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Views: 47 D. Nunciata

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Views: 51494 edureka!

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In this video I will be explaining few applications of NLP and I will be showing from where to download the stanford core nlp server in Telugu. Links: https://stanfordnlp.github.io/CoreNLP/corenlp-server.html https://nlp.stanford.edu/
Views: 105 For U

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Views: 326696 HackTech Wala

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Views: 745451 Siraj Raval

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Views: 168673 Siraj Raval

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Views: 648 Planeter

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In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3. This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy
Views: 455298 Thales Sehn Körting

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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: 224776 Augmented Startups

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Views: 6722271 freeCodeCamp.org

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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: 135247 nptelhrd

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Ye Liu describes the application of link analysis to transactional data in SAS Enterprise Miner 12.3.
Views: 4994 SAS Software

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Application Demo (Social Media Mining and Analysis) Anggi Perwitasari - Thesis
Views: 48 Anggi Perwitasari

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Views: 3983637 ProgrammingKnowledge

07:38
Views: 249098 Augmented Startups

05:47
In this video I explain very briefly how the Random Forest algorithm works with a simple example composed by 4 decision trees. The presentation is available at: https://prezi.com/905bwnaa7dva/?utm_campaign=share&utm_medium=copy
Views: 321552 Thales Sehn Körting

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International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] ******************************************************************* Call for Papers ============== Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations ======================= Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications ======================== Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining Knowledge Processing ==================== Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration/ Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission **************** Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/ijdkp/ijdkp.html
Views: 52 aircc journal

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Views: 2578725 3Blue1Brown

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Views: 82 Planeter

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Ms Excel Telugu,Computer tips in telugu, video tutorial in telugu, telugu video tutorial, Ms word 2007 in telugu www.timecomputers.in hafiztime hafiz telugu videos -~-~~-~~~-~~-~- Please watch: "Best Useful software For Windows Telugu" https://www.youtube.com/watch?v=puGZTRTSoVA -~-~~-~~~-~~-~- #telugutechtuts #hafiztime
Views: 1091773 Telugu TechTuts

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International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, ducational Data Mining Knowledge Processing Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission Authors are invited to submit papers for this journal through e-mail [email protected] . Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/ijdkp/ijdkp.html
Views: 23 aircc journal

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Views: 4 Willypd

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International Journal of Data Mining & Knowledge Management Process (IJDKP) ISSN : 2230 - 9608 [Online] ; 2231 - 007X [Print] http://airccse.org/journal/ijdkp/ijdkp.html Call for papers :- Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the Journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Topics of interest include, but are not limited to, the following: Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining. Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources. Paper Submission Authors are invited to submit papers for this journal through E-mail: [email protected] or [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit : http://airccse.org/journal/ijdkp/ijdkp.html
Views: 154 aircc journal

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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: 474765 Brandon Weinberg

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Views: 120520 Geeky Shows

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www.learnanalytics.in demostrates use of an free and open source platform to build sophisticated predictive models. We demonstrate using R package Rattle to do data analysis without writing a line of r code. We cover hypothesis testing, descriptive statistics, linear and logistic regression with a flavor of machine learning (Random Forest, SVM etc.). Also using graphs such as ROC curves and Area under curves (AUC) to compare various models. To download the dataset and follow on your own follow http://www.learnanalytics.in/datasets/Credit_Scoring.zip
Views: 44150 Learn Analytics

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Download workbook: http://people.highline.edu/mgirvin/ExcelIsFun.htm Learn the basics of Data Analysis at Highline Community College Professional Development Day 2012: Topics in Video: 1. What is Data Analysis? ( 00:53 min mark) 2. How Data Must Be Setup ( 02:53 min mark) Sort: 3. Sort with 1 criteria ( 04:35 min mark) 4. Sort with 2 criteria or more ( 06:27 min mark) 5. Sort by color ( 10:01 min mark) Filter: 6. Filter with 1 criteria ( 11:26 min mark) 7. Filter with 2 criteria or more ( 15:14 min mark) 8. Filter by color ( 16:28 min mark) 9. Filter Text, Numbers, Dates ( 16:50 min mark) 10. Filter by Partial Text ( 20:16 min mark) Pivot Tables: 11. What is a PivotTable? ( 21:05 min mark) 12. Easy 3 step method, Cross Tabulation ( 23:07 min mark) 13. Change the calculation ( 26:52 min mark) 14. More than one calculation ( 28:45 min mark) 15. Value Field Settings (32:36 min mark) 16. Grouping Numbers ( 33:24 min mark) 17. Filter in a Pivot Table ( 35:45 min mark) 18. Slicers ( 37:09 min mark) Charts: 19. Column Charts from Pivot Tables ( 38:37 min mark) Formulas: 20. SUMIFS ( 42:17 min mark) 21. Data Analysis Formula or PivotTables? ( 45:11 min mark) 22. COUNTIF ( 46:12 min mark) 23. Formula to Compare Two Lists: ISNA and MATCH functions ( 47:00 min mark) Getting Data Into Excel 24. Import from CSV file ( 51:21 min mark) 25. Import from Access ( 54:00 min mark) Highline Community College Professional Development Day 2012 Buy excelisfun products: https://teespring.com/stores/excelisfun-store
Views: 1596943 ExcelIsFun

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Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on linear regression, gradient descent, and normal equations and discusses how they relate to machine learning. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 CCS 229 Course Website: http://www.stanford.edu/class/cs229/ Stanford University: http://www.stanford.edu/ Stanford University Channel on YouTube: http://www.youtube.com/stanford
Views: 742580 Stanford

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Investigate a data table made of more than two qualitative variables using Multiple Correspondence Analysis. Discover our products: https://www.xlstat.com/en/solutions Go further: https://help.xlstat.com/customer/en/portal/articles/2062224 30-day free trial: https://www.xlstat.com/en/download -- Stat Café - Question of the Day is a playlist aiming at explaining simple or complex statistical features with applications in Excel and XLSTAT based on real life examples. Do not hesitate to share your questions in the comments. We will be happy to answer you. -- Produced by: Addinsoft Directed by: Nicolas Lorenzi Script by: Jean Paul Maalouf
Views: 3939 XLSTAT

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Investigate the links between the categories of two variables using Correspondence Analysis. Discover our products: https://www.xlstat.com/en/solutions Go further: https://help.xlstat.com/customer/en/portal/articles/2062223 Data can be downloaded here: https://help.xlstat.com/customer/portal/kb_article_attachments/124651/original.xlsx?1511966201 30-day free trial: https://www.xlstat.com/en/download -- Stat Café - Question of the Day is a playlist aiming at explaining simple or complex statistical features with applications in Excel and XLSTAT based on real life examples. Do not hesitate to share your questions in the comments. We will be happy to answer you. -- Produced by: Addinsoft Directed by: Nicolas Lorenzi Script by: Jean Paul Maalouf
Views: 3124 XLSTAT

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Decision Tree (CART) - Machine Learning Fun and Easy ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. A tree has many analogies in real life, and turns out that it has influenced a wide area of machine learning, covering both classification and regression (CART). So a decision tree is a flow-chart-like structure, where each internal node denotes a test on an attribute, each branch represents the outcome of a test, and each leaf (or terminal) node holds a class label. The topmost node in a tree is the root node. ------------------------------------------------------------ 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: 164882 Augmented Startups

01:13:06
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses the topic of reinforcement learning, focusing particularly on MDPs, value functions, and policy and value iteration. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 CS 229 Course Website: http://www.stanford.edu/class/cs229/ Stanford University: http://www.stanford.edu/ Stanford University Channel on YouTube: http://www.youtube.com/stanford
Views: 97084 Stanford

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The IEEE Computer Society presented its 2010 Technical Achievement Award to Ashok N. Srivastava for his pioneering contributions to intelligent information systems. The Technical Achievement Award honors outstanding and innovative contributions to computer and information science and engineering, usually within the past 10 years. Dr. Srivastava accepted his award at the Computer Society's 9 June 2010 awards ceremony in Denver, Colorado. Ashok N. Srivatava is the Principal Investigator for the Integrated Vehicle Health Management research project at NASA. His current research focuses on the development of data mining algorithms for anomaly detection in massive data streams, kernel methods in machine learning, and text mining algorithms. Dr. Srivastava is also the leader of the Intelligent Data Understanding group at NASA Ames Research Center. The group performs research and development of advanced machine learning and data mining algorithms in support of NASA missions. For more information about Ashok N. Srivastava: http://www.computer.org/portal/web/awards/srivastava For more information about IEEE Computer Society Awards: http://www.computer.org/awards
Views: 361 ieeeComputerSociety

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Microsoft Access is a database creation and management program. To understand Microsoft Access, you must first understand basics of databases. In this Course, you will learn about Access databases and how they are used. After conclusion of this workshop, you will be able to demonstrate proficiency while completing the following activities: Create a database file using electronic media Design, create, and populate a database table Design and use a database form with the form wizard Obtain selected information from a table by using query criteria Produce hard copy from query output. Design an attractive report while using the report wizard. This access database tutorial introduction for beginners will provide the introduction to Microsoft access database. -------------------Online Courses to learn---------------------------- Data Analytics with R Certification Training- http://bit.ly/2rSKHNP DevOps Certification Training - http://bit.ly/2T5P6bQ AWS Architect Certification Training - http://bit.ly/2PRHDeF Python Certification Training for Data Science - http://bit.ly/2BB3PV8 Java, J2EE & SOA Certification Training - http://bit.ly/2EKbwMK AI & Deep Learning with TensorFlow - http://bit.ly/2AeIHUR Big Data Hadoop Certification Training- http://bit.ly/2ReOl31 AWS Architect Certification Training - http://bit.ly/2EJhXjk Selenium Certification Training - http://bit.ly/2BFrfZs Tableau Training & Certification - http://bit.ly/2rODzSK Linux Administration Certification Training-http://bit.ly/2Gy9GQH ----------------------Follow--------------------------------------------- My Website - http://www.codebind.com My Blog - https://goo.gl/Nd2pFn My Facebook Page - https://goo.gl/eLp2cQ Google+ - https://goo.gl/lvC5FX Twitter - https://twitter.com/ProgrammingKnow Pinterest - https://goo.gl/kCInUp Text Case Converter - https://goo.gl/pVpcwL ------------------Facebook Links ---------------------------------------- http://fb.me/ProgrammingKnowledgeLearning/ http://fb.me/AndroidTutorialsForBeginners http://fb.me/Programmingknowledge http://fb.me/CppProgrammingLanguage http://fb.me/JavaTutorialsAndCode http://fb.me/SQLiteTutorial http://fb.me/UbuntuLinuxTutorials http://fb.me/EasyOnlineConverter
Views: 1301743 ProgrammingKnowledge

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CCTV image analysis for security and safety applications has become a trending topic in computer vision due to the increasing amount of CCTV cameras that are being deployed in outdoor and indoor scenarios. The analysis of the great amount of video that the cameras are capturing requires a very high investment in personnel and time just to be able to monitorize and analyse the events. Vicomtech-IK4 is working on Semantic Video Analysis Tools that help to automatically analyse and resume the complex activities that are happening on the scene. The complex events are described based on semantic rules helping the end users to describe the events that they want to find in the scene. The Semantic Video Analysis Tool processes the video and provides a final activity report that resumes all the activity in a couple of lines giving a complete overview of the activity occurred on the video file. More info: Viulib® is a software library, a solution that collects, processes and analyzes real-time video images. Download Viulib: http://www.viulib.org/ Viulib is property of http://www.vicomtech.org/ Viulib Contact: [email protected] General Contact: [email protected]
Views: 1186 Vicomtech

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Views: 54 Ajay Kumar Bharaj

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-~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-

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Views: 173 Planeter

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About Statistical Analysis Using Excel is an excerpt from Statistical Analysis Using Excel LiveLessons (Video Training): http://www.quepublishing.com/store/statistical-analysis-using-excel-livelessons-video-9780789750297 7+ Hours of Video Instruction Statistical Analysis Using Excel LiveLessons is the world’s first complete video training course of its kind on the topic. Bestselling author and trainer Conrad Carlberg provides the novice with 7+ hours of hands-on step-by-step video training on the fundamentals of statistical analysis. These videos make the concepts concrete using Excel charts, tools, and functions. Statistical analysis takes two main forms: descriptive statistics and inferential statistics. Descriptive statistics provide numbers that describe how values cluster together (averages), disperse (standard deviations), and vary together (correlations). Inferential statistics informs us regarding the probability that the descriptive statistics that we calculate from samples are accurate estimators of the populations from which we took the samples. These techniques are well worked out in theory and in applications such as Microsoft Excel. They have applicability in fields as diverse as politics and sports, as economics and agriculture, as psychology and business management, as achievement testing and manufacturing. This training on statistical analysis is designed to provide conceptual overviews of topics such as testing the reliability of the difference between the means of a treatment group and a control group, followed by demonstrations of how to handle the topic in Excel. Topics such as statistical power are crucial to understanding inferential analysis but history shows that they are very difficult to communicate through text. By using auditory explanations in combination with Excel's powerful charting capabilities, it's possible to communicate these abstract notions in a concrete fashion. Part I: About Excel and Statistical Analysis Accuracy of functions Appropriate use of statistical functions Overview of the Data Analysis Add-in Part II: Using Excel one variable at a time Central Tendency 1 Central Tendency 2 Variability 1 Variability 2 Variability 3 Array formulas using statistical functions Array formulas or pivot tables? Confidence intervals Descriptive Statistics tool Confidence intervals with the Descriptive Statistics tool Part III: Using one variable to analyze another Two variables at a time Correlation and scattercharts Regression and shared variance Regression diagnostics Understanding regression coefficients Testing the overall regression with the F ratio Forecasting with the TREND() function Part IV: Basic hypothesis testing Hypothesis testing: single sample z tests Single sample z tests: Excel’s normal distribution functions Z tests, alpha and statistical power Part V: Using the t distribution in Excel Mean differences and the t distribution Two-sample t tests Two-sample t tests Recap of consistency and compatibility functions http://www.quepublishing.com/store/statistical-analysis-using-excel-livelessons-video-9780789750297
Views: 1978 Que Publishing

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Views: 403 Another Question II

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Views: 21170 Data School

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Views: 62330 edureka!

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Lesson Overview: Vector Space models are attractive as they use techniques that align with many other Big data analytics. basically we view the bag (of words) as a vector. An example is given. Closeness such as with cosine measure can be defined and its features are analyzed. This measure is generalized to the famous TF-IDF measure. Enroll in this course at https://bigdatacourse.appspot.com/ and download course material, see information on badges and more. It's all free and only takes you a few seconds.

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Views: 81625 edureka!

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Pre-Order my new album on iTunes!: http://smarturl.it/BRAVEiTunesPreOrder Pre-Save my new album on Spotify: http://smarturl.it/BRAVESpotifyPreSave Pre-Order on Google Play & Amazon: http://smarturl.it/BRAVEAmazonPreOrder http://smarturl.it/BRAVEGPlayPreOrder Pre-order my new Songwriting E-Book for just \$3.99 here: http://smarturl.it/MotivationalMethods TAKE MY 10 SONGS CHALLENGE: http://bit.ly/10SongsChallenge 10 Lyric Writing Tips for Beginners: https://www.youtube.com/watch?v=owNbrxOGyeU 10 Songwriting Tips for Beginners: https://www.youtube.com/watch?v=fXZfpLY_x0A 10 MORE Songwriting Tips for Beginners: https://www.youtube.com/watch?v=6Ur9ykNUUXQ&t=269s Take part in monthly One-on-One Songwriting Workshops with me through Patreon! - http://bit.ly/EmmaPatreon 10. Pro Chords 9. Songwriter's Pad 8. Nano Studio 7. Evernote / Evernote Scannable 6. Medly 5. Metronome 4. Four Track 3. Garageband 2. Music Memos 1. Hum Music by Otis McDonald - 'Stay' Hear more from Otis: https://www.youtube.com/channel/UCej6bRv8lR48AEbQvdb_9Cg SUBSCRIBE: ► http://www.youtube.com/emmamcgann FACEBOOK: ► http://www.facebook.com/emmamcgann TWEET: ► http://www.twitter.com/emmamcgann INSTAGRAM: ► http://www.instagram.com/emmamcgann SITE: ► http://www.emmamcgann.com
Views: 582513 EmmaMcGann

02:26