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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: 118364 LearnEveryone
BADM 1.1: Data Mining Applications
 
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This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining. It is part of a series of 37 videos, all of which are available on YouTube. For more information: www.dataminingbook.com twitter.com/gshmueli facebook.com/dataminingbook Here is the complete list of the videos: • Welcome to Business Analytics Using Data Mining (BADM) • BADM 1.1: Data Mining Applications • BADM 1.2: Data Mining in a Nutshell • BADM 1.3: The Holdout Set • BADM 2.1: Data Visualization • BADM 2.2: Data Preparation • BADM 3.1: PCA Part 1 • BADM 3.2: PCA Part 2 • BADM 3.3: Dimension Reduction Approaches • BADM 4.1: Linear Regression for Descriptive Modeling Part 1 • BADM 4.2 Linear Regression for Descriptive Modeling Part 2 • BADM 4.3 Linear Regression for Prediction Part 1 • BADM 4.4 Linear Regression for Prediction Part 2 • BADM 5.1 Clustering Examples • BADM 5.2 Hierarchical Clustering Part 1 • BADM 5.3 Hierarchical Clustering Part 2 • BADM 5.4 K-Means Clustering • BADM 6.1 Classification Goals • BADM 6.2 Classification Performance Part 1: The Naive Rule • BADM 6.3 Classification Performance Part 2 • BADM 6.4 Classification Performance Part 3 • BADM 7.1 K-Nearest Neighbors • BADM 7.2 Naive Bayes • BADM 8.1 Classification and Regression Trees Part 1 • BADM 8.2 Classification and Regression Trees Part 2 • BADM 8.3 Classification and Regression Trees Part 3 • BADM 9.1 Logistic Regression for Profiling • BADM 9.2 Logistic Regression for Classification • BADM 10 Multi-Class Classification • BADM 11 Ensembles • BADM 12.1 Association Rules Part 1 • BADM 12.2 Association Rules Part 2 • Neural Networks: Part I • Neural Nets: Part II • Discriminant Analysis (Part 1) • Discriminant Analysis: Statistical Distance (Part 2) • Discriminant Analysis: Misclassification costs and over-sampling (Part 3)
Views: 3539 Galit Shmueli
Natural Language Processing (NLP) & Text Mining Tutorial Using NLTK | NLP Training | Edureka
 
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** NLP Using Python: - https://www.edureka.co/python-natural-language-processing-course ** This Edureka video will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and much more along with a demo on each one of the topics. The following topics covered in this video : 1. The Evolution of Human Language 2. What is Text Mining? 3. What is Natural Language Processing? 4. Applications of NLP 5. NLP Components and Demo Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV --------------------------------------------------------------------------------------------------------- Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ --------------------------------------------------------------------------------------------------------- - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Natural Language Processing using Python Training focuses on step by step guide to NLP and Text Analytics with extensive hands-on using Python Programming Language. It has been packed up with a lot of real-life examples, where you can apply the learnt content to use. Features such as Semantic Analysis, Text Processing, Sentiment Analytics and Machine Learning have been discussed. This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the important concepts and techniques used in Natural Language Processing using Python Programming Language. You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases of NLP in python programming language to enhance your learning experience. -------------------------- Who Should go for this course ? Edureka’s NLP Training is a good fit for the below professionals: From a college student having exposure to programming to a technical architect/lead in an organisation Developers aspiring to be a ‘Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Text Mining Techniques 'Python' professionals who want to design automatic predictive models on text data "This is apt for everyone” --------------------------------- Why Learn Natural Language Processing or NLP? Natural Language Processing (or Text Analytics/Text Mining) applies analytic tools to learn from collections of text data, like social media, books, newspapers, emails, etc. The goal can be considered to be similar to humans learning by reading such material. However, using automated algorithms we can learn from massive amounts of text, very much more than a human can. It is bringing a new revolution by giving rise to chatbots and virtual assistants to help one system address queries of millions of users. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Human language, developed over thousands and thousands of years, has become a nuanced form of communication that carries a wealth of information that often transcends the words alone. NLP will become an important technology in bridging the gap between human communication and digital data. --------------------------------- For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 60456 edureka!
How data mining works
 
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Data mining concepts Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use.Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term "data mining" is in fact a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence (e.g., machine learning) and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java[8] (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons.[9] Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate. The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps. The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.Data mining Data mining involves six common classes of tasks: Anomaly detection (outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation. Association rule learning (dependency modelling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis. Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data. Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam". Regression – attempts to find a function which models the data with the least error that is, for estimating the relationships among data or datasets. Summarization – providing a more compact representation of the data set, including visualization and report generation.
Views: 686 Technology mart
Web data extractor & data mining- Handling Large Web site Item | Excel data Reseller & Dropship
 
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Web scraping web data extractor is a powerful data, link, url, email tool popular utility for internet marketing, mailing list management, site promotion and 2 discover extractor, the scraper that captures alternative from any website social media sites, or content area on if you are interested fully managed extraction service, then check out promptcloud's services. Use casesweb data extractor extracting and parsing github wanghaisheng awesome web a curated list webextractor360 open source codeplex archive. It uses regular expressions to find, extract and scrape internet data quickly easily. Whether seeking urls, phone numbers, 21 web data extractor is a scraping tool specifically designed for mass gathering of various types. Web scraping web data extractor extract email, url, meta tag, phone, fax from download. Web data extractor pro 3. It can be a url, meta tags with title, desc and 7. Extract url, meta tag (title, desc, keyword), body text, email, phone, fax from web site, search 27 data extractor can extract of different kind a given website. Web data extraction fminer. 1 (64 bit hidden web data extractor semantic scholar. It is very web data extractor pro a scraping tool specifically designed for mass gathering of various types. The software can harvest urls, extracting and parsing structured data with jquery selector, xpath or jsonpath from common web format like html, xml json a curated list of promising extractors resources webextractor360 is free open source extractor. It scours the internet finding and extracting all relative. Download the latest version of web data extractor free in english on how to use pro vimeo. It can harvest urls, web data extractor a powerful link utility. A powerful web data link extractor utility extract meta tag title desc keyword body text email phone fax from site search results or list of urls high page 1komal tanejashri ram college engineering, palwal gandhi1211 gmail mdu rohtak with extraction, you choose the content are looking for and program does rest. Web data extractor free download for windows 10, 7, 8. Custom crawling 27 2011 web data extractor promises to give users the power remove any important from a site. A deep dive into natural language processing (nlp) web data mining is divided three major groups content mining, structure and usage. Web mining wikipedia web is the application of data techniques to discover patterns from world wide. This survey paper reports the basic web mining aims to discover useful information or knowledge from hyperlink structure, page, and usage data. Web data mining, 2nd edition exploring hyperlinks, contents, and web mining not just on the software advice. Data mining in web applications. Web data mining exploring hyperlinks, contents, and usage in web applications what is mining? Definition from whatis searchcrm. Web data mining and applications in business intelligence web humboldt universitt zu berlin. Web mining aims to dis cover useful data and web are not the same thing. Extracting the rapid growth of web in past two decades has made it larg est publicly accessible data source world. Web mining wikipedia. The web is one of the biggest data sources to serve as input for mining applications. Web data mining exploring hyperlinks, contents, and usage web mining, book by bing liu uic computer sciencewhat is mining? Definition from techopedia. Most useful difference between data mining vs web. As the name proposes, this is information gathered by web mining aims to discover useful and knowledge from hyperlinks, page contents, usage data. Although web mining uses many is the process of using data techniques and algorithms to extract information directly from by extracting it documents 19 that are generated systems. Web data mining is based on ir, machine learning (ml), statistics web exploring hyperlinks, contents, and usage (data centric systems applications) [bing liu] amazon. Based on the primary kind of data used in mining process, web aims to discover useful information and knowledge from hyperlinks, page contents, usage. Data mining world wide web tutorialspoint.
Views: 282 CyberScrap youpul
The ART of Data Mining – Practical learnings from real-world data mining applications
 
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Machine Learning and data mining is part SCIENCE (ML algorithms, optimization), part ENGINEERING (large-scale modelling, real-time decisions), part PROCESS (data understanding, feature engineering, modelling, evaluation, and deployment), and part ART. In this talk, Dr. Shailesh Kumar focuses on the "ART of data mining" - the little things that make the big difference in the quality and sophistication of machine learning models we build. Using real-world analytics problems from a variety of domains, Shailesh shares a number of practical learnings in: (1) The art of understanding the data better - (e.g. visualization of text data in a semantic space) (2) The art of feature engineering - (e.g. converting raw inputs into meaningful and discriminative features) (3) The art of dealing with nuances in class labels - (e.g. creating, sampling, and cleaning up class labels) (4) The art of combining labeled and unlabelled data - (e.g. semi-supervised and active learning) (5) The art of decomposing a complex modelling problem into simpler ones - (e.g. divide and conquer) (6) The art of using textual features with structured features to build models, etc. The key objective of the talk is to share some of the learnings that might come in handy while "designing" and "debugging" machine learning solutions and to give a fresh perspective on why data mining is still mostly an ART.
Views: 2009 HasGeek TV
Web Mining Complete Introduction  with Definition and it's type
 
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Click Here to Watch Complete Video Series : http://topperpoint.com/ Web Mining Complete Introduction with Definition and it's type web mining web data mining web mining tools web content mining web mining applications data mining website data analytics predictive analytics business analytics business intelligence data mining software web scraping web mining techniques data mining tools web analytics data analysis data scraping web mining in data mining data warehouse internet data mining website mining web data mining tools mining tools web mining examples data mining techniques data warehousing and data mining text mining software introduction to data mining data mining ppt web data extractor data mining companies data mining book data mining data mining course data mining pdf web crawler web miner screen scraping data mining applications data mining projects internet mining data mining examples data mining concepts and techniques data mining algorithms text mining text analysis web data .
An Example Application of Data Mining
 
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Have a look at one of our decision support systems powered by our data mining algorithms.
Web Crawler - CS101 - Udacity
 
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Help us caption and translate this video on Amara.org: http://www.amara.org/en/v/f16/ Sergey Brin, co-founder of Google, introduces the class. What is a web-crawler and why do you need one? All units in this course below: Unit 1: http://www.youtube.com/playlist?list=PLF6D042E98ED5C691 Unit 2: http://www.youtube.com/playlist?list=PL6A1005157875332F Unit 3: http://www.youtube.com/playlist?list=PL62AE4EA617CF97D7 Unit 4: http://www.youtube.com/playlist?list=PL886F98D98288A232& Unit 5: http://www.youtube.com/playlist?list=PLBA8DEB5640ECBBDD Unit 6: http://www.youtube.com/playlist?list=PL6B5C5EC17F3404D6 Unit 7: http://www.youtube.com/playlist?list=PL6511E7098EC577BE OfficeHours 1: http://www.youtube.com/playlist?list=PLDA5F9F71AFF4B69E Join the class at http://www.udacity.com to gain access to interactive quizzes, homework, programming assignments and a helpful community.
Views: 134393 Udacity
▶ Application of Data Mining - Real Life Use of Data Mining - Where We Can Use Data Mining ?
 
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Data Mining becomes a very hot topic in this moments because of its various uses. We can apply data mining to predict about an event that might happen. ✔Application of Data Mining - Real Life Use of Data Mining - Where We Can Use Data Mining? We're gonna learn some real-life scenario of Data Mining in this video. »See Full #Data_Mining Video Series Here: https://www.youtube.com/watch?v=t8lSMGW5eT0&list=PL9qn9k4eqGKRRn1uBmEhlmEd58ATOziA1 In This Video You are gonna learn Data Mining #Bangla_Tutorial Data mining is an important process to discover knowledge about your customer behavior towards your business offerings. » My #Linkedin_Profile: https://www.linkedin.com/in/rafayet13 » Read My Full Article on #Data_Mining Career Opportunity & So On » Link: https://medium.com/@rafayet13 #Learn_Data_Mining_In_A_Easy_Way #Data_Mining_Essential_Course #Data_Mining_Course_For_Beginner ট্র্যাডিশনাল পদ্ধতিতে যে সকল সমস্যার সহজে কোন সমাধান দেয়া যায় না #ডেটা_মাইনিং ব্যবহারে সহজেই একটি সিদ্ধান্তে পৌঁছানো সম্ভব। আর সে সিদ্ধান্ত কাজে লাগিয়ে ব্যবসায়িক অথবা যে কোন সম্পর্কিত সিদ্ধান্ত গ্রহন সম্ভব। Data Mining,big data,data analysis,data mining tutorial,book bd,Bangla tutorials,data mining software,Data Mining,What is data mining,bookbd,data analysis,data mining tutorial,data science,big data, business intelligence,data mining tools,bangla tutorial,data mining bangla tutorial,how to,how to mine data, knowledge discovery, Artificial Intelligence,Deep learning,machine learning,Python tutorials, Data Mining in the Retail Industry What does the future of business look like? How data will transform business? How data mining will transform business?
Views: 9371 BookBd
Detecting Network Intrusions With Machine Learning Based Anomaly Detection Techniques
 
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Machine learning techniques used in network intrusion detection are susceptible to “model poisoning” by attackers. The speaker will dissect this attack, analyze some proposals for how to circumvent such attacks, and then consider specific use cases of how machine learning and anomaly detection can be used in the web security context. Author: Clarence Chio More: http://www.phdays.com/program/tech/40866/ Any use of this material without the express consent of Positive Technologies is prohibited.
Views: 10636 Positive Technologies
Pocket Data Mining
 
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http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-319-02710-4 Pocket Data Mining PDM is our new term describing collaborative mining of streaming data in mobile and distributed computing environments. With sheer amounts of data streams are now available for subscription on our smart mobile phones, the potential of using this data for decision making using data stream mining techniques has now been achievable owing to the increasing power of these handheld devices. Wireless communication among these devices using Bluetooth and WiFi technologies has opened the door wide for collaborative mining among the mobile devices within the same range that are running data mining techniques targeting the same application. Related publications: Stahl F., Gaber M. M., Bramer M., and Yu P. S, Distributed Hoeffding Trees for Pocket Data Mining, Proceedings of the 2011 International Conference on High Performance Computing & Simulation (HPCS 2011), Special Session on High Performance Parallel and Distributed Data Mining (HPPD-DM 2011), July 4 -- 8, 2011, Istanbul, Turkey, IEEE press. http://eprints.port.ac.uk//3523 Stahl F., Gaber M. M., Bramer M., Liu H., and Yu P. S., Distributed Classification for Pocket Data Mining, Proceedings of the 19th International Symposium on Methodologies for Intelligent Systems (ISMIS 2011), Warsaw, Poland, 28-30 June, 2011, Lecture Notes in Artificial Intelligence LNAI, Springer Verlag. http://eprints.port.ac.uk/3524/ Stahl F., Gaber M. M., Bramer M., and Yu P. S., Pocket Data Mining: Towards Collaborative Data Mining in Mobile Computing Environments, Proceedings of the IEEE 22nd International Conference on Tools with Artificial Intelligence (ICTAI 2010), Arras, France, 27-29 October, 2010. http://eprints.port.ac.uk/3248/
Views: 3021 Mohamed Medhat Gaber
15 Best Free Books for Machine Learning | Download Link Available
 
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Books links on Amazon available here: https://tinyurl.com/yb94wz3k Free Books links available here: https://openload.co/f/IX6_AKTAcTQ https://openload.co/f/qe-5ppSbmGY https://openload.co/f/kfZ88_uvHG4 https://openload.co/f/F4UtH9JQkrA https://openload.co/f/gvz5ORAGtb0 On Amazon : 1.Machine Learning For Absolute Beginners: A Plain English Introduction (Second Edition) (Machine Learning For Beginners Book 1): https://amzn.to/2S6o6sF 2.Python Machine Learning By Example:https://amzn.to/2GsfmvP 3.Learning scikit-learn: Machine Learning in Python : https://amzn.to/2GtBllY 4.Machine Learning in Python : https://amzn.to/2STxNL3 5.Building Machine Learning Systems with Python : https://amzn.to/2S7MHxt 6.Beginning Python Visualization: Crafting Visual Transformation Scripts (Books for Professionals by Professionals) : https://amzn.to/2BwQqNM 7. Data Analysis with Open Source Tools: A Hands-On Guide for Programmers and Data Scientists : https://amzn.to/2S6p34d 8.Machine Learning in Action : https://amzn.to/2BpDN7f 9.Mining The Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub and More : https://amzn.to/2S3armh 10.Advanced Machine Learning with Python : https://amzn.to/2GB5qzU 11.MACHINE LEARNING WITH PYTHON- An Approach to Applied Machine Learning : https://amzn.to/2CjMH7X 12. Machine Learning for Hackers: Case Studies and Algorithms to Get You Started : https://amzn.to/2S8jp1I 13. Vital Introduction to Machine Learning with Python: Best Practices to Improve and Optimize Machine Learning Systems and Algorithms (Computer Coding Book 2): https://amzn.to/2CkKoSb 14.Introduction to Machine Learning with Python: A Guide for Data Scientists :https://amzn.to/2GszTQH 15. Data Mining: Practical Machine Learning Tools and Techniques: https://amzn.to/2GoC3AU 16. Python: Advanced Predictive Analytics : https://amzn.to/2SapMl2 #freebooks #machinelearning #bestbooks
Views: 346 MLAIT
How Big Data Is Used In Amazon Recommendation Systems | Big Data Application & Example | Simplilearn
 
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This Big Data Video will help you understand how Amazon is using Big Data is ued in their recommendation syatems. You will understand the importance of Big Data using case study. Recommendation systems have impacted or even redefined our lives in many ways. One example of this impact is how our online shopping experience is being redefined. As we browse through products, the Recommendation system offer recommendations of products we might be interested in. Regardless of the perspectives, business or consumer, Recommendation systems have been immensely beneficial. And big data is the driving force behind Recommendation systems. Subscribe to Simplilearn channel for more Big Data and Hadoop Tutorials - https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Check our Big Data Training Video Playlist: https://www.youtube.com/playlist?list=PLEiEAq2VkUUJqp1k-g5W1mo37urJQOdCZ Big Data and Analytics Articles - https://www.simplilearn.com/resources/big-data-and-analytics?utm_campaign=Amazon-BigData-S4RL6prqtGQ&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Big Data and Hadoop, check our Big Data Hadoop and Spark Developer Certification Training Course: http://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Amazon-BigData-S4RL6prqtGQ&utm_medium=Tutorials&utm_source=youtube #bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatacertification #HadoopTutorial - - - - - - - - - About Simplilearn's Big Data and Hadoop Certification Training Course: The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab. Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form. As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification. - - - - - - - - What are the course objectives of this Big Data and Hadoop Certification Training Course? This course will enable you to: 1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark 2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management 3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts 4. Get an overview of Sqoop and Flume and describe how to ingest data using them 5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning 6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution 7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations 8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS 9. Gain a working knowledge of Pig and its components 10. Do functional programming in Spark 11. Understand resilient distribution datasets (RDD) in detail 12. Implement and build Spark applications 13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques 14. Understand the common use-cases of Spark and the various interactive algorithms 15. Learn Spark SQL, creating, transforming, and querying Data frames - - - - - - - - - - - Who should take up this Big Data and Hadoop Certification Training Course? Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals: 1. Software Developers and Architects 2. Analytics Professionals 3. Senior IT professionals 4. Testing and Mainframe professionals 5. Data Management Professionals 6. Business Intelligence Professionals 7. Project Managers 8. Aspiring Data Scientists - - - - - - - - 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: 32737 Simplilearn
DataCalculus – Data Mining Software – Data Analysis Tools
 
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DataCalculus is a simple data analysis software with elegant data visualization tools for tabular databases such as Excel files. It covers all the major data mining techniques and applications: data visualization, anomaly detection, association rule learning, classification, and clustering. www.DataCalculus.com
Views: 36 Data Calculus
Data Mining For Business Intelligence
 
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Data Mining For Business Intelligence: Concepts, Techniques, And Applications In Microsoft Office Excel With XLMiner. B... http://www.thebookwoods.com/book02/0470084855.html Author of the book in this video: Galit Shmueli Nitin R. Patel Peter C. Bruce The book in this video is published by: Wiley-Interscience THE MAKER OF THIS VIDEO IS NOT AFFILIATED WITH OR ENDORSED BY THE PUBLISHING COMPANIES OR AUTHORS OF THE BOOK IN THIS VIDEO. ---- DISCLAIMER --- Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for fair use for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. All content in this video and written content are copyrighted to their respective owners. All book covers and art are copyrighted to their respective publishing companies and/or authors. We do not own, nor claim ownership of any images used in this video. All credit for the images or photography go to their rightful owners.
Views: 318 Johan Lidrag Hagen
Mining Web Data for Public Health
 
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Recent years have seen the adoption of new Web data sources in a wide range of health areas. Of all areas, public health applications in behavioral medicine have the most potential to change how we conduct research, opening up exciting new opportunities. Fundamentally, behavioral medicine requires understanding how people make health decisions: what influences their decision, how they weigh information, and how social connections impact decisions. Web data sources provide new opportunities for studying these questions. Answering these questions often requires new data mining methods. In this talk, I will present multi-dimensional topic models of text which jointly capture topic and other aspects of text. We describe Factorial Latent Dirichlet Allocation, a multi-dimensional model in which a document is influenced by K different factors, and each word token depends on a K-dimensional vector of latent variables. I will demonstrate the advantages of this model in the application of mining drug experiences from web forums.
Views: 136 Microsoft Research
The Ultimate Introduction to Web Scraping and Browser Automation
 
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Whenever you need to import data from an external website, hopefully they provide an API and make your life easy. But in the real world, that's not always the case. There are numerous reasons why you might want to get data from a web page or multiple web pages, and there's no API in sight, and in that case you're going to need to fall back onto Web Scraping and Browser Automation. In this screencast I'm going to give a high level overview of how to scrape websites, then cover five different scenarios, in increasing difficulty, for practical web scraping. There is a massive amount of information in this screencast and I'm going to straight up bombard you with it, but if you can make it until the end I guarantee you will come out knowing how to scrape websites with the best of them. As always, you can hit me up on twitter @AlwaysBCoding with questions, comments, to argue about programming, or to drop a suggestion for which topics I should cover next.
Views: 167777 Decypher Media
Using Opinion Mining Techniques in Tourism
 
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Using Opinion Mining Techniques in Tourism To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: www.jpinfotech.org, Blog: www.jpinfotech.blogspot.com This paper proposes a platform for extraction and summarizing of opinions expressed by users in tourism related online platforms. Extracting opinions from user generated reviews, regarding aspects specific to hotel services, are useful both to clients looking for accommodation, and also hotels trying to improve their services. The proposed system extracts hotel reviews from internet and classifies them, using an opinion mining technique. Platform is evaluated using a manually pre-classified dataset of user reviews. In the paper the efficiency of algorithms are analyzed using text mining domain specific measures, and are proposed methods for improving the results.
Views: 380 jpinfotechprojects
Text Mining in Publishing
 
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TEXT MINING AND SCHOLARLY PUBLISHING: This short video by John Bond of Riverwinds Consulting discusses Text Mining and the Scholarly Publishing Industry. MORE VIDEOS on TEXT MINING and Scholarly Publishing can be found at: https://www.youtube.com/playlist?list=PLqkE49N6nq3jY125di1g8UDADCMvCY1zk FIND OUT more about John Bond and his publishing consulting practice at www.RiverwindsConsulting.com SEND IDEAS for John to discuss on Publishing Defined. Email him at [email protected] or see http://www.PublishingDefined.com CONNECT Twitter: https://twitter.com/JohnHBond LinkedIn: https://www.linkedin.com/in/johnbondnj Google+: https://plus.google.com/u/0/113338584717955505192 Goodreads: https://www.goodreads.com/user/show/51052703-john-bond YouTube: https://www.youtube.com/c/JohnBond BOOKS by John Bond: The Story of You: http://www.booksbyjohnbond.com/the-story-of-you/about-the-book/ You Can Write and Publish a Book: http://www.booksbyjohnbond.com/you-can-write-and-publish-a-book/about-the-book/ TRANSCRIPT: Hi there. I am John Bond from Riverwinds Consulting and this is Publishing Defined. Today I am going to discuss text mining as it relates to scholarly publishing. Text mining also goes by the phrase text data mining or text analytics. Text mining in scholarly publishing is the process of deriving high-quality information from peer reviewed articles and other content. It does this by processing large amounts of information and looking for patterns within the data, and then evaluating and interpreting the results. Text mining is most beneficial to researchers or other power users of technical content. It is very different from a keyword search such that you might perform with Google. A key word search likely produces thousands of web links with no uniformity in the results and certainly no ability to draw meaningful conclusions. An example: let’s say you are researching bladder cancer in men and you are looking for specific biomarkers for other disease states. You probably don’t have the time to review all the literature you might find through a search at PubMed. Text mining will review the available literature. It understands the parts of speech (nouns, verbs), recognizes abbreviations, takes term frequency into account, and other natural language processes. It will filter through all the content, extracts relevant facts, spot patterns, and provides the researcher with a more condensed set of results and statements than a literature search or a cursory review of abstracts ever could. It knows bladder cancer is a disease state. It knows, in this instance, to look for men as opposed to women. It understands what a biomarker is and how to apply this term to other disease states. It understands bladder cancer is a phrase and not being used as two separate terms. Text mining software involves high level programming and such concepts as word frequency distribution, pattern recognition, information extraction, and natural language processing as well as other programming concepts well beyond the scope of this video. The overall goal is to turn text into data for analysis and thereby help to draw conclusions. However, the results of text mining in and of themselves is not the end product, just part of the process. Individual text mining tools or enterprise level ones have become more common with researchers, librarians, and large for profit and not for profit organizations, and they will only grow. Aside from a text mining tool, an application is also necessary to check that the content being mined is licensed and to provide appropriate links to the content. Text mining is important to publishers or any group that holds large stores of full text articles or databases because this information as a whole has greater value than each individual part. Text mining can help extract that value. A key point for publishers is that the text mining tool and its user, such as a researcher, needs to have access to the content either by it being open access, through a subscription, or through a purchase. Subscription publishers see revenue when content is accessed or purchased. All publishers see article downloads and page views from text mining efforts. Either way, text mining as a tool in research, in medicine, in pharmaceutical R&D will only continue to grow in importance. Well that’s it. Please subscribe to my YouTube channel or click on the playlist to see more videos about text mining in scholarly publishing. And make comments below or email me with questions. Thank so much and take care.
Views: 320 John Bond
▶ 5 Most Used Data Mining Software || Data Mining Tools -- Famous Data Mining Tools
 
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»See Full #Data_Mining Video Series Here: https://www.youtube.com/watch?v=t8lSMGW5eT0&list=PL9qn9k4eqGKRRn1uBmEhlmEd58ATOziA1 In This Video You are gonna learn Data Mining #Bangla_Tutorial Data mining is an important process to discover knowledge about your customer behavior towards your business offerings. » My #Linkedin_Profile: https://www.linkedin.com/in/rafayet13 » Read My Full Article on Data Mining Career Opportunity & So On » Link: https://medium.com/@rafayet13 #Learn_Data_Mining_In_A_Easy_Way #Data_Mining_Essential_Course #Data_Mining_Course_For_Beginner Here We're Going to Learn Which Software is best to use in Data Mining Field R remains the leading tool, with 49% share, but Python grows faster and almost catches up to R. RapidMiner remains the most popular general Data Science. আধুনিক প্রযুক্তির ব্যবহার বৃদ্ধির সাথে অতি দ্রুত ডেটা উৎপন্ন হচ্ছে। টেক জায়ান্ট আইবিএম জানায় ইন্টারনেটে যত ডেটা আছে তার ৯০ ভাগ উৎপন্ন হয়েছে গত তিন বছরে। এ ডেটা উৎপন্নের হার দিনকে দিন বেড়েই চলছে। বিশেষজ্ঞদের ধারনা ২০২০ সাল নাগাদ প্রায় ৪০ জেটাবাইট ডেটা জেনারেট হবে। যা ২০১১ তুলনায় প্রায় ৫০ গুন বেশি। বিশাল পরিমাণ এই ডেটা প্রক্রিয়াজাতের মাধ্যমে বিজ্ঞান, গবেষণা, চিকিৎসা, শিক্ষা ও ব্যবসায় ব্যপক ভুমিকা রাখা যেতে পারে। তাই বলা হচ্ছে “ বিগ ডেটা ইজ বিগ ইমপ্যাক্ট।” Data Mining,big data,data analysis,data mining tutorial,book , Bangla tutorials,data mining software,Data Mining,What is data mining, bookbd, data analysis,data mining tutorial,data science,big data,business tutorial,data mining Bangla tutorial,how to,how to mine data,knowledge discovery,Artificial Intelligence,Deep learning,machine learning,Python tutorials,
Views: 7709 BookBd
Text Analytics with R | How to Scrap Website Data for Text Analytics | Web Scrapping in R
 
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In this text analytics with R tutorial, I have talked about how you can scrap website data in R for doing the text analytics. This can automate the process of web analytics so that you are able to see when the new info is coming, you just run the R code and your analytics will be ready. Web scrapping in R is done by using the rvest package. Text analytics with R,how to scrap website data in R,web scraping in R,R web scraping,learn web scraping in R,how to get website data in R,how to fetch web data in R,web scraping with R,web scraping in R tutorial,web scraping in R analytics,web scraping in r rvest,web scraping and r,web scraping regex,web scraping facebook in r,r web scraping rvest,web scraping in R,web scraper with r,web scraping in r pdf,web scraping avec and r,web scraping and r
How To Extract Data From Amazon Using R | Amazon Extraction Techniques - ExcelR
 
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ExcelR : Learn Extracting reviews from Amazon using R; Identifying the common start and common end in the source of the review. Things you will learn in this video 1. How to extract reviews from amazon? To buy eLearning course on Data Science click here https://goo.gl/oMiQMw To register for classroom training click here https://goo.gl/UyU2ve To Enroll for virtual online training click here " https://goo.gl/JTkWXo" SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx For Introduction to data science demo click here https://goo.gl/2vkFjq #ExcelRSolutions #DataScience #BusinessAnalytics #DatasciencewithR#DataScienceWithPython #DataScienceTutorialForBeginners #DataScienceTraining #DataScienceCertification #DataSciencetutorial ----- For More Information: Toll Free (IND) : 1800 212 2120 | +91 80080 09706 Malaysia: 60 11 3799 1378 USA: 001-844-392-3571 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com Connect with us: Facebook: https://www.facebook.com/ExcelR/ LinkedIn: https://www.linkedin.com/company/exce... Twitter: https://twitter.com/ExcelrS G+: https://plus.google.com/+ExcelRSolutions
Web Mining - 01
 
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Views: 266 Sarbast Tube
Final Year Projects 2015 | Automated web usage data mining and recommendation system
 
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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: 3448 Clickmyproject
Intro to Data Analysis / Visualization with Python, Matplotlib and Pandas | Matplotlib Tutorial
 
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Python data analysis / data science tutorial. Let’s go! For more videos like this, I’d recommend my course here: https://www.csdojo.io/moredata Sample data and sample code: https://www.csdojo.io/data My explanation about Jupyter Notebook and Anaconda: https://bit.ly/2JAtjF8 Also, keep in touch on Twitter: https://twitter.com/ykdojo And Facebook: https://www.facebook.com/entercsdojo Outline - check the comment section for a clickable version: 0:37: Why data visualization? 1:05: Why Python? 1:39: Why Matplotlib? 2:23: Installing Jupyter through Anaconda 3:20: Launching Jupyter 3:41: DEMO begins: create a folder and download data 4:27: Create a new Jupyter Notebook file 5:09: Importing libraries 6:04: Simple examples of how to use Matplotlib / Pyplot 7:21: Plotting multiple lines 8:46: Importing data from a CSV file 10:46: Plotting data you’ve imported 13:19: Using a third argument in the plot() function 13:42: A real analysis with a real data set - loading data 14:49: Isolating the data for the U.S. and China 16:29: Plotting US and China’s population growth 18:22: Comparing relative growths instead of the absolute amount 21:21: About how to get more videos like this - it’s at https://www.csdojo.io/moredata
Views: 324385 CS Dojo
web scraping using python for beginners
 
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Learn Python here: https://courses.learncodeonline.in/learn/Python3-course In this video, we will talk about basics of web scraping using python. This is a video for total beginners, please comment if you want more videos on web scraping fb: https://www.facebook.com/HiteshChoudharyPage homepage: http://www.hiteshChoudhary.com Download LearnCodeOnline.in app from Google play store and Apple App store
Views: 203199 Hitesh Choudhary
8 Mining Complex Types of Data- Data Warehouse and Data Mining
 
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Tested & Best products- https://www.amazon.in/shop/atozsky Buy any Solar panel with discount- http://bit.ly/2RgM313 Tested and Selected - https://www.amazon.in/shop/atozsky http://www.atozsky.com/ https://www.facebook.com/atozsky.computer/ All credits goes to NIELIT, Delhi INDIA
Views: 461 AtoZ COMPUTER
[Data Science]  Kaggling in the Cloud:  ACM Data Mining Hackathon with Data Science Linux on AWS
 
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@nickkolegraff shows beginners how to get up and running with your first kaggle submission to the ACM hackathon using tools from Data Science Linux for doing data science on large datasets.
Views: 2462 BigDataRLinux
Data Collection and Preprocessing | Lecture 6
 
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Deep Learning Crash Course playlist: https://www.youtube.com/playlist?list=PLWKotBjTDoLj3rXBL-nEIPRN9V3a9Cx07 Highlights: Garbage-in, Garbage-out Dataset Bias Data Collection Web Mining Subjective Studies Data Imputation Feature Scaling Data Imbalance #deeplearning #machinelearning
Views: 2249 Leo Isikdogan
Lecture 46 — Opinion Mining and Sentiment Analysis  Latent Aspect Rating Analysis - Part 1 | UIUC
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
AWS re:Invent 2014 | (WEB301) Operational Web Log Analysis
 
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"Log data contains some of the most valuable raw information you can gather and analyze about your infrastructure and applications. Amid the mess of confusing lines of seemingly random text can be hints about performance, security, flaws in code, user access patterns, and other operational data. Without the proper tools, finding insights in these logs can be like searching for a hay-colored needle in a haystack. In this session you learn what practices and patterns you can easily implement that can help you better understand your log files. You see how you can customize web logs to add more information to them, how to digest logs from around your infrastructure, and how to analyze your log files in near real time. "
Views: 1698 Amazon Web Services
Text Mining, the Tidy Way
 
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Delivered by Julia Silge (Stack Overflow) at the 2017 New York R Conference on April 21st and 22nd at Work-Bench.
Views: 3727 Work-Bench
Machine Learning Tutorial 10 - Binning Data
 
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Best Machine Learning book: https://amzn.to/2MilWH0 (Fundamentals Of Machine Learning for Predictive Data Analytics). Machine Learning and Predictive Analytics. #MachineLearning Features are the term used for the columns in the analytics base table (ABT). There is a particular type of feature known as a continuous feature. These are features that have a very high cardinality because the allowed values (domain) is on a spectrum. We can convert these continuous features to categorical features through a process called binning. This online course covers big data analytics stages using machine learning and predictive analytics. Big data and predictive analytics is one of the most popular applications of machine learning and is foundational to getting deeper insights from data. Starting off, this course will cover machine learning algorithms, supervised learning, data planning, data cleaning, data visualization, models, and more. This self paced series is perfect if you are pursuing an online computer science degree, online data science degree, online artificial intelligence degree, or if you just want to get more machine learning experience. Enjoy! Check out the entire series here: https://www.youtube.com/playlist?list=PL_c9BZzLwBRIPaKlO5huuWQdcM3iYqF2w&playnext=1 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Support me! http://www.patreon.com/calebcurry Subscribe to my newsletter: http://bit.ly/JoinCCNewsletter Donate!: http://bit.ly/DonateCTVM2. ~~~~~~~~~~~~~~~Additional Links~~~~~~~~~~~~~~~ More content: http://CalebCurry.com Facebook: http://www.facebook.com/CalebTheVideoMaker Google+: https://plus.google.com/+CalebTheVideoMaker2 Twitter: http://twitter.com/calebCurry Amazing Web Hosting - http://bit.ly/ccbluehost (The best web hosting for a cheap price!)
Views: 6798 Caleb Curry
Amazon Web Services - Cluster Planning
 
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Connect with me or follow me at https://www.linkedin.com/in/durga0gadiraju https://www.facebook.com/itversity https://github.com/dgadiraju https://www.youtube.com/c/TechnologyMentor https://twitter.com/itversity
Views: 3520 itversity
Detecting Phishing Websites using Machine Learning Technique
 
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Get this project at http://nevonprojects.com/detecting-phishing-websites-using-machine-learning/ In order to detect and predict phishing website, we proposed an intelligent, flexible and effective system that is based on using classification Data mining algorithm
Views: 13192 Nevon Projects
Amazon Scraper Software 2018 -  Scrape for Easy Profits In Seconds! Best Selling Products
 
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To download: http://autopostingtools.com/ Did you see this yet? This software is AWESOME! .. you gotta see this! This video briefly demonstrates a scraping application I created using my proprietary scraping technology In this case, it has been put to use to scrape products from Amazon. It also get the best selling products. Really powerfull software this will set you apart from your competitors. Enjoy The software lets you post in backpage, craigslist, kijiji, facebook, youtube and it also has a lot of scrapers to get information on one place The software has 82 modules that will help you for your marketing. You will be able to make money, generate lead and generate traffic to any site. To download: http://autopostingtools.com/ Links to my Social Media Website: http://josegarcia.ca/ Facebook: http://josegarcia.ca/facebook Youtube: http://josegarcia.ca/youtube LinkedIn: http://josegarcia.ca/linkedin Twitter: http://josegarcia.ca/twitter Titles: Live Review ZonASINHunter - Amazon ASIN Grabber Tool Ebay / Amazon Scraper Software - Scrape for Easy Profits In Seconds! Arbitrage Amazon ASIN Meta Data Scraper Selling Tool | How Scraping Price comparison, Stock Improve Dropship Ebay Dropshipping Software Tool Amazon Scraper - Profit Spy Version 1.6 Upgrade Dropshipping Software Tool Amazon Scraper Profit Spy Version 1.5 Amazon Product Scraper Demonstration Retail Arbitrage Software Ebay Amazon Scraper Profit Spy 1.7 amazon product scraper Dropship Software Experts :: The Amazon Scraper With eBay Calculator Quick Start Guide Tags: zonasinhunter, zon asin hunter, asin grabber, asin scraper, amazon asin scraper, amazon asin grabber, amazon, grab asin, asin, amazon asin list, amazon asin code, amazon asin collector ebay, amazon, arbitrage, dropship, dropshipping, scraper, scraping Amazon Data Scraper amazon product scraper, amazon web scraper, amazon scraper plugin, amazon tool kit, web scrape, website scrapper, data extraction, web scraping software, data extraction software, screen scraping, web scraping, web scraper, scrap prices, amazon tools, amazon, amazon products, amazon scraper, amazon scrape, amazon scrap, amazon listing, Data Extraction, ebay, drop ship, dropship, drop shipping, drop shipping ebay, comparison, price compare ebay scraper software, ebay software, ebay listing software, ebay scraper, find products to sell on ebay, find items to sell on ebay, ebay scraping software, ebay scraping, finding items to sell on ebay, how to find items to sell on ebay dropshipping software, amazon scraper software, retail arbitrage software, dropshipping tool, dropshipping application, amazon scraping software, dropshipping scraper, dropshipper software, dropshipper applications, buy amazon sell ebay, scrape amazon products DParser, Scraping, Data, Parsing ebay scraper, ebay software, ebay software tool, retail arbitrage software, retail arbitrage, chris green retail arbitrage, amazon software, amazone software tool, amazon dropshipping tool, amazon dropshipping, amazon retail arbitrage, online arbitrage software, online arbitrage software tool amazon scrapers, scraper dropship software, dropshipping, drop ship, clearance, liquidation, residual, liquidator, DropshipPriceTracker.com, DropshipPriceTracker, dropshipping on ebay from amazon, drop shipping software, Amazon Scraper, Ebay Power Seller, Homebased Business, Dropshipping Tools, DS Domination Tools, DS Domination Recomended Tool, What is drop shipping, Amazon Software, Dropshipping Software, Ebay Dropshipping
Web Based Information Retrieval Techniques V2
 
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Subject: Library and Information Science Paper: Information Storage and Retrieval Module:Web Based Information Retrieval Techniques V2 Content Writer:
Views: 1416 Vidya-mitra
TBYI: Data Mining
 
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Think Before You Ink is a video mini-series created by the University of British Columbia's Digital Tattoo project that aims to raise awareness among the general public about issues surrounding digital identity and citizenship. Ever wonder how Amazon knew you'd want to buy that slap chop set? Or how Netflix predicted you'd love House of Cards before you even knew about it? Data Mining is the powerful technology behind this predictive magic. To learn more about data mining and how it impacts your daily life, watch the video above! And don't forget to visit our website at www.digitaltattoo.ubc.ca to learn more. Music offered by Syril: Licensed for public use. CC copyright. https://www.youtube.com/watch?v=BArOuD_UBGE
Online Data Collection: 10,000 Times More Efficient than the KGB?
 
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Complete Premium video at: http://fora.tv/conference/hsm_wif_2010 Andreas Weigend, former Chief Scientist for Amazon.com, discusses what he calls the "social data revolution." He explains that personal data collection is growing at such an exponential rate, that it's now 10,000 times more efficient than the KGB was 20 years ago. To view more highlights from the HSM World Innovation Forum 2010 series, visit http://www.youtube.com/view_play_list?p=88C0567991E989D6 ----- The world's greatest thought leaders in the field convene at the World Innovation Forum to provide actionable insights into the central issues at the heart of innovation today -- Marketing, Web 2.0, Health Care, Social Media, Design, Technology, Education, Green. Former Chief Scientist at Amazon.com Andreas Weigend on marketing and web 2.0: Marketing in the web 2.0: Beyond cutting costs and optimizing business processes What are the implications for new business models products and services? A world of abundance: Making the most of quantitative and qualitative data Social networks and the new uses of data: The power of social recommendations and behavioral targeting Lessons from the inside: What we can learn from Amazon Andreas Weigend, Amazon.com's Chief Scientist until January 2004, is a leading behavioral marketing expert. His career as a scientist, data strategist and quantitative methods innovator has enabled him to bridge the gap between industry and academia. As the Chief Scientist of Amazon.com, he developed data mining techniques including session-based marketing, and designed applications ranging from heuristic cross-selling to customer network and lifecycle analysis. Weigend currently teaches the graduate course Data Mining and Electronic Commerce at Stanford University.
Views: 3830 FORA.tv
How to Use AWS Sagemaker to Train You ML Models Faster
 
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Learn more about AWS Startups at – https://amzn.to/2HBQt0y Zalando is Europe's leading Fashion platform. They strive to democratize ML to give every service team the ability to use ML to improve the user experience. Kshiji Kumar (VP ML at Zalando) will present together with Vlad Zhukov (Director Sagemaker at AWS) how Sagemaker helped the Zalando service teams with Time to market for their ML applications. Model training for forecasting or recommendation took several days to complete and with the help of Sagemaker distributed training, this has been reduced to hours whilst keeping the cost constant. This allows Zalando to experiment more often and iterate faster.
Views: 416 Amazon Web Services
Java For Text Mining and NLP with Stanford NLP
 
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This technical book aim to equip the reader with Java programming, Text Mining, and Natural Language Processing fundamentals in a fast and practical way. There will be many examples and explanations that are straight to the point. You will develop your own Text Mining Application at the end of the book. Contents 1. Introduction 2. Getting Started (Installing IDE, ...) 3. Language Essentials I (variables, data types, ...) 4. Language Essentials II (loops, if... else..., methods) 5. Object Essentials (classes, inheritance, polymorphism, encapsulation, ...) 6. Text Mining Essentials (Import Text Files, Text Transformation (lowercase, stopwords), Text Understanding (Stanford NLP), Text Classification (Stanford Classifier) ) 7. Conclusion Book: http://www.svbook.com Course: TBA
Views: 66 SVBook
Data Mining | Tutorial for Beginners [Part 8] | NoSQL  Database | Great Learning
 
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#DataMining | What is Data Mining? What are the applications of Data Mining? In this course, you will learn the basic concepts and fundamentals of Data Mining and more. About the Speaker: Raghu Raman A V Raghu is a Big Data and AWS expert with over a decade of training and consulting experience in AWS, Apache Hadoop Ecosystem including Apache Spark. He has worked with global customers like IBM, Capgemini, HCL, Wipro to name a few as well as Bay Area startups in the US. #BigData #DataMining #GreatLakes #GreatLearning About Great Learning: - Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. - Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM - What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U - Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670 - Want to know more about the careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU - For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1 - Learn More at: https://www.greatlearning.in/ For more updates on courses and tips follow us on: - Google Plus: https://plus.google.com/u/0/108438615307549697541 - Facebook: https://www.facebook.com/GreatLearningOfficial/ - LinkedIn: https://www.linkedin.com/company/great-learning/ - Follow our Blog: https://www.greatlearning.in/blog/?utm_source=Youtube Great Learning has collaborated with the University of Texas at Austin for the PG Program in Artificial Intelligence and Machine Learning and with UT Austin McCombs School of Business for the PG Program in Analytics and Business Intelligence.
Views: 128 Great Learning
Multilingual Text Mining: Lost in Translation, Found in Native Language Mining - Rohini Srihari
 
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There has been a meteoric rise in the amount of multilingual content on the web. This is primarily due to social media sites such as Facebook, and Twitter, as well as blogs, discussion forums, and reader responses to articles on traditional news sites. Language usage statistics indicate that Chinese is a very close second to English, and could overtake it to become the dominant language on the web. It is also interesting to see the explosive growth in languages such as Arabic. The availability of this content warrants a discussion on how such information can be effectively utilized. Such data can be mined for many purposes including business-related competitive insight, e-commerce, as well as citizen response to current issues. This talk will begin with motivations for multilingual text mining, including commercial and societal applications, digital humanities applications such as semi-automated curation of online discussion forums, and lastly, government applications, where the value proposition (benefits, costs and value) is different, but equally compelling. There are several issues to be touched upon, beginning with the need for processing native language, as opposed to using machine translated text. In tasks such as sentiment or behaviour analysis, it can certainly be argued that a lot is lost in translation, since these depend on subtle nuances in language usage. On the other hand, processing native language is challenging, since it requires a multitude of linguistic resources such as lexicons, grammars, translation dictionaries, and annotated data. This is especially true for "resourceMpoor languages" such as Urdu, and Somali, languages spoken in parts of the world where there is considerable focus nowadays. The availability of content such as multilingual Wikipedia provides an opportunity to automatically generate needed resources, and explore alternate techniques for language processing. The rise of multilingual social media also leads to interesting developments such as code mixing, and code switching giving birth to "new" languages such as Hinglish, Urdish and Spanglish! This phenomena exhibits both pros and cons, in addition to posing difficult challenges to automatic natural language processing. But there is also an opportunity to use crowd-sourcing to preserve languages and dialects that are gradually becoming extinct. It is worthwhile to explore frameworks for facilitating such efforts, which are currently very ad hoc. In summary, the availability of multilingual data provides new opportunities in a variety of applications, and effective mining could lead to better cross-cultural communication. Questions Addressed (i) Motivation for mining multilingual text. (ii) The need for processing native language (vs. machine translated text). (iii) Multilingual Social Media: challenges and opportunities, e.g., preserving languages and dialects.
Views: 1484 UA German Department
Netflix - 2014 Data Mining Techniques - Vrije University Amsterdam
 
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None-- Created using PowToon -- Free sign up at http://www.powtoon.com/ . Make your own animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 647 Yikang Wang
Product Aspect Ranking and Its Applications
 
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Huge collections of consumer reviews are now available on the Web expressing various opinions on multiple aspects of products. Among these aspects some are more important than others and have greater influence on consumers' decisions. This method uses aspect ranking to automatically identify important product aspects or features from online consumer reviews. Aspect ranking can be used in document-level sentiment classification and extractive review summarization achieving significant performance improvement on these applications.
Views: 1216 jz00hj
4. What is Integration (Hindi) |
 
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What is Integration and need for it.
Views: 398228 Lighthouse
Deep Learning Applications in Web Search & IR  - lecture by Ankit Bahuguna - Code Europe Spring 2017
 
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Deep Learning techniques are currently being employed to solve a variety of problems, delivering state of the art results. In this lecture, I will highlight its application in Web Search and Information Retrieval with a view of work done at Cliqz - A privacy focussed browser with Integrated Search.
Views: 172 Code Europe