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Data Mining Sources PowerPoint Presentation Slides
 
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Every organization needs to adapt to the ever-changing business environment. Sensing this need, we have come up with these content-ready change management PowerPoint presentation slides. These change management PPT templates will help you deal with any kind of an organizational change. Be it with people, goals or processes. The business solutions incorporated here will help you identify the organizational structure, create vision for change, implement strategies, identify resistance and risk, manage cost of change, get feedback and evaluation, and much more. With the help of various change management tools and techniques illustrated in this presentation design, you can achieve the desired business outcomes. This business transition PowerPoint design also covers certain related topics such as change model, transformation strategy, change readiness, change control, project management and business process. By implementing the change control methods mentioned in the presentation, you will be able to have a smooth transition in an organization. So, without waiting much, download our extensively researched change management framework presentation. With our Change Management Presentation slides, understand the need for change and plan to go through it without any hassles.
Data Mining with Weka (1.1: Introduction)
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 1: Introduction http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 125980 WekaMOOC
Why you should study data mining
 
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Why you should study data mining? A talk given at UCLA for communications work.
Views: 148 Abhi D
Data Mining with Weka (1.3: Exploring datasets)
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 3: Exploring datasets http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 80613 WekaMOOC
Lecture 11 - Overfitting
 
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Overfitting - Fitting the data too well; fitting the noise. Deterministic noise versus stochastic noise. Lecture 11 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/ This lecture was recorded on May 8, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Views: 80280 caltech
Intelligent Heart Disease Prediction System Using Data Mining Techniques
 
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Presentation Data Mining & Decision-making: Case of Amazon.com
 
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Week 2 assignment for MooreFMIS7003 course at NCU. Prepared by FahmeenaOdetta Moore.
Data Mining with Weka (1.6: Visualizing your data)
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 6: Visualizing your data http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 68671 WekaMOOC
Data pre processing – 1 Summarization and Cleaning Methods
 
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Project Name: e-Content generation and delivery management for student –Centric learning Project Investigator:Prof. D V L N Somayajulu
Views: 5778 Vidya-mitra
More Data Mining with Weka (1.6: Working with big data)
 
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More Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 6: Working with big data http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/Le602g https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 10279 WekaMOOC
What is machine learning and how to learn it ?
 
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http://www.LearnCodeOnline.in Machine learning is just to give trained data to a program and get better result for complex problems. It is very close to data mining. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with: The heavily hyped, self-driving Google car? The essence of machine learning. Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life. Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation. Fraud detection? One of the more obvious, important uses in our world today. fb: https://www.facebook.com/HiteshChoudharyPage homepage: http://www.hiteshChoudhary.com
Views: 779813 Hitesh Choudhary
Data Mining with Weka (4.1: Classification boundaries)
 
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Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 1: Classification boundaries 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: 26076 WekaMOOC
Data Science In 5 Minutes | Data Science For Beginners | What Is Data Science? | Simplilearn
 
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This Data Science tutorial video will give you an idea on the life of a Data Scientist, steps involved in Data science project, roles & salary offered to a Data Scientist. Data is everywhere. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. Data Science is basically dealing with unstructured and structured data. Data Science is a field that comprises of everything that is related to data cleansing, preparation, and analysis. In simple terms, it is the umbrella of techniques used when trying to extract insights and information from data. Now, let us get started and understand what is Data Science all about. Below topics are explained in this Data Science tutorial: 1. Life of a Data Scientist 2. Steps in Data Science project - Understanding the business problem - Data acquisition - Data preparation - Exploratory data analysis - Data modeling - Visualization and communication - Deploy & maintenance 3. Roles offered to a Data Scientist 4. Salary of a Data Scientist To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Read the full article here: https://www.simplilearn.com/career-in-data-science-ultimate-guide-article?utm_campaign=What-is-Data-Science-bTTxei-S1WI&utm_medium=Tutorials&utm_source=youtube Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6 #DataScienceWithPython #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course. Why learn Data Science? Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data. You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to: 1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics. Install the required Python environment and other auxiliary tools and libraries 2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions 3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave 4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package 5. Gain expertise in machine learning using the Scikit-Learn package The Data Science with python is recommended for: 1. Analytics professionals who want to work with Python 2. Software professionals looking to get into the field of analytics 3. IT professionals interested in pursuing a career in analytics 4. Graduates looking to build a career in analytics and data science 5. Experienced professionals who would like to harness data science in their fields Learn more at: https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training?utm_campaign=What-is-Data-Science-X3paOmcrTjQ&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 192745 Simplilearn
Data Structures and Algorithms Complete Tutorial Computer Education for All
 
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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
Advanced Data Mining with Weka (1.2: Linear regression with lags)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 2: Linear regression with lags http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/JyCK84 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 8404 WekaMOOC
More Data Mining with Weka (3.4: Learning association rules)
 
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More Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 4: Learning association rules http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/nK6fTv https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 13391 WekaMOOC
How to Create an Awesome Slide Presentation (for Keynote or Powerpoint)
 
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In this episode of SPI TV, I'm going to show you how to create an awesome slide deck for your next presentation, one that captivates your audience and supports your talk, not bores people to death and puts people to sleep. I've performed dozens of presentations myself, and I take great pride in how I approach my slide deck. I'm always getting complimented on my slides, and I want the same to happen to you. Creating great slides doesn't have to be difficult, and with a few simple rules and some guidelines to follow, you'll stand out as a top presenter the next time you're on stage or presenting in front of a group. Please note that I do talk briefly about why this is important, however if you'd like -~-~~-~~~-~~-~- Building an email list? Watch my latest video: "How to Get More Email Subscribers (17 Lead Magnet Ideas)": https://www.youtube.com/watch?v=6te1AlLUA10 -~-~~-~~~-~~-~-
Views: 982336 Pat Flynn
Naive Bayes Classifier | Naive Bayes Algorithm | Naive Bayes Classifier With Example | Simplilearn
 
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This Naive Bayes Classifier tutorial video will introduce you to the basic concepts of Naive Bayes classifier, what is Naive Bayes and Bayes theorem, conditional probability concepts used in Bayes theorem, where is Naive Bayes classifier used, how Naive Bayes algorithm works with solved examples, advantages of Naive Bayes. By the end of this video, you will also implement Naive Bayes algorithm for text classification in Python. The topics covered in this Naive Bayes video are as follows: 1. What is Naive Bayes? ( 01:06 ) 2. Naive Bayes and Machine Learning ( 05:45 ) 3. Why do we need Naive Bayes? ( 05:46 ) 4. Understanding Naive Bayes Classifier ( 06:30 ) 5. Advantages of Naive Bayes Classifier ( 20:17 ) 6. Demo - Text Classification using Naive Bayes ( 22:36 ) To learn more about Machine Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the Slides here: https://goo.gl/Cw9wqy #NaiveBayes #MachineLearningAlgorithms #DataScienceCourse #DataScience #SimplilearnMachineLearning - - - - - - - - Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer Why learn Machine Learning? Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems The Machine Learning Course is recommended for: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Naive-Bayes-Classifier-l3dZ6ZNFjo0&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 37894 Simplilearn
Final Year Projects | Horizontal Aggregations in SQL to Prepare Data Sets for Data Mining Analysis
 
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Final Year Projects | Horizontal Aggregations in SQL to Prepare Data Sets for Data Mining Analysis More Details: Visit http://clickmyproject.com/horizontal-aggregations-in-sql-to-prepare-data-sets-for-data-mining-analysis-p-124.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-778-1155 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us : [email protected]
Views: 2596 Clickmyproject
Gene Mining from Metagenomic Data
 
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Lecture by Ms. Deepti Davla during 3rd short course on Metagenomics: Role of NGS and Bioinformatics
Views: 641 omeresearchfacility
Data Science Methodology 101 - Data Preparation Concepts
 
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Enroll in the course for free at: https://bigdatauniversity.com/courses/data-science-methodology-2/ Data Science Methodology Grab you lab coat, beakers, and pocket calculator…wait what? wrong path! Fast forward and get in line with emerging data science methodologies that are in use and are making waves or rather predicting and determining which wave is coming and which one has just passed. Connect with Big Data University: https://www.facebook.com/bigdatauniversity https://twitter.com/bigdatau https://www.linkedin.com/groups/4060416/profile ABOUT THIS COURSE •This course is free. •It is self-paced. •It can be taken at any time. •It can be audited as many times as you wish. Learn the major steps involved in tackling a data science problem. Learn the major steps involved in practicing data science, with interesting real-world examples at each step: from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment. https://bigdatauniversity.com/courses/data-science-methodology-2/
Views: 7043 Cognitive Class
SPSS Questionnaire/Survey Data Entry - Part 1
 
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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: 564111 Quantitative Specialists
Data Mining with Weka (3.4: Decision trees)
 
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Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 4: Decision trees http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/1LRgAI https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 70323 WekaMOOC
Advanced Data Mining with Weka (4.6: Application: Image classification)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 6: Application: Image classification http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/msswhT https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 8265 WekaMOOC
Mod-01 Lec-04 Clustering vs. Classification
 
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Pattern Recognition by Prof. C.A. Murthy & Prof. Sukhendu Das,Department of Computer Science and Engineering,IIT Madras.For more details on NPTEL visit http://nptel.ac.in
Views: 21193 nptelhrd
KNN Algorithm - How KNN Algorithm Works With Example | Data Science For Beginners | Simplilearn
 
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This KNN Algorithm tutorial (K-Nearest Neighbor Classification Algorithm tutorial) will help you understand what is KNN, why do we need KNN, how do we choose the factor 'K', when do we use KNN, how does KNN algorithm work and you will also see a use case demo showing how to predict whether a person will have diabetes or not using KNN algorithm. KNN algorithm can be applied to both classification and regression problems. Apparently, within the Data Science industry, it's more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. Now lets deep dive into this video to understand what is KNN algorithm and how does it actually works. Below topics are explained in this K-Nearest Neighbor Classification Algorithm (KNN Algorithm) tutorial: 1. Why do we need KNN? 2. What is KNN? 3. How do we choose the factor 'K'? 4. When do we use KNN? 5. How does KNN algorithm work? 6. Use case - Predict whether a person will have diabetes or not To learn more about Machine Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the slides here: https://goo.gl/XP6xcp Watch more videos on Machine Learning: https://www.youtube.com/watch?v=7JhjINPwfYQ&list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy #MachineLearningAlgorithms #Datasciencecourse #datascience #SimplilearnMachineLearning #MachineLearningCourse Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer Why learn Machine Learning? Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems The Machine Learning Course is recommended for: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=What-is-Machine-Learning-7JhjINPwfYQ&utm_medium=Tutorials&utm_source=youtube For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 47905 Simplilearn
mod01lec01
 
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Views: 38286 Data Mining - IITKGP
FDA Director of Data Mining, Skip Francis' Keynote on Complex Data Analytics
 
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Skip Francis, MD keynote speech on opportunity and challenges for use of mobile health and social media data for analytics. Slides available at: http://go.umd.edu/mobilesocialanalytics
Advanced Data Mining with Weka (2.1: Incremental classifiers in Weka)
 
05:48
Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 1: Incremental classifiers in Weka http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3259 WekaMOOC
Advanced Data Mining with Weka (1.1: Introduction)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 1: Introduction http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/JyCK84 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 7281 WekaMOOC
Lecture 2. Tracking data preparation, visualization & descriptive stats, John Fieberg
 
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Video from the Short Course on Analyzing Animal Tracking data at the North Carolina Museum of Natural Sciences, May, 2018. This workshop shows how to use Movebank and the EnvData tool to annotate animal tracks with environmental data and build resource selection functions. This workshop was sponsored by the National Science Foundation (NSF). Presentation slides, R Code, and example data are available here: https://movebankworkshopraleighnc.netlify.com/
Views: 264 Movebank
How to introduce yourself in a job interview
 
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Have you ever wondered that why despite of having listed all the details about yourself in the resume, the interviewer still likes to start the session by asking you “Tell me something about yourself” The reason for this is that people like listening to information been presented to them in the form of verbal expression in contrast to referring to a printed document. Yes this simply means that they directly like to hear from the horses’s mouth that how would the candidates like to narrate the story of their life, in their own words. “Tell us something about yourself” doesn’t means that you go right back to your childhood memories and share tales of your first love or your favorite ROCK band or your obsession with Harry Potter; rather think about the awards, accolades or experiences you’ve had in your life. Consider the themes, personality traits and skills that come across through these experiences and introspect that which of these are the most important to get across through recruiters. Through personal introduction, the interviewer is giving you a chance to highlight all the key features of your personal and professional life, the top experiences you’ve had so far and the most influential skills you possess. Follow the points mentioned in the video, the next time you face a job interview and enhance the chances of you getting selected!!
Views: 6258806 Ankit Kalonia
Final Year Projects | Enabling Multilevel Trust in Privacy Preserving Data Mining
 
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Final Year Projects | Enabling Multilevel Trust in Privacy Preserving Data Mining More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 1447 Clickmyproject
Lecture - 34 Data Mining and Knowledge Discovery
 
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Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 134568 nptelhrd
Data Science Tutorial | Data Science for Beginners | Data Science with Python Tutorial | Simplilearn
 
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This Data Science Tutorial will help you understand what is Data Science, who is a Data Scientist, what does a Data Scientist do and also how Python is used for Data Science. Data science is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining. This Data Science tutorial will help you establish your skills at analytical techniques using Python. With this Data Science video, you’ll learn the essential concepts of Data Science with Python programming and also understand how data acquisition, data preparation, data mining, model building & testing, data visualization is done. This Data Science tutorial is ideal for beginners who aspire to become a Data Scientist. This Data Science tutorial will cover the following topics: 1. What is Data Science? ( 00:43 ) 2. Who is a Data Scientist? ( 02:02 ) 3. What does a Data Scientist do? ( 02:25 ) To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the slides here: https://goo.gl/V4Zn8i Read the full article here: https://www.simplilearn.com/career-in-data-science-ultimate-guide-article?utm_campaign=What-is-Data-Science-bTTxei-S1WI&utm_medium=Tutorials&utm_source=youtube Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6 #DataScienceWithPython #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course. Why learn Data Science? Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. A data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data. You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to: 1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics. Install the required Python environment and other auxiliary tools and libraries 2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions 3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave 4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package 5. Gain expertise in machine learning using the Scikit-Learn package The Data Science with python is recommended for: 1. Analytics professionals who want to work with Python 2. Software professionals looking to get into the field of analytics 3. IT professionals interested in pursuing a career in analytics 4. Graduates looking to build a career in analytics and data science 5. Experienced professionals who would like to harness data science in their fields Learn more at: https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training?utm_campaign=What-is-Data-Science-bTTxei-Data-Sciene-Tutorial-jNeUBWrrRsQ&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 36400 Simplilearn
Support Vector Machine (SVM) - Fun and Easy Machine Learning
 
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Support Vector Machine (SVM) - Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS COURSE - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML ►MACHINE LEARNING COURSES -http://augmentedstartups.info/machine-learning-courses ------------------------------------------------------------------------ A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. To understand SVM’s a bit better, Lets first take a look at why they are called support vector machines. So say we got some sample data over here of features that classify whether a observed picture is a dog or a cat, so we can for example look at snout length or and ear geometry if we assume that dogs generally have longer snouts and cat have much more pointy ear shapes. So how do we decide where to draw our decision boundary? Well we can draw it over here or here or like this. Any of these would be fine, but what would be the best? If we do not have the optimal decision boundary we could incorrectly mis-classify a dog with a cat. So if we draw an arbitrary separation line and we use intuition to draw it somewhere between this data point for the dog class and this data point of the cat class. These points are known as support Vectors – Which are defined as data points that the margin pushes up against or points that are closest to the opposing class. So the algorithm basically implies that only support vector are important whereas other training examples are ‘ignorable’. An example of this is so that if you have our case of a dog that looks like a cat or cat that is groomed like a dog, we want our classifier to look at these extremes and set our margins based on these support vectors. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 176041 Augmented Startups
ASEE MidAtlantic Conference Talk on Educational Data Mining
 
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Washington D.C. ASEE Conference giving talk
Statistical Process Control (SPC) in Hindi – (Part 1). SPC  हिंदी में सीखे।
 
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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: 208943 Quality HUB India
001 Statistics - Measures of Central Tendency - Arithmetic Mean
 
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This video covers calculation of Arithmetic mean ( from the Chapter Measures of Central Tendency ). Calculation of Arithmetic mean ( AM ) for ungrouped data and discrete data has been explained. The short cut method for discrete data has also been explained. Calculator trick to calculate AM has been explained. This video ( Statistics series ) is not class specific, I have tried cover all the details hence this lecture might be helpful for but not limited to - class 11 ( Statistics ), CA-CPT, CMA( foundation ), CS-Foundation, B.Com( H and P ), BBA, and various other competitive exams. If you liked the video please give it a thumbs up ( press the LIKE button ) and SUBSCRIBE to my channel. Thank You !! All the best :-)
Views: 467711 studyezee
Data Mining with Weka (4.4: Logistic regression)
 
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Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 4: Logistic 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: 32820 WekaMOOC
A Systematic Review on Educational Data Mining
 
10:45
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/clickmyproject Mail Us: [email protected]
Views: 61 Clickmyproject
Data Analysis in SPSS Made Easy
 
14:06
Use simple data analysis techniques in SPSS to analyze survey questions.
Views: 835518 Claus Ebster
Data Mining Final Project - Crime Aware - Demo
 
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This is the demo video for crime aware
Views: 460 kuma Dm
C2090-013  IBM SPSS Modeler Data Mining for Business Partners v2 Latest exam dumps
 
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Please visit at www.brainitworks.com or email us at [email protected] • Complete set of 50 to 350 total numbers of Questions & Answers Set along with accurate explanation. PPT in PDF format will be given, which may be get printed to test your knowledge before going for the real exam. • Questions & Answers set shall be given for practice similar like, which you will getting in real time examination. • IBM recommends combining education courses and hands-on experience to prepare for your certification exam as questions will test your ability to apply the knowledge you have gained in training. • Three Resume samples will be given for fresher, Mid-experience & Advance level Experience IBM aspirants. • 60 Days Free Updates • 10 years in the business, more than 414952 of happy customers. • Amazing 99.6% exam pass rate. Join our success! Awesome rate of Success. Trust us for best results, at the best price. IT Certifications made easy with Accurate & Update Questions. Expand your Qualification with our Self-Paced User-Friendly Exam. Prepare your certification exams with Certification Questions & Answers verified by experienced professionals! We make your certification journey easier as we provide you with learning materials to help you to pass your exams from the first try. Professionally researched by Certified Trainers, our preparation materials contribute to industry's highest 99.6% pass rate among our customers. Just like all our exams.
Views: 4 Andrea Jones
Lecture 4 Duality Part 2
 
22:58
Lecture 4 builds on the topics of duality and accruals seen in lectures 1-3. This takes a learning by doing approach so in this video so I am recording transactions, then preparing a reconciliation of cash, a profit and loss account and a statement of financial position. Apologies that the start says now lets look at slide 2, this video was prepared for a client and slide 1 had their name on it. The slides can be down loaded and more videos seen on the website http://www.mefielding.com/introduction-to-accounting.html
Views: 9 Mark FP
Sampling & its 8 Types: Research Methodology
 
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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: 352604 Examrace
How Analytics Enables Security Analytics - Or Not
 
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This is a recording of a talk at the Conference on Knowledge Discovery and Data Mining (KDD) in Chicago in August 2013. Slides are at: http://slidesha.re/1bsVNcX. The talk discusses the data mining and analytics challenges in information security. In the Cyber Security domain, we have been collecting 'big data' for almost two decades. The volume and variety of our data is extremely large, but understanding and capturing the semantics of the data is even more of a challenge. Finding the needle in the proverbial haystack has been attempted from many different angles. In this talk we will have a look at what approaches have been explored, what has worked, and what has not. We will see that there is still a large amount of work to be done and data mining is going to play a central role. We'll try to motivate that in order to successfully find bad guys, we will have to embrace a solution that not only leverages clever data mining, but employs the right mix between human computer interfaces, data mining, and scalable data platforms.
Views: 3069 Raffael Marty
Cement Manufacturing
 
02:58
For more information: http://www.7activestudio.com [email protected] http://www.7activemedical.com/ [email protected] http://www.sciencetuts.com/ [email protected] Contact: +91- 9700061777, +91- 9100061777 7 Active Technology Solutions Pvt.Ltd. is an educational 3D digital content provider for K-12. We also customise the content as per your requirement for companies platform providers colleges etc . 7 Active driving force "The Joy of Happy Learning" -- is what makes difference from other digital content providers. We consider Student needs, Lecturer needs and College needs in designing the 3D & 2D Animated Video Lectures. We are carrying a huge 3D Digital Library ready to use. MANUFACURE OF PORTLAND CEMENT:Raw Materials: The raw materials required for cement manufacture are. Lime stone which provides calcium. Clay which provides aluminium and silica.Cement is manufactured by two methods they are. Wet process. Dry process. Now let us discuss wet process and dry process detailed.WET PROCESS:In the wet process, first the clay is purified by washing in a wash mill.The lime stone is crushed into small particles and mixed with purified clay in proper proportions to get raw slurry.DRY PROCESS: In the dry process the raw materials are mixed in proper proportions.The mixture is dried, pulverized (Crushed into fine particles) and made uniform. The resulting powder is called "raw material".The raw slurry or raw meal, obtained by one of wet or dry process called charge. Charge is introduced into a rotary Kiln.The rotary kiln consists of a steel cylinder about 150meters long and 4meter diameter and rotates 30 to 60 turns per hour.At one end of the cylinder a screw conveyer is arranged which slowly allows the charge into the cylinder.The other end of the cylinder, a burneris arranged coal or burning oil is burnt at this end.The charge entering the cylinder slowly moves towards the hot end.At the burning end of the kiln, the temperature is around 1700 to 1900degrees centigrade, at this end some chemical reactions takes place between calcium oxide and aluminium silicates.Mixture of calcium silicates and calcium aluminates is formed.The resultant product consists of gray hard balls called clinker cement.Clinker cement is cooled, ground to fine powder and mixed with 2 to 3 percent of gypsum.
Views: 1325636 7activestudio
Nikunj Oza: "Data-driven Anomaly Detection" | Talks at Google
 
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This talk will describe recent work by the NASA Data Sciences Group on data-driven anomaly detection applied to air traffic control over Los Angeles, Denver, and New York. This data mining approach is designed to discover operationally significant flight anomalies, which were not pre-defined. These methods are complementary to traditional exceedance-based methods, in that they are more likely to yield false alarms, but they are also more likely to find previously-unknown anomalies. We discuss the discoveries that our algorithms have made that exceedance-based methods did not identify. Nikunj Oza is the leader of the Data Sciences Group at NASA Ames Research Center. He also leads a NASA project team which applies data mining to aviation safety. Dr. Ozaąs 40+ research papers represent his research interests which include data mining, machine learning, anomaly detection, and their applications to Aeronautics and Earth Science. He received the Arch T. Colwell Award for co-authoring one of the five most innovative technical papers selected from 3300+ SAE technical papers in 2005. His data mining team received the 2010 NASA Aeronautics Research Mission Directorate Associate Administratorąs Award for best technology achievements by a team. He received his B.S. in Mathematics with Computer Science from MIT in 1994, and M.S. (in 1998) and Ph.D. (in 2001) in Computer Science from the University of California at Berkeley.
Views: 7994 Talks at Google