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How data mining works
 
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In this video we describe data mining, in the context of knowledge discovery in databases. More videos on classification algorithms can be found at https://www.youtube.com/playlist?list=PLXMKI02h3_qjYoX-f8uKrcGqYmaqdAtq5 Please subscribe to my channel, and share this video with your peers!
Views: 244830 Thales Sehn Körting
PDF Data Extraction and Automation 3.1
 
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Learn how to read and extract PDF data. Whether in native text format or scanned images, UiPath allows you to navigate, identify and use PDF data however you need. Read PDF. Read PDF with OCR.
Views: 153098 UiPath
Data Mining  Association Rule - Basic Concepts
 
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short introduction on Association Rule with definition & Example, are explained. Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database. Parts of Association rule is explained with 2 measurements support and confidence. types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples. Names of Association rule algorithm and fields where association rule is used is also mentioned.
Data Mining with Weka (2.5: Cross-validation)
 
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Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 5: Cross-validation http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/D3ZVf8 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 42263 WekaMOOC
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Website: https://www.edureka.co/data-science Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 84188 edureka!
Association analysis: Frequent Patterns, Support, Confidence and Association Rules
 
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This lecture provides the introductory concepts of Frequent pattern mining in transnational databases.
Views: 70939 StudyKorner
PDF file: Reading and Extracting data using Python
 
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This a basic program for understanding PyPDF2 module and its methods. Simple program to read data in a PDF file.
Views: 17655 P Prog
INTRODUCTION TO DATA MINING IN HINDI
 
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Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 119260 LearnEveryone
K mean clustering algorithm with solve example
 
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#kmean datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 472475 Last moment tuitions
Introduction to Datawarehouse in hindi | Data warehouse and data mining Lectures
 
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#datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 344963 Last moment tuitions
Frequent Pattern (FP) growth Algorithm for Association Rule Mining
 
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The FP-Growth Algorithm, proposed by Han, is an efficient and scalable method for mining the complete set of frequent patterns by pattern fragment growth, using an extended prefix-tree structure for storing compressed and crucial information about frequent patterns named frequent-pattern tree (FP-tree).
Views: 135044 StudyKorner
Diagnosis of Lung Cancer Prediction System Using Data Mining Classification Techniques
 
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Views: 7500 Clickmyproject
Data Mining Lecture -- Bayesian Classification | Naive Bayes Classifier | Solved Example (Eng-Hindi)
 
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In the bayesian classification The final ans doesn't matter in the calculation Because there is no need of value for the decision you have to simply identify which one is greater and therefore you can find the final result. -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 217639 Well Academy
More Data Mining with Weka (4.6: Cost-sensitive classification vs. cost-sensitive learning)
 
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More Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 6: Cost-sensitive classification vs. cost-sensitive learning http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/I4rRDE https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 8764 WekaMOOC
Data Mining with Weka (4.5: Support vector machines)
 
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Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 5: Support vector machines 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: 46984 WekaMOOC
Data Mining with Weka (5.2: Pitfalls and pratfalls)
 
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Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 2: Pitfalls and pratfalls http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/5DW24X https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 12730 WekaMOOC
More Data Mining with Weka (2.5: Evaluating 2‐class classification)
 
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More Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 5: Evaluating 2‐class classification http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/QldvyV https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 7612 WekaMOOC
More Data Mining with Weka (2.2: Supervised discretization and the FilteredClassifier)
 
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More Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 2: Supervised discretization and the FilteredClassifier http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/QldvyV https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 11644 WekaMOOC
Collection of Data| आकड़ों  का संकलन Part 1 of 5 by Vijay Adarsh | Stay Learning | (HINDI)
 
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Collection of Data (आकड़ों का संकलन) Data are collected by individual research workers or by organization through sample surveys or experiments, keeping in view the objectives of the study. The data collected may be: 1) Primary Data 2) Secondary Data 1) Primary data Primary data means the raw data which has just been collected from the source and has not gone any kind of statistical treatment like sorting and tabulation. 2) Secondary Data Data which has already been collected by someone, may be sorted, tabulated and has undergone a statistical treatment. It is fabricated or tailored data. To View Full Video Lectures Visit - https://bit.ly/2PEEnUC ★ ACCOUNTS VIDEOS ★ https://www.youtube.com/channel/UCAXbiqmSkp9Sse4guGRMqDw?view_as=subscriber ★ COST ACCOUNTING VIDEOS ★ https://www.youtube.com/channel/UCAXbiqmSkp9Sse4guGRMqDw?view_as=subscriber ★ FINANCIAL MANAGEMENT VIDEOS ★ https://www.youtube.com/channel/UCAXbiqmSkp9Sse4guGRMqDw?view_as=subscriber ★ ECONOMICS VIDEOS ★ https://www.youtube.com/channel/UCK5RB8xNW_iOXz-rcGJZyTw?view_as=subscriber ★ INCOME TAX VIDEOS ★ https://www.youtube.com/channel/UCRRFVa1axTUdwZzc4Ta42XQ?view_as=subscriber ★ MATHS VIDEOS ★ https://www.youtube.com/channel/UCaIY3jMl7QDUWN6P6kSUYWw?view_as=subscriber STUDY TIPS ऐसे पढोगे तो हमेशा TOPPER बनोगे | Study Tips https://bit.ly/2QUXaew ENGLISH – Fatafat (Easy Way to Learn English) अंग्रेजी सीखें - फटाफट https://bit.ly/2PoAF4H ★ ExpertMotivation Channel https://bit.ly/2EsPBKC ★ For Any Information Video classes & Face To Face Batches Call +91 9268373738 E-mail: [email protected] (We Prefer emails rather than calls) Call timings Monday to Friday - Morning 10 to Evening 7 FACEBOOK: https://www.facebook.com/VijayAdarshIndia WEBSITE: http://www.vijayadarsh.com
Views: 127451 StayLearning
Data Mining with Weka (3.6: Nearest neighbor)
 
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Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 6: Nearest neighbor http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/YjZnrh https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 48606 WekaMOOC
More Data Mining with Weka (4.3: Scheme-independent attribute selection)
 
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More Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 3: Scheme-independent attribute selection http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/I4rRDE https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 6340 WekaMOOC
Data Mining with Weka (4.6: Ensemble learning)
 
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Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 6: Ensemble learning 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: 23344 WekaMOOC
More Data Mining with Weka (5.4: Meta-learners for performance optimization)
 
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More Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 4: Meta-learners for performance optimization http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/rDuMqu https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 7586 WekaMOOC
NLP : Python PDF Data Extraction
 
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Code : https://goo.gl/xUjhg2 Python Core ------------ Video in English https://goo.gl/df7GXL Video in Tamil https://goo.gl/LT4zEw Python Web application ---------------------- Videos in Tamil https://goo.gl/rRjs59 Videos in English https://goo.gl/spkvfv Python NLP ----------- Videos in Tamil https://goo.gl/LL4ija Videos in English https://goo.gl/TsMVfT Artificial intelligence and ML ------------------------------ Videos in Tamil https://goo.gl/VNcxUW Videos in English https://goo.gl/EiUB4P ChatBot -------- Videos in Tamil https://goo.gl/JU2WPk Videos in English https://goo.gl/KUZ7PY YouTube channel link www.youtube.com/atozknowledgevideos Website http://atozknowledge.com/ Technology in Tamil & English
Views: 16677 atoz knowledge
Data Mining with Weka (5.4: Summary)
 
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Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 4: Summary http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/5DW24X https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 11873 WekaMOOC
Advanced Data Mining with Weka (3.6: Application: Functional MRI Neuroimaging data)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 6: Application: Functional MRI Neuroimaging data http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/8yXNiM https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 1476 WekaMOOC
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: 411385 Examrace
KDD ( knowledge data discovery )  in data mining in hindi
 
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#kdd #datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 92478 Last moment tuitions
Time Series Analysis in Python | Time Series Forecasting | Data Science with Python | Edureka
 
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** Python Data Science Training : https://www.edureka.co/python ** This Edureka Video on Time Series Analysis n Python will give you all the information you need to do Time Series Analysis and Forecasting in Python. Below are the topics covered in this tutorial: 1. Why Time Series? 2. What is Time Series? 3. Components of Time Series 4. When not to use Time Series 5. What is Stationarity? 6. ARIMA Model 7. Demo: Forecast Future Subscribe to our channel to get video updates. Hit the subscribe button above. Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm #timeseries #timeseriespython #machinelearningalgorithms - - - - - - - - - - - - - - - - - About the Course Edureka’s Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Python Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR. During our Python Certification Training, our instructors will help you to: 1. Master the basic and advanced concepts of Python 2. Gain insight into the 'Roles' played by a Machine Learning Engineer 3. Automate data analysis using python 4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 6. Explain Time Series and it’s related concepts 7. Perform Text Mining and Sentimental analysis 8. Gain expertise to handle business in future, living the present 9. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 98789 edureka!
More Data Mining with Weka (4.1: Attribute selection using the "wrapper" method)
 
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More Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 1: Attribute selection using the "wrapper" method http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/I4rRDE https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 17304 WekaMOOC
Site Characterization Services: Step 1 Data Mining
 
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Terracon accomplishes data mining with a GIS-based platform that manages vast amounts of geospatial information from thousands of locations across the country. This provides our clients with better information right at the start of a project.
Views: 1599 Terraconconsultants
data mining fp growth | data mining fp growth algorithm | data mining fp tree example | fp growth
 
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In this video FP growth algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining algorithms in hindi, data mining in hindi, data mining lecture, data mining tools, data mining tutorial, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining fp growth, data mining fp growth algorithm, data mining fp tree example, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining, fp growth algorithm, fp growth algorithm example, fp growth algorithm in data mining, fp growth algorithm in data mining example, fp growth algorithm in data mining examples ppt, fp growth algorithm in data mining in hindi, fp growth algorithm in r, fp growth english, fp growth example, fp growth example in data mining, fp growth frequent itemset, fp growth in data mining, fp growth step by step, fp growth tree
Views: 172625 Well Academy
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: 131272 WekaMOOC
Introduction to Data Science with R - Data Analysis Part 1
 
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Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 1061466 David Langer
Installing PDF-4+ 2019 (Chinese Language) - ICDD - International Centre For Diffraction Data
 
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This Chinese language video describes installing ICDD's PDF-4+ database. http://www.icdd.com/index.php/pdf-4/ PDF-4+ 2019 contains 412,083 entries. It combines the world’s largest sources of inorganic diffraction data from crystals and powders into a single database. The result is a comprehensive collection of inorganic materials, produced in a standardized format that can be rapidly searched for unknown phase identification. Extensive data mining is facilitated with 126 display fields coupled with 74 searches. PDF-4+ is designed to support automated quantitative analyses by providing key reference data required for these analyses. It also contains an array of tools that supplement conventional analyses, such as a full suite of data simulation programs enabling the analysis of neutron, electron, and synchrotron data, in addition to conventional X-ray data. PDF-4+ features digitized patterns, molecular graphics, and atomic coordinates. These features incorporated into PDF-4+ enhance the ability to do quantitative analysis using third party software by any of three methods: Rietveld Analysis, Reference Intensity Ratio (RIR) Method, or Total Pattern Analysis.
Views: 86 ICDD
Naive Bayes Classifier Algorithm Example Data Mining | Bayesian Classification | Machine Learning
 
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naive Bayes classifiers in data mining or machine learning are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features. Naive Bayes has been studied extensively since the 1950s. It was introduced under a different name into the text retrieval community in the early 1960s,and remains a popular (baseline) method for text categorization, the problem of judging documents as belonging to one category or the other (such as spam or legitimate, sports or politics, etc.) with word frequencies as the features. With appropriate pre-processing, it is competitive in this domain with more advanced methods including support vector machines. It also finds application in automatic medical diagnosis. for more refer to https://en.wikipedia.org/wiki/Naive_Bayes_classifier naive bayes classifier example for play-tennis Download PDF of the sum on below link https://britsol.blogspot.in/2017/11/naive-bayes-classifier-example-pdf.html *****************************************************NOTE********************************************************************************* The steps explained in this video is correct but please don't refer the given sum from the book mentioned in this video coz the solution for this problem might be wrong due to printing mistake. **************************************************************************************************************************************** All data mining algorithm videos Data mining algorithms Playlist: http://www.youtube.com/playlist?list=PLNmFIlsXKJMmekmO4Gh6ZBZUVZp24ltEr ******************************************************************** book name: techmax publications datawarehousing and mining by arti deshpande n pallavi halarnkar *********************************************
Views: 43621 fun 2 code
Data Mining with Weka (3.1: Simplicity first!)
 
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Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 1: Simplicity first! 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: 30971 WekaMOOC
Some statistics tests, t-test, z-test, f-test and chi square test- A theoritical aspect
 
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( Correction - Pen was assumed name instead of auther) T-test, Z-test, F-yest, Chi square test. For different competitive exams Keep watching chanakya group of economics.
Scales of Measurement - Nominal, Ordinal, Interval, Ratio (Part 1) - Introductory Statistics
 
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This video reviews the scales of measurement covered in introductory statistics: nominal, ordinal, interval, and ratio (Part 1 of 2). Scales of Measurement Nominal, Ordinal, Interval, Ratio YouTube Channel: https://www.youtube.com/user/statisticsinstructor Subscribe today! Lifetime access to SPSS videos: http://tinyurl.com/m2532td Video Transcript: In this video we'll take a look at what are known as the scales of measurement. OK first of all measurement can be defined as the process of applying numbers to objects according to a set of rules. So when we measure something we apply numbers or we give numbers to something and this something is just generically an object or objects so we're assigning numbers to some thing or things and when we do that we follow some sort of rules. Now in terms of introductory statistics textbooks there are four scales of measurement nominal, ordinal, interval, and ratio. We'll take a look at each of these in turn and take a look at some examples as well, as the examples really help to differentiate between these four scales. First we'll take a look at nominal. Now in a nominal scale of measurement we assign numbers to objects where the different numbers indicate different objects. The numbers have no real meaning other than differentiating between objects. So as an example a very common variable in statistical analyses is gender where in this example all males get a 1 and all females get a 2. Now the reason why this is nominal is because we could have just as easily assigned females a 1 and males a 2 or we could have assigned females 500 and males 650. It doesn't matter what number we come up with as long as all males get the same number, 1 in this example, and all females get the same number, 2. It doesn't mean that because females have a higher number that they're better than males or males are worse than females or vice versa or anything like that. All it does is it differentiates between our two groups. And that's a classic nominal example. Another one is baseball uniform numbers. Now the number that a player has on their uniform in baseball it provides no insight into the player's position or anything like that it just simply differentiates between players. So if someone has the number 23 on their back and someone has the number 25 it doesn't mean that the person who has 25 is better, has a higher average, hits more home runs, or anything like that it just means they're not the same playeras number 23. So in this example its nominal once again because the number just simply differentiates between objects. Now just as a side note in all sports it's not the same like in football for example different sequences of numbers typically go towards different positions. Like linebackers will have numbers that are different than quarterbacks and so forth but that's not the case in baseball. So in baseball whatever the number is it provides typically no insight into what position he plays. OK next we have ordinal and for ordinal we assign numbers to objects just like nominal but here the numbers also have meaningful order. So for example the place someone finishes in a race first, second, third, and so on. If we know the place that they finished we know how they did relative to others. So for example the first place person did better than second, second did better than third, and so on of course right that's obvious but that number that they're assigned one, two, or three indicates how they finished in a race so it indicates order and same thing with the place finished in an election first, second, third, fourth we know exactly how they did in relation to the others the person who finished in third place did better than someone who finished in fifth let's say if there are that many people, first did better than third and so on. So the number for ordinal once again indicates placement or order so we can rank people with ordinal data. OK next we have interval. In interval numbers have order just like ordinal so you can see here how these scales of measurement build on one another but in addition to ordinal, interval also has equal intervals between adjacent categories and I'll show you what I mean here with an example. So if we take temperature in degrees Fahrenheit the difference between 78 degrees and 79 degrees or that one degree difference is the same as the difference between 45 degrees and 46 degrees. One degree difference once again. So anywhere along that scale up and down the Fahrenheit scale that one degree difference means the same thing all up and down that scale. OK so if we take eight degrees versus nine degrees the difference there is one degree once again. That's a classic interval scale right there with those differences are meaningful and we'll contrast this with ordinal in just a few moments but finally before we do let's take a look at ratio.
Views: 410218 Quantitative Specialists
Advanced Data Mining with Weka (2.4: MOA classifiers and streams)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 4: MOA classifiers and streams 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: 3205 WekaMOOC
Hashing techniques to resolve collision| Separate chaining and Linear Probing | Data structure
 
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In this video, I have explained hashing methods(Chaining and Linear Probing) which are used to resolve the collision. Jenny’s Lectures CS/IT NET&JRF is a Free YouTube Channel providing Computer Science / Information Technology / Computer-related tutorials including Programming Tutorials, NET & JRF Coaching Videos, Algorithms, GATE Coaching Videos, UGC NET, NTA NET, JRF, BTech, MTech, Ph.D., tips and other helpful videos for Computer Science / Information Technology students to advanced tech theory and computer science lectures, Teaching Computer Science in Informal Space. Learning to teach computer scienceoutside the classroom…. YouTube a top choice for users that want to learn computer programming, but don't have the money or the time to go through a complete college/ Institute / Coaching Centre course. ... Jenny’s Lectures CS/IT NET&JRF is aFree YouTube Channel providing computer-related ... and educate students in science, technology and other subjects. If you have any further questions, query, topic, please don't hesitate to contact me. Please feel free to comment or contact by ([email protected]), if you require any further information. Main Topics: Algorithms, Applied Computer Science, Artificial Intelligence, Coding, Computer Engineering, Computer Networking,Design and Analysis Of Algorithms, Data Structures, Digital Electronics, Object Oriented Programming using C++/Java/Python, Discrete Mathematical Structures, Operating Systems Computer Simulation, Computing, Bit Torrent, Abstract, C, C++, Acrobat, Ada, Pascal, ADABAS, Ad-Aware, Add-in, Add-on, Application Development, Adobe Acrobat, Automatic Data Processing, Adware, Artificial Intelligence, AI, Algorithm, Alphanumeric, Apache, Apache Tomcat, API, Application Programming Interface, Applet, Application, Application Framework, Application Macro, Application Package, Application Program, Application Programmer, Application Server, Application Software, Application Stack, Application Suite, System Administrator, Ada Programming, Architecture, computer software, ASP, Active Server Pages, Assembly, Assembly Language, Audacity, AutoCAD, Autodesk, Auto sketch, Backup, Restore, Backup & Recovery, BASH, BASIC, Beta Version, Binary Tree, Boolean, Boolean Algebra, Boolean AND, Boolean logic, Boolean OR, Boolean value, Binary Search Tree, BST, Bug, Business Software, C Programming Language, Computer Aided Design, Auto CAD, National Testing Agency, NTA,CAD, Callback, Call-by-Reference, Call by reference, Call-by-Value, Call by Value, CD/DVD, Encoding, Mapping, Character, Class, Class Library, ClearCase, ClearQuest, Client, Client-Side, cmd.exe, Cloud computing, Code, Codec, ColdFusion, Command, Command Interpreter, Command.com, Compiler, Animation, Computer Game, Computer Graphics, Computer Science, CONFIG.SYS, Configuration, Copyright, Customer Relationship Management, CRM, CVS, Data, Data Architect, Data Architecture, Data Cleansing, Data Conversion, Data Element, Data Mapping, Data Migration, Data Modeling, Data Processing, Data Scrubbing, Data Structure , Data Transformation, Database Administration, Database Model, Query Language, Database Server, Data log, Debugger, Database Management System, DBMS, Data Definition Language, DDL, Dead Code, Debugger, Decompile, Defragment, Delphi, Design Compiler, Device Driver, Distributed, Data Mart, Data Mining, Data Manipulation Language, DML, DOS, Disk Operating System, Dreamweaver, Drupal, Data Warehouse, Extensible Markup Language, XML, ASCII, Fibonacci , Firefox, Firmware, GUI, Graphical User Interface, LINUX, UNIX, J2EE, Java 2 Platform, Enterprise Edition, Java, Java EE, Java Beans, Java Programming Language, JavaScript, JDBC, Java Database Connectivity, Kernel, Keyboard, Keygen, LAMP, MySQL, Perl, PHP, Python, Logic Programming, Locator, Fusion, Fission, Low-Level Language, Mac OS, Macintosh Operating System, Machine Code, Machine Language, Metadata, Microsoft Access, Microsoft .Net Framework, Microsoft .Net, Microsoft SQL Server, Microsoft Windows, Middleware, MIS, Management Information systems, Module, Mozilla, MS-DOS,Microsoft Disk Operating System, Magic User Interface, MUI, MySQL, Normalization, Numerical, Object-Oriented, Open Source, Solaris, Parallel Processing, Parallel, Patch, Pascal, PDF, Portable Document Format, Postgres, Preemptive, Program, Programming Language, QuickTime, Report Writer, Repository, Rewind, Runtime, Scripting Languages, Script, Search Engine, Software Life-Cycle, VBScript, Virtual Basic Script, Classes, Queues, Stack, B-Tree, Computer Science, Information Technology, IT, CSE Quora profile: https://www.quora.com/profile/Jayanti-Khatri-Lamba Find me on Instagram: https://www.instagram.com/jayantikhatrilamba/
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: 74315 WekaMOOC
Data Mining: The Data Mining Guide for Beginners Audiobook by Herbert Jones
 
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You can listen to the full audiobook Data Mining: The Data Mining Guide for Beginners: Including Applications for Business, Data Mining Techniques, Concepts, and More, free at our library. Do you want to learn about data mining but don't feel like listening to a boring textbook? This data mining audiobook could be the answer you're looking for. Have you ever asked yourself how companies can provide you with a personalized data that is tailored just for you or how Facebook displays feeds and stories related to your search history? Well, data mining is the answer to both these questions. Data Mining: The Data Mining Guide for Beginners, Including Applications for Business, Data Mining Techniques, Concepts, and More will help you understand the basic concepts in data mining as well as its applications. It will dwell mostly on mining methods required in the processing as well as decision-making. There is no question that data mining has continued to grow and create value in many businesses. The ability to identify hidden knowledge and patterns in the numbers and texts generated daily provides analysts with room to understand the behavior of users. Through the development of models to identify patterns and discover new intelligence, it is now possible to change the business paradigm. This beginners guide will help you understand the different techniques that you can apply in data mining. It will help you develop the right foundation and skills important to master data mining. Inside you will learn the following: Model creation How to prepare your data How to clean your data Data mining Similarity and distances of data The effect of data distribution Association pattern mining What cluster analysis is What an outlier in data mining is How to deal with outliers in data mining Methods of identifying outliers in data Applications of data mining in the business industry Listen to this audiobook now to learn more about data mining! PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.
Views: 1 Boyce Whelan
PDF-4+  Elemental Search - Spanish - International Centre for Diffraction Data
 
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ICDD Member, Graciela C. D. de Delgado, of the University de Los Andes in Venezuela describes the steps of running an elemental search using the PDF-4+ database. http://www.icdd.com/index.php/pdf-4/ PDF-4+ 2019 contains 412,083 entries. It combines the world’s largest sources of inorganic diffraction data from crystals and powders into a single database. The result is a comprehensive collection of inorganic materials, produced in a standardized format that can be rapidly searched for unknown phase identification. Extensive data mining is facilitated with 126 display fields coupled with 74 searches. PDF-4+ is designed to support automated quantitative analyses by providing key reference data required for these analyses. It also contains an array of tools that supplement conventional analyses, such as a full suite of data simulation programs enabling the analysis of neutron, electron, and synchrotron data, in addition to conventional X-ray data. PDF-4+ features digitized patterns, molecular graphics, and atomic coordinates. These features incorporated into PDF-4+ enhance the ability to do quantitative analysis using third party software by any of three methods: Rietveld Analysis, Reference Intensity Ratio (RIR) Method, or Total Pattern Analysis.
Views: 11 ICDD
Data Mining with Weka (1.5: Using a filter )
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 5: Using a filter 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: 72410 WekaMOOC
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: 521834 studyezee
ROC Curves and Area Under the Curve (AUC) Explained
 
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An ROC curve is the most commonly used way to visualize the performance of a binary classifier, and AUC is (arguably) the best way to summarize its performance in a single number. As such, gaining a deep understanding of ROC curves and AUC is beneficial for data scientists, machine learning practitioners, and medical researchers (among others). SUBSCRIBE to learn data science with Python: https://www.youtube.com/dataschool?sub_confirmation=1 JOIN the "Data School Insiders" community and receive exclusive rewards: https://www.patreon.com/dataschool RESOURCES: - Transcript and screenshots: https://www.dataschool.io/roc-curves-and-auc-explained/ - Visualization: http://www.navan.name/roc/ - Research paper: http://people.inf.elte.hu/kiss/13dwhdm/roc.pdf LET'S CONNECT! - Newsletter: https://www.dataschool.io/subscribe/ - Twitter: https://twitter.com/justmarkham - Facebook: https://www.facebook.com/DataScienceSchool/ - LinkedIn: https://www.linkedin.com/in/justmarkham/
Views: 327778 Data School
More Data Mining with Weka (5.6: Summary)
 
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More Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 6: Summary http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/rDuMqu https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3434 WekaMOOC