. With more experience, you can apply for managerial positions like transaction processor, document processor and many others. Your scope is not restricted to back office operations. Candidates with a few years of experience in data entry can take up operational related jobs in KPO and customer service department. Yet, they would be considered as fresher in the new department. This job is for those who do not have a fancy degree and yet, want to take up corporate job. With this job, entry into corporate world becomes easy for all kinds of candidates. The academic excellence is not an important qualification for this job. Thus, candidates with backlog and those with moderate communication skill can apply for this position if, their typing skill is excellent. For more jobs & career information and daily job alerts, subscribe to our channel and support us. You can also install our Mobile app for govt jobs for getting regular notifications on your mobile. Freshersworld.com is the No.1 job portal for freshers jobs in India. Check Out website for more Jobs & Careers. http://www.freshersworld.com?src=Youtube - - ***Disclaimer: This is just a career guidance video for fresher candidates. The name, logo and properties mentioned in the video are proprietary property of the respective companies. The career and job information mentioned are an indicative generalised information. In no way Freshersworld.com, indulges into direct or indirect recruitment process of the respective companies.
Views: 235407 Freshersworld.com Jobs & Careers
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: 2949433 Simply Explained - Savjee
ALL DATA MINING ALGORITHM videos are on below link : _____________________________________________________________ https://www.youtube.com/watch?v=JZepOmvB514&list=PLNmFIlsXKJMmekmO4Gh6ZBZUVZp24ltEr ********************************************************************* apriori algorithm simple example : http://britsol.blogspot.in/2017/08/apriori-algorithm-example.html ____________________________________________________________ book name: techmax publications datawarehousing and mining by arti deshpande n pallavi halarnkar
Views: 11836 fun 2 code
short introduction on Association Rule with definition & Example, are explained. Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database. Parts of Association rule is explained with 2 measurements support and confidence. types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples. Names of Association rule algorithm and fields where association rule is used is also mentioned.
Views: 96221 IT Miner - Tutorials & Travel
Week 2 assignment for MooreFMIS7003 course at NCU. Prepared by FahmeenaOdetta Moore.
Views: 69 FahmeenaOdetta Moore
understanding how the input flows to the output in back propagation neural network with the calculation of values in the network. the example is taken from below link refer this https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/ for full example
Views: 164203 Naveen Kumar
In this video we describe the DTW algorithm, which is used to measure the distance between two time series. It was originally proposed in 1978 by Sakoe and Chiba for speech recognition, and it has been used up to today for time series analysis. DTW is one of the most used measure of the similarity between two time series, and computes the optimal global alignment between two time series, exploiting temporal distortions between them. Source code of graphs available at https://github.com/tkorting/youtube/blob/master/how-dtw-works.m The presentation was created using as references the following scientific papers: 1. Sakoe, H., Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoustic Speech and Signal Processing, v26, pp. 43-49. 2. Souza, C.F.S., Pantoja, C.E.P, Souza, F.C.M. Verificação de assinaturas offline utilizando Dynamic Time Warping. Proceedings of IX Brazilian Congress on Neural Networks, v1, pp. 25-28. 2009. 3. Mueen, A., Keogh. E. Extracting Optimal Performance from Dynamic Time Warping. available at: http://www.cs.unm.edu/~mueen/DTW.pdf
Views: 42727 Thales Sehn Körting
Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 1023477 David Langer
NOTE: Formula "pij = ui+vi-Cij" according to this formula the optimal values should be Zero or less than Zero which mean Zero or negative values, and in this formula if we did not reach the optimality then we should select the maximum positive value to proceed further. If you use this Cij-(u1+vj) formula then the values should be zero or positive value to reach the optimality, and in this formula if we did not reach the optimality then we should select the maximum negative value to proceed further. We can apply either any any one of the formula to find out the optimality. So both the formulas are doing same thing only but the values of sign (- +) will be differ. Here is the video about Transportation problem in Modi method-U V method using north west corner method, optimum solution in operation research, with sample problem in simple manner. Hope this will help you to get the subject knowledge at the end. Thanks and All the best. To watch more tutorials pls visit: www.youtube.com/c/kauserwise * Financial Accounts * Corporate accounts * Cost and Management accounts * Operations Research * Statistics ▓▓▓▓░░░░───CONTRIBUTION ───░░░▓▓▓▓ If you like this video and wish to support this kauserwise channel, please contribute via, * Paytm a/c : 6383617203 * Western Union / MoneyGram [ Name: Kauser, Country: India & Email: [email protected] ] [Every contribution is helpful] Thanks & All the Best!!! ───────────────────────────
Views: 2282647 Kauser Wise
( TensorFlow Training - https://www.edureka.co/ai-deep-learning-with-tensorflow ) This Edureka "Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand the basics of Neural Networks and how to use it for deep learning. It explains Single layer and Multi layer Perceptron in detail. Below are the topics covered in this tutorial: 1. Why Neural Networks? 2. Motivation Behind Neural Networks 3. What is Neural Network? 4. Single Layer Percpetron 5. Multi Layer Perceptron 6. Use-Case 7. Applications of Neural Networks Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course. - - - - - - - - - - - - - - Who should go for this course? The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. Business Analysts who want to understand Deep Learning (ML) Techniques 4. Information Architects who want to gain expertise in Predictive Analytics 5. Professionals who want to captivate and analyze Big Data 6. Analysts wanting to understand Data Science methodologies However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio. - - - - - - - - - - - - - - Why Learn Deep Learning With TensorFlow? TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning. Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world. For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 79282 edureka!
k nearest neighbour algorithm in data mining belongs to the supervised learning domain and finds intense application in pattern recognition, data mining and intrusion detection https://www.geeksforgeeks.org/k-nearest-neighbours/ BOOK NAME : techmax publications datawarehousing and mining by arti deshpande n pallavi halarnkar $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ ALL DATA MINING ALGORITHM VIDEOS ARE BELOW : https://www.youtube.com/watch?v=JZepOmvB514&list=PLNmFIlsXKJMmekmO4Gh6ZBZUVZp24ltEr $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ PDF OF KNN ALGORITHM EXAMPLE IS AT BELOW LINK https://britsol.blogspot.in/2017/12/knn-k-nearest-neighbor-algorithm.html $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ EXAMPLES OF APRIORI ALGORITHM ARE AT BELOW LINK http://britsol.blogspot.in/2017/08/apriori-algorithm-example.html $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ DECISION TREE BASIC EXAMPLE PDF AND VIDEO ARE BELOW : VIDEO : https://www.youtube.com/watch?v=ajG5Yq1myMg&list=PLNmFIlsXKJMmekmO4Gh6ZBZUVZp24ltEr&index=2 PDF : http://britsol.blogspot.in/2017/10/decision-tree-algorithm-pdf.html $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
Views: 3542 fun 2 code
The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that is based on natural selection, the process that drives biological evolution. The genetic algorithm repeatedly modifies a population of individual solutions. Genetic algorithm with flow chart diagram and pseudo code with solved example Some topics-- Metrix chain multiplication DAA in hindi https://youtu.be/9LHQRnmW_OE Perceptron learning Algorithm In hindi https://youtu.be/x3joYu5VI38 Neural network in hindi playlist https://www.youtube.com/playlist?list=PLqLEnFoF-ykdiMeuGRCy8yoWpuOdDz-jy Computer graphics in hindi dda algo https://www.youtube.com/playlist?list=PLqLEnFoF-ykeSyrdq7oag5Xfs79nUXSce Python in hindi playlist https://www.youtube.com/playlist?list=PLqLEnFoF-ykfWg6g1JY7ixqO86o1wPzZu Rest api using java https://www.youtube.com/playlist?list=PLqLEnFoF-ykdZf0kcJfWX548yrItxtAnv Web services by piyush sir https://www.youtube.com/watch?v=WihA-ZZ51l8&list=PLqLEnFoF-ykdZf0kcJfWX548yrItxtAnv Automata in hindi https://www.youtube.com/playlist?list=PLqLEnFoF-ykcMCOVWYzv2EaNjsh7KI-FH Django full playlist in hindi https://www.youtube.com/playlist?list=PLqLEnFoF-ykcD3-Gkppoq4FReU2q1DMOW Optimal binary search tree https://www.youtube.com/playlist?list=PLqLEnFoF-ykdpNwj0dxZIqNS-7wu2ejid Loops in java https://www.youtube.com/playlist?list=PLqLEnFoF-ykeNQy6UkSh6-S1UBhDoDN6L jsp tutorial in Hindi https://www.youtube.com/playlist?list=PLqLEnFoF-ykd4v-yaf4H1f6h6exbwfqMI String handling in java https://www.youtube.com/playlist?list=PLqLEnFoF-ykegkqwCrPcKoe2Akj4_SOGk Spring lecture https://www.youtube.com/playlist?list=PLqLEnFoF-ykfNI2opX4ieZksm1RKce-Mw Jdbc lactures https://www.youtube.com/playlist?list=PLqLEnFoF-ykfNI2opX4ieZksm1RKce-Mw Oracle lactures https://www.youtube.com/playlist?list=PLqLEnFoF-ykebhrlEZzeSpyDbka_sGAml Android lactures https://www.youtube.com/playlist?list=PLqLEnFoF-ykdHUoBMd1LJZKD7EJiCmX-N Compiler design in hindi https://www.youtube.com/playlist?list=PLqLEnFoF-ykepOgKaNDiO7JgwXMPHsQLk Automatic review request https://www.youtube.com/watch?v=l2b9DiAIySY&list=PLqLEnFoF-ykcQ4FHNiJ7RPr9otPM5gWmV&index=9 Html tutorials https://www.youtube.com/playlist?list=PLqLEnFoF-ykdEpnKfWUJlCXS_f17sL9Dt Php lactures in hindi https://www.youtube.com/playlist?list=PLqLEnFoF-ykfN35jmbTJ39qPeOLhXAN4P Operating system in hindi https://www.youtube.com/playlist?list=PLqLEnFoF-ykezceIJKIEEuPX-2fI31HkJ Mobile computing in hindi https://www.youtube.com/playlist?list=PLqLEnFoF-ykfwbQgfCYVtZxzyQzgfXMOB Hebb learning algorithm https://youtu.be/n4QxeET2hTo auto assosiative algorithm https://youtu.be/2nLp-2z6wG4 genetic algorithm applications genetic algorithm example genetic algorithm tutorial genetic algorithm pdf genetic algorithm python genetic algorithm matlab genetic algorithm steps genetic algorithm machine learning genetic algorithm example genetic algorithm youtube genetic algorithm in artificial intelligence genetic algorithm in soft computing genetic algorithm step by step example max one problem genetic algorithm introduction to genetic algorithm genetic algorithm in artificial intelligence examples genetic algorithm in neural network ppt genetic algorithm neural network python genetic algorithm neural network matlab training feedforward neural networks using genetic algorithms keras genetic algorithm neural network fitness function genetic algorithm neural network github pytorch genetic algorithm genetic algorithm tutorial genetic algorithm example in artificial intelligence genetic algorithm mit genetic algorithm steps genetic algorithm example code in r coding a genetic algorithm genetic algorithm matlab genetic algorithm in business genetic algorithm in artificial algorithm genetic algorithm artificial intelligent
Views: 3066 Muo sigma classes
In this video we will derive the back-propagation algorithm as is used for neural networks. I use the sigmoid transfer function because it is the most common, but the derivation is the same, and easily extensible. Helpful diagram: https://www.dropbox.com/s/vj0qg9jlmy3mwof/Explanation_1.pdf?dl=0 This particular video goes from the derivative of the sigmoid itself to the delta for the output layer The presentation can be found here: https://www.dropbox.com/s/z5bz0cw0boxxon1/BackPropagation.pdf?dl=0
Views: 284316 Ryan Harris
This lecture provides the introductory concepts of Frequent pattern mining in transnational databases.
Views: 66619 StudyKorner
In this video, Dr Greg Martin provides an introduction to research methods, methedology and study design. Specifically he takes a look at qualitative and quantitative research methods including case control studies, cohort studies, observational research etc. Global health (and public health) is truly multidisciplinary and leans on epidemiology, health economics, health policy, statistics, ethics, demography.... the list goes on and on. This YouTube channel is here to provide you with some teaching and information on these topics. I've also posted some videos on how to find work in the global health space and how to raise money or get a grant for your projects. Please feel free to leave comments and questions - I'll respond to all of them (we'll, I'll try to at least). Feel free to make suggestions as to future content for the channel. SUPPORT: —————- This channel has a crowd-funding campaign (please support if you find these videos useful). Here is the link: http://bit.ly/GH_support OTHER USEFUL LINKS: ———————— Channel page: http://bit.ly/GH_channel Subscribe: http://bit.ly/GH_subscribe Google+: http://bit.ly/GH_Google Twitter: @drgregmartin Facebook: http://bit.ly/GH_facebook HERE ARE SOME PLAYLISTS ——————————————- Finding work in Global Health: http://bit.ly/GH_working Epidemiology: http://bit.ly/GH_epi Global Health Ethics: http://bit.ly/GH_ethics Global Health Facts: http://bit.ly/GH_facts WANT CAREER ADVICE? ———————————— You can book time with Dr Greg Martin via Google Helpouts to get advice about finding work in the global health space. Here is the link: http://bit.ly/GH_career -~-~~-~~~-~~-~- Please watch: "Know how interpret an epidemic curve?" https://www.youtube.com/watch?v=7SM4PN7Yg1s -~-~~-~~~-~~-~-
Views: 336521 Global Health with Greg Martin
Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class. Playlist on Linear Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongstreet
Views: 798075 statisticsfun
Data Warehouse and Mining For more: http://www.anuradhabhatia.com
Views: 119508 Anuradha Bhatia