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Data Mining For Automated Personality Classification
 
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Get this project at http://nevonprojects.com/data-mining-for-automated-personality-classification-2/ Here we use data mining algorithm to mine a training data set for automated human personality classification.
Views: 4542 Nevon Projects
Signal Processing and Machine Learning Techniques for Sensor Data Analytics
 
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Free MATLAB Trial: https://goo.gl/yXuXnS Request a Quote: https://goo.gl/wNKDSg Contact Us: https://goo.gl/RjJAkE Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe ------------------------------------------------------------------------- An increasing number of applications require the joint use of signal processing and machine learning techniques on time series and sensor data. MATLAB can accelerate the development of data analytics and sensor processing systems by providing a full range of modelling and design capabilities within a single environment. In this webinar we present an example of a classification system able to identify the physical activity that a human subject is engaged in, solely based on the accelerometer signals generated by his or her smartphone. We introduce common signal processing methods in MATLAB (including digital filtering and frequency-domain analysis) that help extract descripting features from raw waveforms, and we show how parallel computing can accelerate the processing of large datasets. We then discuss how to explore and test different classification algorithms (such as decision trees, support vector machines, or neural networks) both programmatically and interactively. Finally, we demonstrate the use of automatic C/C++ code generation from MATLAB to deploy a streaming classification algorithm for embedded sensor analytics.
Views: 10920 MATLAB
Heart Disease Prediction Project
 
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Get this project kit at http://nevonprojects.com/heart-disease-prediction-project/ System allows user to predict heart disease by users symptoms using data mining
Views: 27144 Nevon Projects
Human Activity Recognition (Predictive Modelling with Data Mining)
 
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Predicted human activity based on tri-axial accelerometer data worn by 4 healthy individuals on 4 different positions, over the span of 8 hours. Carried out multinomial classification of human activities into 5 classes - sitting, sitting-down, standing, standing-up and walking using the K-Nearest Neighbors model (best performing apart from Naive Bayes and Random Forests classifiers). Fabricated and discovered additional features of the accelerometer for better detecting change-points in human activities (transition from one activity to another) with maximum accuracy and minimum latency. Analyzed overlapping and non-overlapping sliding windows of different sizes of the raw data in order to exploit its temporal nature and performed Principal Component Analysis for dimensionality reduction. Provided business recommendations such as the single best and combination of positions to wear the accelerometer.
Views: 1261 Naval Katoch
Neural Network in Data Mining
 
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Analysis Of Neural Networks in Data Mining by, Venkatraam Balasubramanian Master's in Industrial and Human Factor Engineering
Views: 4103 prasana sarma
Machine Learning - Supervised VS Unsupervised Learning
 
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Enroll in the course for free at: https://bigdatauniversity.com/courses/machine-learning-with-python/ Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning. This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each. Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed! Explore many algorithms and models: Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction. Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests. Get ready to do more learning than your machine! Connect with Big Data University: https://www.facebook.com/bigdatauniversity https://twitter.com/bigdatau https://www.linkedin.com/groups/4060416/profile ABOUT THIS COURSE •This course is free. •It is self-paced. •It can be taken at any time. •It can be audited as many times as you wish. https://bigdatauniversity.com/courses/machine-learning-with-python/
Views: 54483 Cognitive Class
Learning From Data (Data Mining) Presentation
 
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This video explain how data processes in machine and how the machine learn from human. Machine Learning Artificial Intelligence Data Mining Information Technology Data Techniques Technology 2017 Machine Learning Human learning Data Process Algorithms Learning Algorithms Machine Learning Algorithms
Views: 483 M Rukhshan Ali
Smart Health Prediction Using Data Mining
 
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Get the project at http://nevonprojects.com/smart-health-prediction-using-data-mining/ A smart system that suggests a persons disease and suggestions to cure based on his symptoms, also has online doctor to consult for further treatment and cure.
Views: 32518 Nevon Projects
Data mining projects using weka
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-projects-uk/
Views: 2524 PHD Projects
Data Mining with Weka (4.6: Ensemble learning)
 
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Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 6: Ensemble learning http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/augc8F https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 20705 WekaMOOC
Human Genome Data Mining in Excel
 
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Steps importing the Human Genome into excel Pivot Tables - more to come in 2014
Views: 251 Excel Sprapps
Data Mining Team C
 
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Human Activity Recognition Using R
Views: 16 shameel mohamed
Graph Mining for Log Data Presented by David Andrzejewski
 
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This talk discusses a few ways in which machine learning techniques can be combined with human guidance in order to understand what the logs are telling us. Sumo Training: https://www.sumologic.com/learn/training/
Views: 1782 Sumo Logic, Inc.
Machine Learning in R - Supervised vs. Unsupervised
 
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Learn the basics of Machine Learning with R. Start our Machine Learning Course for free: https://www.datacamp.com/courses/introduction-to-machine-learning-with-R In the previous video, you learned about three machine learning techniques: Classification, Regression and Clustering. As you might have felt, there are quite some similarities between Classification and Regression. For both, you try to find a function, or a model, which can later be used to predict labels or values for unseen observations. It is important that during the training of the function, labeled observations are available to the algorithm. We call these techniques supervised learning. Labeling can be a tedious work and is often done by puny humans. There are other techniques which don't require labeled observations to learn from data. These techniques are called unsupervised learning. You've already acquainted yourself with one of these techniques in the previous video, namely Clustering. Clustering will find groups of observations that are similar, and thus does not require specific labeled observations. In the next chapter we'll talk about assessing the performance of your trained model. In supervised learning, we can use the real labels of the observations and compare them with the labels we predicted. It's quite straightforward that you want your model's predictions to be as similar as possible to the real labels. With unsupervised learning, however, measuring the performance gets more difficult: we don't have real labels to compare anything to. You'll learn some neat techniques to assess the quality of a clustering in the next chapter. As you get more experienced as a data scientist, you might notice that things aren't always black and white. In machine learning, some techniques overlap between supervised and unsupervised learning. With semi-supervised learning, for example, you can have alot of observations which are not labeled, and a few which are. You can then first perform clustering to group all observations which are similar. Afterwards, you can use information about the clusters and about the few labeled observations to assign a class to unlabeled observations. This will give you more labeled observations to perform supervised learning on. Enough talking, let's do some more exercises!
Views: 30819 DataCamp
Time Series Classification Using Wavelet Scattering Transform
 
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This is a ~3-minute video highlight produced by undergraduate students Charlie Tian and Christina Coley regarding their research topic during the 2017 AMALTHEA REU Program at Florida Institute of Technology in Melbourne, FL. They were mentored by doctoral student Kaylen Bryan and professor Dr. Adrian Peter (Engineering Systems Department). More details about their project can be found at http://www.amalthea-reu.org.
Intelligent Heart Disease Prediction System Using Data Mining Techniques || in Bangalore
 
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The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not “mined” to discover hidden information for effective decision making. Discovery of hidden patterns and relationships often goes unexploited. Advanced data mining techniques can help remedy this situation. This research has developed a prototype Intelligent Heart Disease Prediction System (IHDPS) using data mining techniques, namely, Decision Trees, Naïve Bayes and Neural Network. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. IHDPS can answer complex “what if” queries which traditional decision support systems cannot. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease, to be established. IHDPS is Web-based, user-friendly, scalable, reliable and expandable. It is implemented on the .NET platform.
Educational Data Mining: Predict the Future, Change the Future
 
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Teachers College is proud to introduce the 2012-13 Julius and Rosa Sachs Distinguished Lecturer Professor Ryan Baker, Columbia University. Ryan Shaun Joazeiro de Baker is Visiting Associate Professor in the Department of Human Development. He earned his Ph.D. in Human-Computer Interaction from Carnegie Mellon University, and was a post-doctoral fellow in the Learning Sciences at the University of Nottingham. He earned his Bachelor's Degree (Sc.B.) in Computer Science from Brown University. Dr. Baker has been Assistant Professor of Psychology and the Learning Sciences at Worcester Polytechnic Institute. He previously served as the first Technical Director of the Pittsburgh Science of Learning Center DataShop, the largest public repository for data on the interaction between learners and educational software. He is currently serving as the founding President of the International Educational Data Mining Society, and as Associate Editor of the Journal of Educational Data Mining. His research combines educational data mining, learning analytics and quantitative field observation methods in order to better understand how students respond to educational software, and how these responses impact their learning. He studies these issues within intelligent tutors, simulations, and educational games. In recent years, he and his colleagues have developed strategies to make inferences in real-time about students' motivation, meta-cognition, affect, and robust learning.
Machine Learning for Real Time Poses Classification Using Kinect Skeleton Data (2016)
 
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Realtime human poses classification technique, by using skeleton data provided by the Kinect sensor. Different users performed a set of tasks from a vocabulary of eighteen poses. From skeleton data of each pose, twenty features are extracted so that they are invariant with respect to the user size and its position in the scene. We then compared the generalization performances of four machine learning algorithms; support vectors machines (SVM), artificial neural networks (ANN), k-nearest neighbors (KNN) and Bayes classifier (BC). The method used in this work shows that SVM outperforms the other algorithms. see paper here: https://www.researchgate.net/publication/303043316_Machine_Learning_for_Real_Time_Poses_Classification_Using_Kinect_Skeleton_Data
Views: 121 Abdelhak Mahmoudi
11. Introduction to Machine Learning
 
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: Eric Grimson In this lecture, Prof. Grimson introduces machine learning and shows examples of supervised learning using feature vectors. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 356865 MIT OpenCourseWare
fuzzy logic in artificial intelligence in hindi | introduction to fuzzy logic example | #28
 
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fuzzy logic in artificial intelligence in hindi | fuzzy logic example | #28 Fuzzy Logic (FL) is a method of reasoning that resembles human reasoning. The approach of FL imitates the way of decision making in humans that involves all intermediate possibilities between digital values YES and NO. The conventional logic block that a computer can understand takes precise input and produces a definite output as TRUE or FALSE, which is equivalent to human’s YES or NO. The inventor of fuzzy logic, Lotfi Zadeh, observed that unlike computers, the human decision making includes a range of possibilities between YES and NO, such as − CERTAINLY YES POSSIBLY YES CANNOT SAY POSSIBLY NO CERTAINLY NO well,academy,Fuzzy logic in hindi,fuzzy logic in artificial intelligence in hindi,artificial intelligence fuzzy logic,fuzzy logic example,fuzzy logic in artificial intelligence,fuzzy logic with example,fuzzy logic in artificial intelligence in hindi with exapmle,fuzzy logic,what is fuzzy logic in hindi,what is fuzzy logic with example,introduction to fuzzy logic
Views: 87326 Well Academy
Matlab Training | Disease Prediction using Data Mining | Anova + PCA Features | SVM
 
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Disease prediction using data mining system using ANOVA2 + PCA and SVM classifier. An automated algorithm for disease prediction using MATLAB online training. For any further help contact us at [email protected] visit us at http://www.researchinfinitesolutions.com/ Direct at :: +91-6239359461 Whatsapp at :: +91-6239359461
Views: 15961 Fly High with AI
Seeing Behaviors as Humans Do׃ Uncovering Hidden Patterns in Time Series Data w⁄ Deep Networks
 
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Time-series (longitudinal) data occurs in nearly every aspect of our lives; including customer activity on a website, financial transactions, sensor/IoT data. Just like in written text, specific events in a sequence of events are affected by the past and affect events in the future, and this can reveal a lot of hidden structure in the source of the events. Yet, today's predictive techniques largely rely on demographic (cross-sectional) data and do not take into account the sequences of events as they occur. In this session, Mohammad will discuss techniques for taking time-series data from a variety of domains and sources and grouping entities based on temporal behavior, using RNNs. These clusters of time-series sequences can either be visualized or used for campaign targeting in the case of user clickstream behavior or understanding stock symbols that behave similarly based on their trading behavior. About the Speaker: Mohammad Saffar is a deep learning software engineer at Arimo, world's leader in AI platform for the Enterprise. He loves being involved in designing and implementing real-world systems specifically machine learning and data mining related systems. His past projects involve video-based intent recognition, multi-agent intent recognition and face recognition with deep networks. Mohammad holds a PhD. in Computer Science from the University of Nevada-Reno. *This talk was at the Cloudera Wrangle 2016*
Views: 2049 Arimo, Inc.
Data Mining, Prediction
 
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Group Project. Prediction of Bankruptcy.
Views: 233 Meruert Myrzabekova
A.I. Experiments: Visualizing High-Dimensional Space
 
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Check out https://g.co/aiexperiments to learn more. This experiment helps visualize what’s happening in machine learning. It allows coders to see and explore their high-dimensional data. The goal is to eventually make this an open-source tool within TensorFlow, so that any coder can use these visualization techniques to explore their data. http://g.co/aiexperiments Built by Daniel Smilkov, Fernanda Viégas, Martin Wattenberg, and the Big Picture team at Google. More resources: http://www.tensorflow.org
Views: 450283 Google Developers
Data Mining: How You're Revealing More Than You Think
 
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Data mining recently made big news with the Cambridge Analytica scandal, but it is not just for ads and politics. It can help doctors spot fatal infections and it can even predict massacres in the Congo. Hosted by: Stefan Chin Head to https://scishowfinds.com/ for hand selected artifacts of the universe! ---------- Support SciShow by becoming a patron on Patreon: https://www.patreon.com/scishow ---------- Dooblydoo thanks go to the following Patreon supporters: Lazarus G, Sam Lutfi, Nicholas Smith, D.A. Noe, سلطان الخليفي, Piya Shedden, KatieMarie Magnone, Scott Satovsky Jr, Charles Southerland, Patrick D. Ashmore, Tim Curwick, charles george, Kevin Bealer, Chris Peters ---------- Looking for SciShow elsewhere on the internet? Facebook: http://www.facebook.com/scishow Twitter: http://www.twitter.com/scishow Tumblr: http://scishow.tumblr.com Instagram: http://instagram.com/thescishow ---------- Sources: https://www.aaai.org/ojs/index.php/aimagazine/article/viewArticle/1230 https://www.theregister.co.uk/2006/08/15/beer_diapers/ https://www.theatlantic.com/technology/archive/2012/04/everything-you-wanted-to-know-about-data-mining-but-were-afraid-to-ask/255388/ https://www.economist.com/node/15557465 https://blogs.scientificamerican.com/guest-blog/9-bizarre-and-surprising-insights-from-data-science/ https://qz.com/584287/data-scientists-keep-forgetting-the-one-rule-every-researcher-should-know-by-heart/ https://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853 http://dml.cs.byu.edu/~cgc/docs/mldm_tools/Reading/DMSuccessStories.html http://content.time.com/time/magazine/article/0,9171,2058205,00.html https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=all&_r=0 https://www2.deloitte.com/content/dam/Deloitte/de/Documents/deloitte-analytics/Deloitte_Predictive-Maintenance_PositionPaper.pdf https://www.cs.helsinki.fi/u/htoivone/pubs/advances.pdf http://cecs.louisville.edu/datamining/PDF/0471228524.pdf https://bits.blogs.nytimes.com/2012/03/28/bizarre-insights-from-big-data https://scholar.harvard.edu/files/todd_rogers/files/political_campaigns_and_big_data_0.pdf https://insights.spotify.com/us/2015/09/30/50-strangest-genre-names/ https://www.theguardian.com/news/2005/jan/12/food.foodanddrink1 https://adexchanger.com/data-exchanges/real-world-data-science-how-ebay-and-placed-put-theory-into-practice/ https://www.theverge.com/2015/9/30/9416579/spotify-discover-weekly-online-music-curation-interview http://blog.galvanize.com/spotify-discover-weekly-data-science/ Audio Source: https://freesound.org/people/makosan/sounds/135191/ Image Source: https://commons.wikimedia.org/wiki/File:Swiss_average.png
Views: 134884 SciShow
Deep Learning vs Multidimensional Classification in Human-Guided Text Mining (GI研・天神イムズ・日本語・スクリーン...
 
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Deep (Neural) Learning has recently become popular in AI research.  The method is traditionally showcased in vision-related tasks where input can be easily regulated.  However, when applied to text mining, the irregular textual input becomes a hurdle.  Overcoming the hurdle involves processing the text and using its frequency distribution as a numeric input.  This paper compares the technology with a recently proposed method in multidimensional classification. The specific feature in focus is a human-guided system where the learning dataset is not available at once but arrives gradually, along with human annotation.
Views: 108 Marat Zhanikeev
Natural Language Processing in Python || Natural Language Processing Tutorial
 
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Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. Challenges in natural language processing frequently involve speech recognition, natural language understanding, and natural language generation. Natural Language Processing in Python Speaker: Alice Zhao Natural language processing (NLP) is an exciting branch of artificial intelligence (AI) that allows machines to break down and understand human language. As a data scientist, I often use NLP techniques to interpret text data that I'm working with for my analysis. During this tutorial, I plan to walk through text pre-processing techniques, machine learning techniques and Python libraries for NLP. Text pre-processing techniques include tokenization, text normalization and data cleaning. Once in a standard format, various machine learning techniques can be applied to better understand the data. This includes using popular modeling techniques to classify emails as spam or not, or to score the sentiment of a tweet on Twitter. Newer, more complex techniques can also be used such as topic modeling, word embeddings or text generation with deep learning. We will walk through an example in Jupyter Notebook that goes through all of the steps of a text analysis project, using several NLP libraries in Python including NLTK, TextBlob, spaCy and gensim along with the standard machine learning libraries including pandas and scikit-learn. ## Setup Instructions [ https://github.com/adashofdata/nlp-in-python-tutorial ) Learn From Expert = Learn Python: https://www.youtube.com/playlist?list=PLqrmzsjOpq5jHouzMfPsQ0OtZeKsIQ_h_ Machine Learning: https://www.youtube.com/playlist?list=PLqrmzsjOpq5iBQEtgHSeF4WaVzII_ycBn Data Science: https://www.youtube.com/playlist?list=PLqrmzsjOpq5gTZwO8ey_EcBMkpi4gjHob Python Pandas: https://www.youtube.com/playlist?list=PLqrmzsjOpq5jRH3uPWix4H6Ilc0NdJCPD Tech Talks From Expert: https://www.youtube.com/playlist?list=PLqrmzsjOpq5irFw0F-_XkqNXiiM1vyMoE JavaScript: https://www.youtube.com/playlist?list=PLqrmzsjOpq5hF1dIpIKZBH7AvOFyjRi8e Angular: https://www.youtube.com/playlist?list=PLqrmzsjOpq5ih8oBojNGej0oe7JsM6BGo IT Security: https://www.youtube.com/playlist?list=PLqrmzsjOpq5g5SKYrInDt0LDj0Ekflrvu IT Administration: https://www.youtube.com/playlist?list=PLqrmzsjOpq5hwvojwjsx6byUAICs_afEn Quantum Computing: https://www.youtube.com/playlist?list=PLqrmzsjOpq5iYFVnfEESpP8ErB8KcnKVC Learn SQL : https://www.youtube.com/playlist?list=PLqrmzsjOpq5jVqMfp9gUSLq9PyNBoZwdj Deep Learning: https://www.youtube.com/playlist?list=PLqrmzsjOpq5jctokAY61zrLxlE6Ub4tW_ ********************************************************** ==================================================== https://pyohio.org A FREE annual conference for anyone interested in Python in and around Ohio, the entire Midwest, maybe even the whole world. This video was published Creative Commons Attribution license (reuse allowed) Source: https://youtu.be/DqWrRpYSdio ********************************************************************
Views: 180 My CS
Web parsing, Web Scraping, Data Mining
 
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In this video we will look through opportunities for integrating PHP and Human Emulator functional.
Views: 6580 webemulator
Natural Language Processing in Python
 
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Alice Zhao https://pyohio.org/2018/schedule/presentation/38/ Natural language processing (NLP) is an exciting branch of artificial intelligence (AI) that allows machines to break down and understand human language. As a data scientist, I often use NLP techniques to interpret text data that I'm working with for my analysis. During this tutorial, I plan to walk through text pre-processing techniques, machine learning techniques and Python libraries for NLP. Text pre-processing techniques include tokenization, text normalization and data cleaning. Once in a standard format, various machine learning techniques can be applied to better understand the data. This includes using popular modeling techniques to classify emails as spam or not, or to score the sentiment of a tweet on Twitter. Newer, more complex techniques can also be used such as topic modeling, word embeddings or text generation with deep learning. We will walk through an example in Jupyter Notebook that goes through all of the steps of a text analysis project, using several NLP libraries in Python including NLTK, TextBlob, spaCy and gensim along with the standard machine learning libraries including pandas and scikit-learn. ## Setup Instructions [ https://github.com/adashofdata/nlp-in-python-tutorial](https://github.com/adashofdata/nlp-in-python-tutorial) === https://pyohio.org A FREE annual conference for anyone interested in Python in and around Ohio, the entire Midwest, maybe even the whole world.
Views: 1125 PyOhio
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 Natural Language Processing Training call us at US: +18336900808 (Toll Free) or India: +918861301699 , Or, write back to us at [email protected]
Views: 2848 edureka!
BADM 5.4 K-Means Clustering
 
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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: http://www.dataminingbook.com https://www.twitter.com/gshmueli https://www.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 Networks: Part II • Discriminant Analysis (Part 1) • Discriminant Analysis: Statistical Distance (Part 2) • Discriminant Analysis: Misclassification costs and over-sampling (Part 3)
Views: 381 Galit Shmueli
How SVM (Support Vector Machine) algorithm works
 
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In this video I explain how SVM (Support Vector Machine) algorithm works to classify a linearly separable binary data set. The original presentation is available at http://prezi.com/jdtqiauncqww/?utm_campaign=share&utm_medium=copy&rc=ex0share
Views: 464476 Thales Sehn Körting
Data Mining the Deceased Trailer. Brandeis University, Lown 002. Nov 2 2:00-3:30
 
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Trailer for Data Mining the Deceased: Ancestry and the Business of Family. Brandeis University Lown OO2. Nov 2. 2:00-3:30 with Q and A
Views: 1467 Julia Creet
New Global Classification for Brain Tumors: ABTA Educational Webinar Series
 
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Brain tumors have been traditionally classified by pathologists mainly based on their histologic appearance under the microscope. While this tried-and-true method is still extremely important for the identification and classification of brain tumors, in some cases, genetic testing of the tumor can more precisely classify the tumor based on important molecular markers. Recently, the World Health Organization updated its guidelines for brain tumor classification. This update included molecular features to help define and diagnosis brain tumors. Here from Kenneth Aldape, MD, Professor, Department of Pathology at University of Toronto, where he discusses the importance of this global change for brain tumor classification and provides information on how molecular markers have been incorporated into tumor classification. He also presents how this may help move the field forward for improved diagnosis and treatment of brain tumors. In this webinar, participants had the opportunity to ask Dr. Aldape questions in an interactive session. Hear his answers and understand the importance of this major milestones for diagnosing and treating brain tumors. For more information about the ABTA's Educational Webinar and to sign up for other upcoming topics visit: http://www.abta.org/brain-tumor-information/webinars.html. To view the full collection of ABTA's educational webinars, visit https://www.youtube.com/playlist?list=PLTcHii7RGP5jq-8WyvIBQ8ECjcflTTBCl or the ABTA's Anytime Learning Resource at: http://www.abta.org/brain-tumor-information/anytime-learning/
NIPS 2011 Big Learning - Algorithms, Systems, & Tools Workshop: Towards Human Behavior...
 
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Big Learning Workshop: Algorithms, Systems, and Tools for Learning at Scale at NIPS 2011 Invited Talk: Towards Human Behavior Understanding from Pervasive Data: Opportunities and Challenges Ahead by Nuria Oliver Nuria Oliver is currently the Scientific Director for the Multimedia, HCI and Data Mining & User Modeling Research Areas in Telefonica Research (Barcelona, Spain). Her research interests include mobile computing, multimedia data analysis, search and retrieval, smart environments, context awareness, statistical machine learning and data mining, artificial intelligence, health monitoring, social network analysis, computational social sciences, and human computer interaction. She is currently working on the previous disciplines to build human-centric intelligent systems. Abstract: We live in an increasingly digitized world where our -- physical and digital -- interactions leave digital footprints. It is through the analysis of these digital footprints that we can learn and model some of the many facets that characterize people, including their tastes, personalities, social network interactions, and mobility and communication patterns. In my talk, I will present a summary of our research efforts on transforming these massive amounts of user behavioral data into meaningful insights, where machine learning and data mining techniques play a central role. The projects that I will describe cover a broad set of areas, including smart cities and urban computing, psychographics, socioeconomic status prediction and disease propagation. For each of the projects, I will highlight the main results and point at technical challenges still to be solved from a data analysis perspective.
Views: 1621 GoogleTechTalks
Data Mining using R | R Tutorial for Beginners | Data Mining Tutorial for Beginners 2018 | ExcelR
 
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ExcelR Data Mining Tutorial for Beginners 2018 - Introduction to Data mining using R language. 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
Human Diagnosis Project | 100&Change Semi-Finalist
 
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Learn more about all eight semi-finalists in #100andchange, a competition for a single $100 million grant from MacArthur: https://www.macfound.org/programs/100change
Views: 3416 macfound
Serena Peruzzo - Data driven literary analysis
 
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PyData Amsterdam 2016 Can unsupervised learning mimic a literary critic? This talk will give an overview of unsupervised document classification techniques and apply them to the analysis and classification of Shakespeare’s plays. Unsupervised document classification addresses the problem of assigning categories to documents without the use of a training set or predefined categories. This is useful to enhance information retrieval, the basic assumption being that similar contents are also relevant to the same query. A similar assumption is made in literature to define literary genres and sub-genres, where works which share specific conventions in terms of form and content are described by the same genre. The talk gives an overview of document clustering and its challenges, with a focus on dimensionality reduction and how to address it with topic modelling techniques like LDA (Latent Dirichlet Allocation). Using Shakespeare’s body of work as a case study, the talk describes how to use nltk, sklearn and gensim to process and analyse theatrical works with the final goal of testing whether document clustering yields to the same classification given by literature experts. Slides available here: https://speakerdeck.com/sereprz/data-driven-literary-analysis-an-unsupervised-approach-to-text-analysis-and-classification
Views: 989 PyData
Application of Data Mining Techniques in Discovering Important Patterns from Tourism Data of Seoul
 
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Application of Data Mining Techniques in Discovering Important Patterns from Tourism Data of Seoul
Views: 114 Hyun-jin Ahn
Improving Sentiment Classification of Social Media Posts through Data Refinements
 
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Author: Vita Markman, LinkedIn Corporation Abstract: Quality training data is essential for building high performance machine learning models. However, certain types of tasks such as opinion mining are inherently subjective, making it hard to elicit reliable judgements from human annotators. The problem is further exacerbated in situations where opinions are elicited on short text such as Tweets or micro reviews containing only one or two lines. The talk addresses various means of circumventing these challenges via automation of some annotation tasks as well as setting up multiple experiments for collecting human judgements. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 181 KDD2016 video
Introduction to Data Mining: Sampling for Data Selection
 
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In part three of data preprocessing, we discuss the technique of sampling for data selection -- At Data Science Dojo, we're extremely passionate about data science. Our in-person data science training has been attended by more than 3500+ employees from over 700 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8M760 See what our past attendees are saying here: https://hubs.ly/H0f8M7b0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... -- Vimeo: https://vimeo.com/datasciencedojo
Views: 4153 Data Science Dojo
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: 329 jpinfotechprojects
Making friends | Ourn Sarath
 
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Views: 11085 Ourn Sarath
Data Mining - Emotional Noise to Uncloud A/V Emotion Perceptual Eval. | Lectures On-Demand
 
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Emily Mower - Provost, Computer Science and Engineering at the University of Michigan The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
Views: 1349 Michigan Engineering
Natural Language Processing In 10 Minutes | NLP Tutorial For Beginners | NLP Training | Edureka
 
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** Natural Language Processing Using Python: https://www.edureka.co/python-natural-language-processing-course ** This Edureka video will provide you with a short and crisp description of NLP (Natural Language Processing) and Text Mining. You will also learn about the various applications of NLP in the industry. NLP Tutorial : https://www.youtube.com/watch?v=05ONoGfmKvA Subscribe to our channel to get video updates. Hit the subscribe button above. ------------------------------------------------------------------------------------------------------- #NLPin10minutes #NLPtutorial #NLPtraining #Edureka 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 learned 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 Natural Language Processing Training call us at US: +18336900808 (Toll Free) or India: +918861301699 , Or, write back to us at [email protected]
Views: 3578 edureka!
Design Mining the Web
 
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The Web has transformed the nature of creative work. For the first time, millions of people have a direct outlet for sharing their creations with the world. As a result, the Web has become the largest repository of design knowledge in human history, and the ensuing democratization of design has created a critical feedback loop, engendering a new culture of reuse and remixing. The means and methods designers use to employ to draw on prior work, however, remain mostly informal and ad hoc. How can content producers find relevant examples amongst hundreds of millions of possibilities and leverage existing design practice to inform and improve their creations? In this episode, P.h.D. candidate at Standford University, Ranjitha Kumar, explores data-driven techniques for working with examples at scale during the design process, automating search and curation, enabling rapid retargeting, and learning generative probabilistic models to support new design interactions. Knowledge discovery and data mining have revolutionized informatics; in this talk, Kumar discusses what we can learn from mining design.
Views: 1792 UWTV
Cinema Data Mining
 
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Authors: Jorg Wicker, Nicolas Krauter, Bettina Derstorff, Christof Stonner, Efstratios Bourtsoukidis, Thomas Klupfel, Jonathan Williams, Stefan Kramer Abstract: While the physiological response of humans to emotional events or stimuli is well-investigated for many modalities (like EEG, skin resistance, ...), surprisingly little is known about the exhalation of so-called Volatile Organic Compounds (VOCs) at quite low concentrations in response to such stimuli. VOCs are molecules of relatively small mass that quickly evaporate or sublimate and can be detected in the air that surrounds us. The paper introduces a new field of application for data mining, where trace gas responses of people reacting on-line to films shown in cinemas (or movie theaters) are related to the semantic content of the films themselves. To do so, we measured the VOCs from a movie theater over a whole month in intervals of thirty seconds, and annotated the screened films by a controlled vocabulary compiled from multiple sources. To gain a better understanding of the data and to reveal unknown relationships, we have built prediction models for so-called forward prediction (the prediction of future VOCs from the past), backward prediction (the prediction of past scene labels from future VOCs), which is some form of abductive reasoning, and Granger causality. Experimental results show that some VOCs and some labels can be predicted with relatively low error, and that hint for causality with low p-values can be detected in the data. The data set is publicly available at: https://github.com/jorro/smelloffear. ACM DL: http://dl.acm.org/citation.cfm?id=2783404 DOI: http://dx.doi.org/10.1145/2783258.2783404