Search results “Web mining recommender systems definition”
What is RECOMMENDER SYSTEM? What does RECOMMENDER SYSTEM mean? RECOMMENDER SYSTEM meaning - RECOMMENDER SYSTEM definition - RECOMMENDER SYSTEM explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Recommender systems or recommendation systems (sometimes replacing "system" with a synonym such as platform or engine) are a subclass of information filtering system that seek to predict the "rating" or "preference" that a user would give to an item. Recommender systems have become extremely common in recent years, and are utilized in a variety of areas: some popular applications include movies, music, news, books, research articles, search queries, social tags, and products in general. There are also recommender systems for experts, collaborators, jokes, restaurants, garments, financial services, life insurance, romantic partners (online dating), and Twitter pages. Recommender systems typically produce a list of recommendations in one of two ways – through collaborative and content-based filtering or the personality-based approach. Collaborative filtering approaches building a model from a user's past behavior (items previously purchased or selected and/or numerical ratings given to those items) as well as similar decisions made by other users. This model is then used to predict items (or ratings for items) that the user may have an interest in. Content-based filtering approaches utilize a series of discrete characteristics of an item in order to recommend additional items with similar properties. These approaches are often combined (see Hybrid Recommender Systems). The differences between collaborative and content-based filtering can be demonstrated by comparing two popular music recommender systems – Last.fm and Pandora Radio. Last.fm creates a "station" of recommended songs by observing what bands and individual tracks the user has listened to on a regular basis and comparing those against the listening behavior of other users. Last.fm will play tracks that do not appear in the user's library, but are often played by other users with similar interests. As this approach leverages the behavior of users, it is an example of a collaborative filtering technique. Pandora uses the properties of a song or artist (a subset of the 400 attributes provided by the Music Genome Project) in order to seed a "station" that plays music with similar properties. User feedback is used to refine the station's results, deemphasizing certain attributes when a user "dislikes" a particular song and emphasizing other attributes when a user "likes" a song. This is an example of a content-based approach. Each type of system has its own strengths and weaknesses. In the above example, Last.fm requires a large amount of information on a user in order to make accurate recommendations. This is an example of the cold start problem, and is common in collaborative filtering systems. While Pandora needs very little information to get started, it is far more limited in scope (for example, it can only make recommendations that are similar to the original seed). Recommender systems are a useful alternative to search algorithms since they help users discover items they might not have found by themselves. Interestingly enough, recommender systems are often implemented using search engines indexing non-traditional data. Montaner provided the first overview of recommender systems from an intelligent agent perspective. Adomavicius provided a new, alternate overview of recommender systems. Herlocker provides an additional overview of evaluation techniques for recommender systems, and Beel et al. discussed the problems of offline evaluations. Beel et al. have also provided literature surveys on available research paper recommender systems and existing challenges.
Views: 2173 The Audiopedia
How Big Data Is Used In Amazon Recommendation Systems | Big Data Application & Example | Simplilearn
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: 27372 Simplilearn
Book Recommendation System Based on Filtering and Association Rule Mining
Recommendation systems are widely used to recommend products to the end users that are most appropriate. Online book selling websites now-a-days are competing with eachother by many means.Recommendation system is one of the stronger tools to increase profit and retaining buyer. The book recommendation system must recommend books that are of buyer’s interest. This paper presents book recommendation system based on combined features of content filtering, collaborative filtering and association rule mining.
Views: 1717 Final Year Solutions
Movie Recommendation System with RapidMiner (in Turkish)
The csv files and xml files of the processes can be downloaded from following link: https://github.com/inancarin/RapidMiner/tree/master/Recommendation%20System
Views: 1638 İnanç Arın
What is INFORMATION FILTERING SYSTEM? What does INFORMATION FILTERING SYSTEM mean? INFORMATION FILTERING SYSTEM meaning - INFORMATION FILTERING SYSTEM definition - INFORMATION FILTERING SYSTEM explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ An information filtering system is a system that removes redundant or unwanted information from an information stream using (semi)automated or computerized methods prior to presentation to a human user. Its main goal is the management of the information overload and increment of the semantic signal-to-noise ratio. To do this the user's profile is compared to some reference characteristics. These characteristics may originate from the information item (the content-based approach) or the user's social environment (the collaborative filtering approach). Whereas in information transmission signal processing filters are used against syntax-disrupting noise on the bit-level, the methods employed in information filtering act on the semantic level. The range of machine methods employed builds on the same principles as those for information extraction. A notable application can be found in the field of email spam filters. Thus, it is not only the information explosion that necessitates some form of filters, but also inadvertently or maliciously introduced pseudo-information. On the presentation level, information filtering takes the form of user-preferences-based newsfeeds, etc. Recommender systems and content discovery platforms are active information filtering systems that attempt to present to the user information items (film, television, music, books, news, web pages) the user is interested in. These systems add information items to the information flowing towards the user, as opposed to removing information items from the information flow towards the user. Recommender systems typically use collaborative filtering approaches or a combination of the collaborative filtering and content-based filtering approaches, although content-based recommender systems do exist.
Views: 470 The Audiopedia
Lecture 93 —  Spam Farms | Mining of Massive Datasets | Stanford University
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What is COLLABORATIVE FILTERING? What does COLLABORATIVE FILTERING mean? COLLABORATIVE FILTERING meaning - COLLABORATIVE FILTERING definition - COLLABORATIVE FILTERING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
Views: 1140 The Audiopedia
Recommender Systems
http://www.techgig.com/expert-speak/Recommender-Systems-243 In simple words, Recommender Systems are software tools and techniques, for suggesting items to a user. Recommender systems can be implemented to show generalized or personalized recommendations to the user on your e-commerce/socializing websites. For recommendation, system gathers data relevant to the user and processes that data using different algorithms to make recommendations. Recommender systems have proven to be valuable means for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce.
Views: 5798 TechGig
Personalized Music Recommendation by Mining Social Media Tags
Title: Personalized Music Recommendation by Mining Social Media Tags Domain: Data Mining Description: Over the past few years, the recommender system has been proposed as a critical role to help users choose the preferred product from a massive amount of data. For music recommendation, most recent recommender systems made attempts to associate music with the user’s preferences primarily based on user ratings. However, this kind of recommendation mechanism encounters the problem called rating diversity that makes the prediction results unreliable. To cope with this problem, in this paper, we propose a novel music recommendation approach that utilizes social media tags instead of ratings to calculate the similarity between music pieces. Through the proposed tag-based similarity, the user preferences hidden in tags can be inferred effectively. The empirical evaluations on real social media datasets reveal that our proposed approach using social tags outperforms the existing ones using only ratings in terms of predicting the user’s preferences to music. Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. contact us for more details: 044-43548566,8110081181 [email protected]
Web page recommendation based on web usage and domain knowledge
Title: Web page recommendation based on web usage and domain knowledge Domain: Data Mining For more details contact: E-Mail: [email protected] Purchase The Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2017 – 2018 data mining projects 5. 2017 – 2018 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2017 – 2018 ieee titles 8. 2017 – 2018 base paper 9. 2017 – 2018 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2017 – 2018 data mining weka projects 13. 2017 – 2018 b.e projects 14. 2017 – 2018 m.e projects 15. 2017 – 2018 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2017 – 2018 ieee base paper free download 23. 2017 – 2018 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique 39. data mining approaches for intrusion detection 40. data mining in ranking system using weka tool 41. data mining projects using weka 42. data mining in bioinformatics using weka 43. data mining using weka tool 44. data mining tool weka tutorial 45. data mining abstract 46. data mining base paper 47. data mining research papers 2017 - 2018 48. 2017 - 2018 data mining research papers 49. 2017 data mining research papers 50. data mining IEEE Projects 52. data mining and text mining ieee projects 53. 2017 text mining ieee projects 54. text mining ieee projects 55. ieee projects in web mining 56. 2017 web mining projects 57. 2017 web mining ieee projects 58. 2017 data mining projects with source code 59. 2017 data mining projects for final year students 60. 2017 data mining projects in java 61. 2017 data mining projects for students
Lecture 15 — Topic Mining and Analysis  Term as Topic | UIUC
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A Multi-Armed Bandit Framework for Recommendations at Netflix | DataEngConf SF '18
Don’t miss the next DataEngConf in Barcelona: https://dataeng.co/2O0ZUq7 Download Slides: https://dataeng.co/2sied03 ABOUT THE TALK: In this talk, we will present a general multi-armed bandit framework for recommending titles to our 117M+ members on the Netflix homepage. A key aspect of our framework is closed loop attribution to link how our members respond to a recommendation. Our framework performs frequent updates of policies using user feedback collected from a past time interval window. We will take deeper look at the system architecture. We will illustrate the use of that framework by focusing on two example policies – a greedy exploit policy which maximize the probability a user will play a title and an incrementality-based policy. The latter is a novel online learning approach that takes the causal effect of a recommendation into account. An incrementality-based policy recommends titles that brings about the maximum increase in a specific quantity of interest, such as engagement. This helps discount the effect of recommendations when a user would have played anyway. We describe offline experiments and online A/B test results for both of these example policies. ABOUT THE SPEAKERS: Jaya Kawale is a Senior Research Scientist at Netflix working on problems related to targeting and recommendations. She received her PhD in Computer Science from the University of Minnesota and has published research papers at several top-tier conferences. Her main areas of interest are large scale machine learning and data mining. Elliot is a software engineer at Netflix on the Personalization Infrastructure team. Currently, he builds big data systems for personalizing recommendations for Netflix subscribers, using a variety of technologies including Scala, Spark/Spark Streaming, Kafka, and Cassandra. He graduated from UC Berkeley (B.S.) and Stanford (M.S.) and has previously worked at eBay and Apple. Follow DataEngConf on: Twitter: https://twitter.com/dataengconf LinkedIn: https://www.linkedin.com/company/hakkalabs Facebook: https://web.facebook.com/hakkalabs
Views: 2417 Data Council
Data Mining using R | R Tutorial for Beginners | Data Mining Tutorial for Beginners 2018 | ExcleR
Data Mining Using R (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. Data Mining Certification Training Course Content : https://www.excelr.com/data-mining/ Introduction to Data Mining Tutorials : https://youtu.be/uNrg8ep_sEI What is Data Mining? Big data!!! Are you demotivated when your peers are discussing about data science and recent advances in big data. Did you ever think how Flip kart and Amazon are suggesting products for their customers? Do you know how financial institutions/retailers are using big data to transform themselves in to next generation enterprises? Do you want to be part of the world class next generation organisations to change the game rules of the strategy making and to zoom your career to newer heights? Here is the power of data science in the form of Data mining concepts which are considered most powerful techniques in big data analytics. Data Mining with R unveils underlying amazing patterns, wonderful insights which go unnoticed otherwise, from the large amounts of data. Data mining tools predict behaviours and future trends, allowing businesses to make proactive, unbiased and scientific-driven decisions. Data mining has powerful tools and techniques that answer business questions in a scientific manner, which traditional methods cannot answer. Adoption of data mining concepts in decision making changed the companies, the way they operate the business and improved revenues significantly. Companies in a wide range of industries such as Information Technology, Retail, Telecommunication, Oil and Gas, Finance, Health care are already using data mining tools and techniques to take advantage of historical data and to create their future business strategies. Data mining can be broadly categorized into two branches i.e. supervised learning and unsupervised learning. Unsupervised learning deals with identifying significant facts, relationships, hidden patterns, trends and anomalies. Clustering, Principle Component Analysis, Association Rules, etc., are considered unsupervised learning. Supervised learning deals with prediction and classification of the data with machine learning algorithms. Weka is most popular tool for supervised learning. Topics You Will Learn… Unsupervised learning: Introduction to datamining Dimension reduction techniques Principal Component Analysis (PCA) Singular Value Decomposition (SVD) Association rules / Market Basket Analysis / Affinity Filtering Recommender Systems / Recommendation Engine / Collaborative Filtering Network Analytics – Degree centrality, Closeness Centrality, Betweenness Centrality, etc. Cluster Analysis Hierarchical clustering K-means clustering Supervised learning: Overview of machine learning / supervised learning Data exploration methods Basic classification algorithms Decision trees classifier Random Forest K-Nearest Neighbours Bayesian classifiers: Naïve Bayes and other discriminant classifiers Perceptron and Logistic regression Neural networks Advanced classification algorithms Bayesian Networks Support Vector machines Model validation and interpretation Multi class classification problem Bagging (Random Forest) and Boosting (Gradient Boosted Decision Trees) Regression analysis Tools You Will Learn… R: R is a programming language to carry out complex statistical computations and data visualization. R is also open source software and backed by large community all over the world who are contributing to enhancing the capability. R has many advantages over other tools available in the market and it has been rated No.1 among the data scientist community. Mode of Trainings : E-Learning Online Training ClassRoom Training --------------------------------------------------------------------------- For More Info Contact :: Toll Free (IND) : 1800 212 2120 | +91 80080 09704 Malaysia: 60 11 3799 1378 USA: 001-608-218-3798 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com
information system project
Recommender System for a movie streaming service by using RapidMiner Extension
Views: 233 Samiur Rahman
GoodReads Book Recommender System
It's a recommender system to recommend books using various algorithms. It uses GoodReads books data from kaggle. Our aim is to recommend user books from the huge collections of books available that he must not have read and would like to read next based on his previous book choices. This is a good problem to solve because many recommender systems have been created to suggest various products on e-commerce sites and movie-related sites, But no one has worked on such large data of books to recommend books. Viewing it from the user's perspective, this idea will reduce user’s effort to search the books to be read next that are inclined towards his interest. Presentation Link : goo.gl/6iJpjv
Views: 118 kanika narang
Lecture 55 — Latent Factor Recommender System  | Stanford University
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Web Personalization based on Usage Mining part 2
By : Ahmed Hamdy Ali
Views: 245 Ahmed Emara
Building Recommendation Systems in Azure - Machine Learning and recommenders
http://www.arewa.video Building Recommendation Systems in Azure - Machine Learning and recommenders
Word Recommendation System for Hindi (Thesis Project Demo)
Word Recommendation System for Hindi Using N-gram approximation
Views: 334 Prem Yadav
Implementation of Placement Training Recommendation system
Different colleges may be using different kind placement training , This video shows the development of a system which recommends the best placement training policies from the current placement training's available in different colleges using web scraping and text mining techniques .
IS project video
Recommender System for a movie streaming service by using RapidMiner Extension
Views: 60 Samiur Rahman
Reputation Measurement and Malicious Feedback Rating Prevention in Web Service Recommendation System
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Views: 271 siva kumar
Mining Web graph Recommendations
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Views: 394 finalsemprojects
Lecture 94 — Trust Rank | Mining of Massive Datasets | Stanford University
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Jure Leskovec: "Large-scale Graph Representation Learning"
New Deep Learning Techniques 2018 "Large-scale Graph Representation Learning" Jure Leskovec, Stanford University Abstract: Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. However, traditionally machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph. In this talk I will discuss methods that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. I will provide a conceptual review of key advancements in this area of representation learning on graphs, including random-walk based algorithms, and graph convolutional networks. Institute for Pure and Applied Mathematics, UCLA February 7, 2018 For more information: http://www.ipam.ucla.edu/programs/workshops/new-deep-learning-techniques/?tab=overview
64 Cosine Similarity Example
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Views: 40736 Oresoft LWC
Travel recommendation by mining people attributes
Project by V.Thanigaivel VM5416 T.Arivazhagan VM5402 V.V.Vivek VM5410 The main abstract of this project is concerned with recommendation of places for a user based upon the hugely available community contributed pictures and people attributes. Leveraging community-contributed data (e.g., blogs, GPS logs, and geo-tagged photos) for personalized recommendation is one of the active research problems since there are rich contexts and human activities in such explosively growing data. In this work, we focus on personalized travel recommendation and show promising applications by leveraging the freely available community-contributed photos. We propose to conduct personalized travel recommendation by further considering specific user profiles or attributes (e.g., gender) as well as travel group types (e.g., family & friends, couple). Instead of mining photo logs only, we exploit the automatically detected people attributes. This is a very useful project for people who loves long travel and want to visit innovative places. By using the matlab we are detecting the gender and number of people who went for travel to that place.
Views: 229 Thanigai Vel V
Click Stream Data Analysis
This video about how clickstream data is gonna helpful in the e-commerce business
Views: 1275 Jayanth Gowda
Naive Bayes Classifier | Naive Bayes Algorithm | Naive Bayes Classifier With Example | Simplilearn
This Naive Bayes Classifier tutorial video will introduce you to the basic concepts of Naive Bayes classifier, what is Naive Bayes and Bayes theorem, conditional probability concepts used in Bayes theorem, where is Naive Bayes classifier used, how Naive Bayes algorithm works with solved examples, advantages of Naive Bayes. By the end of this video, you will also implement Naive Bayes algorithm for text classification in Python. The topics covered in this Naive Bayes video are as follows: 1. What is Naive Bayes? ( 01:06 ) 2. Naive Bayes and Machine Learning ( 05:45 ) 3. Why do we need Naive Bayes? ( 05:46 ) 4. Understanding Naive Bayes Classifier ( 06:30 ) 5. Advantages of Naive Bayes Classifier ( 20:17 ) 6. Demo - Text Classification using Naive Bayes ( 22:36 ) To learn more about Machine Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the Slides here: https://goo.gl/Cw9wqy #NaiveBayes #MachineLearningAlgorithms #DataScienceCourse #DataScience #SimplilearnMachineLearning - - - - - - - - Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer Why learn Machine Learning? Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems The Machine Learning Course is recommended for: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Naive-Bayes-Classifier-l3dZ6ZNFjo0&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 27957 Simplilearn
Predicting Stock Prices - Learn Python for Data Science #4
In this video, we build an Apple Stock Prediction script in 40 lines of Python using the scikit-learn library and plot the graph using the matplotlib library. The challenge for this video is here: https://github.com/llSourcell/predicting_stock_prices Victor's winning recommender code: https://github.com/ciurana2016/recommender_system_py Kevin's runner-up code: https://github.com/Krewn/learner/blob/master/FieldPredictor.py#L62 I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Stock prediction with Tensorflow: https://nicholastsmith.wordpress.com/2016/04/20/stock-market-prediction-using-multi-layer-perceptrons-with-tensorflow/ Another great stock prediction tutorial: http://eugenezhulenev.com/blog/2014/11/14/stock-price-prediction-with-big-data-and-machine-learning/ This guy made 500K doing ML stuff with stocks: http://jspauld.com/post/35126549635/how-i-made-500k-with-machine-learning-and-hft Please share this video, like, comment and subscribe! That's what keeps me going. and please support me on Patreon!: https://www.patreon.com/user?u=3191693 Check out this youtube channel for some more cool Python tutorials: https://www.youtube.com/watch?v=RZF17FfRIIo Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 517168 Siraj Raval
Anmol Bhasin on Recommendation Engines
There are two common technologies known as recommendation engine: Content Based Recommendation and Collaborative Filtering. Learn what's different from Anmol Bhasin.
Views: 2179 Lutz Finger
Part 1:  An intro to How we Make Smart Online Product Recommendations
How we make smart online product recommendations using collaborative Filtering and Market Basket Analysis
Views: 1124 Perry Drake
cse494 f11 week12 1 1
CSE494 Information Integration, Retrieval and Mining, Fall 2011 http://rakaposhi.eas.asu.edu/cse494 The theory behind NBC learning (in terms of maximizing likelihood). NBC applied to Text--unigram model. Feature selection using mutual information. connection between feature selection and LSI and LDA. Recommendation systems. Content-based filtering and application of naive bayes classifier to vector of bags model of text. Collaborative filtering and its relative tradeoffs vis a vis content-based filtering.
IU X-Informatics Unit 12: Lesson 1: Recommender Systems as an Optimization Problem
Lesson Overview: We define a set of general recommender systems as matching of items to people or perhaps collections of items to collections of people where items can be other people, products in a store, movies, jobs, events, web pages etc. We present this as ''yet another optimization problem'' Enroll in this course athttps://bigdatacourse.appspot.com/ and download course material, see information on badges and more. It's all free and only takes you a few seconds.
Text Mining the Contributors to Rail Accidents
Text Mining the Contributors to Rail Accidents 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: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com Rail accidents represent an important safety concern for the transportation industry in many countries. In the 11 years from 2001 to 2012, the U.S. had more than 40 000 rail accidents that cost more than $45 million. While most of the accidents during this period had very little cost, about 5200 had damages in excess of $141 500. To better understand the contributors to these extreme accidents, the Federal Railroad Administration has required the railroads involved in accidents to submit reports that contain both fixed field entries and narratives that describe the characteristics of the accident. While a number of studies have looked at the fixed fields, none have done an extensive analysis of the narratives. This paper describes the use of text mining with a combination of techniques to automatically discover accident characteristics that can inform a better understanding of the contributors to the accidents. The study evaluates the efficacy of text mining of accident narratives by assessing predictive performance for the costs of extreme accidents. The results show that predictive accuracy for accident costs significantly improves through the use of features found by text mining and predictive accuracy further improves through the use of modern ensemble methods. Importantly, this study also shows through case examples how the findings from text mining of the narratives can improve understanding of the contributors to rail accidents in ways not possible through only fixed field analysis of the accident reports.
Views: 654 jpinfotechprojects
Text Mining
Views: 311 Quantiphi Data Lab
Web search 2: big data beats clever algorithms
A simple algorithm operating on lots of data will often outperform a more clever algorithm working with a sample. We illustrate this on the Question Answering (QA) task, where a simple algorithm (rewriting the question into web queries) outperformed systems based on sophisticated linguistic analysis.
Views: 1649 Victor Lavrenko
What is TERMINOLOGY EXTRACTION? What does TERMINOLOGY EXTRACTION mean? TERMINOLOGY EXTRACTION meaning - TERMINOLOGY EXTRACTION definition - TERMINOLOGY EXTRACTION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Terminology extraction (also known as term extraction, glossary extraction, term recognition, or terminology mining) is a subtask of information extraction. The goal of terminology extraction is to automatically extract relevant terms from a given corpus. In the semantic web era, a growing number of communities and networked enterprises started to access and interoperate through the internet. Modeling these communities and their information needs is important for several web applications, like topic-driven web crawlers, web services, recommender systems, etc. The development of terminology extraction is essential to the language industry. One of the first steps to model the knowledge domain of a virtual community is to collect a vocabulary of domain-relevant terms, constituting the linguistic surface manifestation of domain concepts. Several methods to automatically extract technical terms from domain-specific document warehouses have been described in the literature. Typically, approaches to automatic term extraction make use of linguistic processors (part of speech tagging, phrase chunking) to extract terminological candidates, i.e. syntactically plausible terminological noun phrases, NPs (e.g. compounds "credit card", adjective-NPs "local tourist information office", and prepositional-NPs "board of directors" - in English, the first two constructs are the most frequent). Terminological entries are then filtered from the candidate list using statistical and machine learning methods. Once filtered, because of their low ambiguity and high specificity, these terms are particularly useful for conceptualizing a knowledge domain or for supporting the creation of a domain ontology or a terminology base. Furthermore, terminology extraction is a very useful starting point for semantic similarity, knowledge management, human translation and machine translation, etc. The methods for terminology extraction can be applied to parallel corpora. Combined with e.g. co-occurrence statistics, candidates for term translations can be obtained. Bilingual terminology can be extracted also from comparable corpora (corpora containing texts within the same text type, domain but not translations of documents between each other).
Views: 660 The Audiopedia
Dynamic Business Logic with Machine Learning & Deep Learning: Letting Your Data Build Your System
Advancements in data and analytics, hardware acceleration, and advanced libraries and services in Machine Learning and Deep Learning have unleashed the power to learn your business logic rather than “try to code" for it. In this session, we’ll dive into design paradigms and architectures that allow you to drive your logic from your data and add intelligence to your applications. The session will describe the key ways customers build intelligent AI systems starting with AWS AI Services, then platforms, and lastly libraries.
Views: 386 Amazon Web Services
Orchestrating the Intelligent Web with Apache Mahout
Presenter(s): Aneesha Bakharia URL: http://2011.linux.conf.au/programme/schedule/view_talk/213 Presenters: Aneesha Bakharia ([email protected]) and Aaron Tan ([email protected]) It is becoming increasingly important to incorporate “collective intelligence” within web, mobile and business intelligence applications. Traditionally the implementation of algorithms capable of adding intelligence to an application either required a highly specialised knowledge of machine learning or was extremely costly. Apache Mahout is one of the first open source and scalable machine learning libraries that seeks to mainstream the use of machine learning. This presentation will focus on providing the audience with a practical understanding of the algorithms included in Apache Mahout and how they can be used to provide insight into the patterns that exist in large amounts of data? Text clustering with the Latent Dirichlet Algorithm will also be covered. The Apache Mahout library consists of scalable machine learning algorithms for data mining tasks that encompass classification (Naïve Bayes and Support Vector Machines), clustering (k­means, Expectation Maximization, Mean Shift, Latent Dirichlet Allocation and Hierarchical Clustering), recommendation (collaborative filtering) and frequent pattern mining (parallel fp-growth). As of the 0.3 release, an impressive total of 25 machine learning algorithms have been implemented. Apache Mahout achieves scalability by leveraging Apache Hadoop which implements the MapReduce parallel processing paradigm that was first made popular by Google. Latent Dirichlet Allocation is a relatively new algorithm first introduced in 2003 with a suggested use in Topic Modeling (text clustering). Unlike generic clustering algorithms such as k-means, Latent Dirichlet Allocation is able to model document overlap. Latent Dirichlet Allocation is not a hard clustering algorithm and is able to map documents and words to multiple clusters. This feature is a natural fit for documents, which usually discuss multiple topics. The Latent Dirichlet Allocation algorithm simultaneously groups both documents and words into clusters. This is a useful feature as the main words belonging to a cluster and the prominent documents within a cluster are both output by the algorithm. Twitter recently released a feature called Lists that allows you to group people you follow and view the timeline of Tweets for users in a List separately. We will use the Latent Dirichlet Allocation algorithm to cluster people you follow and suggest Lists for Twitter. This will serve as a practical overview of using Apache Mahout for clustering. The following topics of interest are: - What is machine learning? - What is Apache Mahout? - Who is using Apache Mahout? - The MapReduce paradigm - Machine learning with Apache Mahout - Clustering with Apache Mahout - Classification with Apache Mahout - Collaborative Filtering with Apache Mahout - Frequent Pattern Mining with Apache Mahout - Processing Large Datasets with Multiple Cluster Nodes - Building a Twitter List recommendation application with the Latent Dirichlet Allocation algorithm http://2011.linux.conf.au/ - http://www.linux.org.au CC BY-SA - http://creativecommons.org/licenses/by-sa/4.0/legalcode.txt
IMDB Movie Popularity Prediction
Data Mining & Machine Learning Project
Views: 789 Rohan Kavade
Building a Recommendation Engine with Machine Learning Techniques (Brian Sam-Bodden) - FSF 2016
In this talk Brian will walk you through the ideas, techniques and technologies used to build a SaaS Recommendation Engine. From building an efficient software classifier, to storing the large amounts of data required, to the pipeline of artificial intelligence and machine learning algorithms used. The system is being built with a myriad of technologies including Java, Cassandra, Ruby, Rails, Clojure, Javascript and more. About the speaker: Brian Sam-Bodden is an author, instructor, speaker and hacker that has spent most of his life (adult and otherwise) crafting software. He is well versed in several programming languages and has a deep passion for Machine Learning and Artificial Intelligence in general. His perfect Sunday includes walks on a virtual beach with his pals of HAL, DeepThought, the Architect and MCP. Brian lives in the post apocalyptic, waste-land, Mad-Max’esque state of Arizona, U.S.A. where he leads Integrallis; a Polyglot Consultancy and is also the founder of Binnacle (http://binnacle.io) a multi-purpose dashboard for web applications. Talk given at Full Stack Fest 2016 (https://www.fullstackfest.com)
Views: 2565 Codegram Technologies

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