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Anomaly Detection: Algorithms, Explanations, Applications
 
01:26:56
Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly "alarms" to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning. See more at https://www.microsoft.com/en-us/research/video/anomaly-detection-algorithms-explanations-applications/
Views: 16079 Microsoft Research
Mod-01 Lec-08 Rank Order Clustering, Similarity Coefficient based algorithm
 
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Manufacturing Systems Management by Prof. G. Srinivasan, Department of Management, IITmadras. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 14548 nptelhrd
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: 149210 SciShow
Single nucleotide polymorphism SNP
 
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For more information, log on to- http://shomusbiology.weebly.com/ Download the study materials here- http://shomusbiology.weebly.com/bio-materials.html A single-nucleotide polymorphism (SNP, pronounced snip; plural snips) is a DNA sequence variation occurring when a single nucleotide — A, T, C or G — in the genome (or other shared sequence) differs between members of a biological species or paired chromosomes in a human. For example, two sequenced DNA fragments from different individuals, AAGCCTA to AAGCTTA, contain a difference in a single nucleotide. In this case we say that there are two alleles. Almost all common SNPs have only two alleles. The genomic distribution of SNPs is not homogenous; SNPs usually occur in non-coding regions more frequently than in coding regions or, in general, where natural selection is acting and fixating the allele of the SNP that constitutes the most favorable genetic adaptation.[1] Other factors, like genetic recombination and mutation rate, can also determine SNP density.[2] SNP density can be predicted by the presence of microsatellites: AT microsatellites in particular are potent predictors of SNP density, with long (AT)(n) repeat tracts tending to be found in regions of significantly reduced SNP density and low GC content.[3] Within a population, SNPs can be assigned a minor allele frequency — the lowest allele frequency at a locus that is observed in a particular population. This is simply the lesser of the two allele frequencies for single-nucleotide polymorphisms. There are variations between human populations, so a SNP allele that is common in one geographical or ethnic group may be much rarer in another. These genetic variations between individuals (particularly in non-coding parts of the genome) are exploited in DNA fingerprinting, which is used in forensic science . Also, these genetic variations underlie differences in our susceptibility to disease. The severity of illness and the way our body responds to treatments are also manifestations of genetic variations. For example, a single base mutation in the APOE (apolipoprotein E) gene is associated with a higher risk for Alzheimer disease.[4] Source of the article published in description is Wikipedia. I am sharing their material. Copyright by original content developers. Link- http://en.wikipedia.org/wiki/Main_Page
Views: 129126 Shomu's Biology
Comparing Time Series
 
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(Index: https://www.stat.auckland.ac.nz/~wild/wildaboutstatistics/ ) It is often interesting and useful to compare several series in terms of trend and seasonal patterns. How do the trends compare? How big are the seasonal effects for one series compared to another? Do they all behave in the same way at the same times? What oddities stand out in the plots? After you’ve watched this video, you should be able to answer these questions •When we are plotting several related series so that we can compare the patterns in them, what are the strengths and the weaknesses of a plot that puts all of the series on the same graph? •When we are plotting several related series so that we can compare the patterns in them, what are the strengths and the weaknesses of a plot that puts all of the series on their own separate graphs? •What types of feature of each series can we compare using the iNZight graphs for comparing series?
Views: 6293 Wild About Statistics
Data Mining - SMS Spam Filter - Team E
 
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Final Project for Data Mining 95-791
Views: 416 Madeleine Gleave
Algorithm will help search for cancer cells
 
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Ulysses Balis, M.D. and Jason Hipp, M.D., Ph.D., demonstrate a pattern-matching algorithm aimed at making the analysis of cell and tissue samples better, faster and simpler. Here they demonstrate the extremely flexible program, known as Spatially Invariant Vector Quantization (SIVQ), by using it to search for patches of grass outside the National Institutes of Health in a Google Map image. Learn more at: www.uofmhealth.org/News/sivq_pathology_0200
Views: 1056 Michigan Medicine
John P Overington (Medicines Discovery Catapult): Data Mining Small Molecule Drug Discovery
 
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Despite having more information and technology than at any point in history, drug discovery is becoming harder. It is tempting to believe that there was ‘low hanging fruit’ in the past, and that previous generations had easier to treat diseases, simpler biology and a large number of drug-like leads to optimize. Regardless of the cause, there is now a pressing need to understand fundamental complex biological systems, especially those linked to disease pathology. The most definitive tools for illuminating biology for this are often small molecules, and there is now intense interest in developing, in a cost effective way, potent, well distributed and selective chemical probes, then applying these to understand the role of novel genes, potentially leading to a new medicine. Underlying the development of chemical probes and drug leads, is what is known from the past, and what general rules can be learnt that are useful in the future. The presentation will detail the background and development of two large, now public domain, chemical biology databases – ChEMBL and SureChEMBL. These databases, in particular ChEMBL have led to the development of many new algorithms for target prediction, chemical library design, etc. Next four examples of data mining of ChEMBL and other public domain data will be described. 1) A framework to anticipate and integrate into compound design processes the effect of mutations in the target – this is of special importance in the area of anti-infective and anti-cancer drugs where resistance is a significant healthcare issue. 2) An analysis of drug properties according to target class for the antibiotics, where differences in physicochemical properties can be correlated in target properties. 3) Addressing the problem of target validation using genetics, which could de-risk the development of chemical tools and leads, and place novel targets into an appropriate therapeutic setting. 4) Is the concept of ‘Druggability’ real, or has it led to restriction in the number of systems that the community is prepared to work on?
Views: 450 ChemAxon
Data Scientist: кто нужен бизнесу и как их обучить | Виктор Кантор, Data Mining in Action
 
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Подпишись: https://on.fless.pro/subscribe Беседуем с Виктором Кантором об образовании и карьерах в Data Science. Виктор, обучивший не одну сотню специалистов по DS в рамках Data Mining in Action, делится своими взглядами на потребности рынка, карьерные возможности для людей с разным бекграундом и перспективы образования в DS и DS в образовании. Изменит ли Data Science будущее? Или это очередной хайп? Пишите в комментариях. [TIMETAGS] 00:35 Data Science - что внутри? 11:53 Немного о Data Engineers 16:59 Нюансы обучения Data Scientist-ов 26:12 Data Science и его роль в образовании 36:51 Можно ли сделать изучение Data Science увлекательным? 38:28 Data Science и будущее разных профессий 46:03 О безусловном доходе Другие недавние интервью: - Data Science: Kaggle GRANDMASTER за полгода? | Павел Плесков, Data Nerds - https://youtu.be/5wMAPUrd0ag - КАРЬЕРА НА СТЫКЕ DIGITAL И STRATEGY CONSULTING | АНАСТАСИЯ КИМ, IBM iX - https://youtu.be/7kwd_0qYXY4 Канал Виктора Кантора в ТГ: https://t.me/kantor_ai Мы на других платформах: FLESS https://fless.pro Instagram https://www.instagram.com/flesspro Facebook https://www.facebook.com/flesspro VK https://vk.com/flesspro Telegram https://t.me/flesspro
Views: 10502 Fless
Data Mining Using R: Introduction to Data Mining Techniques | Machine Learning - ExcelR
 
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ExcelR Data Mining Tutorial for Beginners 2018 - Introduction to various Data mining unsupervised techniques namely Clustering, Dimension Reduction, Association Rules, Recommender System or Collaborative filtering, Network Analytics. Things you will learn in this video 1)What is DataMining 2)DataMining in Nutshell 3)Types of methods 4)DataMining process 5)Approaches 6)Types of Clustering Algorithms To buy eLearning course on DataScience click here https://goo.gl/oMiQMw To enroll for the virtual online course click here https://goo.gl/m4MYd8 To register for classroom training click here https://goo.gl/UyU2ve SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx For Introduction to Clustering Analysis clicks here https://goo.gl/wuXN48 For Introduction to K-mean clustering click here https://goo.gl/PYqXRJ #ExcelRSolutions #DataMining#clusteringTechniques #datascience #datasciencetutorial #datascienceforbeginners #datasciencecourse ----- For More Information: Toll Free (IND) : 1800 212 2120 | +91 80080 09706 Malaysia: 60 11 3799 1378 USA: 001-844-392-3571 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com Connect with us: Facebook: https://www.facebook.com/ExcelR/ LinkedIn: https://www.linkedin.com/company/exce... Twitter: https://twitter.com/ExcelrS G+: https://plus.google.com/+ExcelRSolutions
Advanced Data Mining with Weka (4.6: Application: Image classification)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 6: Application: Image classification http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/msswhT https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 8588 WekaMOOC
AI vs Machine Learning vs Deep Learning | Machine Learning Training with Python | Edureka
 
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** NIT Warangal Post Graduate Program on AI and Machine Learning: https://www.edureka.co/nitw-ai-ml-pgp ** This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) on "AI vs Machine Learning vs Deep Learning" talks about the differences and relationship between AL, Machine Learning and Deep Learning. Below are the topics covered in this tutorial: 1. AI vs Machine Learning vs Deep Learning 2. What is Artificial Intelligence? 3. Example of Artificial Intelligence 4. What is Machine Learning? 5. Example of Machine Learning 6. What is Deep Learning? 7. Example of Deep Learning 8. Machine Learning vs Deep Learning Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm - - - - - - - - - - - - - - - - - Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV Instagram: https://www.instagram.com/edureka_learning Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka - - - - - - - - - - - - - - - - - #edureka #AIvsMLvsDL #PythonTutorial #PythonMachineLearning #PythonTraining How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 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 be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you: 1. Master the Basic and Advanced Concepts of Python 2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs 3. Master the Concepts of Sequences and File operations 4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python 5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 7. Master the concepts of MapReduce in Hadoop 8. Learn to write Complex MapReduce programs 9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python 10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics 11. Master the concepts of Web scraping in Python 12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Customer Review Sairaam Varadarajan, Data Evangelist at Medtronic, Tempe, Arizona: "I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the "customer of Edureka!". Instructors are knowledge... able and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. Most of the online courses are free, edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry. I am confident that, no other website which have tailored the courses like Edureka. It will help for an immediate take-off in Data Science and Hadoop working."
Views: 478835 edureka!
Comparing the Theory and Practice of Spectral Algorithms to Combinatorial Flow Algorithms for ...
 
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Dorit Hochbaum, UC Berkeley Spectral Algorithms: From Theory to Practice http://simons.berkeley.edu/talks/dorit-hochbaum-2014-10-30
Views: 358 Simons Institute
SAXually Explicit Images: Data Mining Large Shape Databases
 
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Google TechTalks May 12, 2006 Eamonn Keogh ABSTRACT The problem of indexing large collections of time series and images has received much attention in the last decade, however we argue that there is potentially great untapped utility in data mining such collections. Consider the following two concrete examples of problems in data mining. Motif Discovery (duplication detection): Given a large repository of time series or images, find approximately repeated patterns/images. Discord Discovery: Given a large repository of time series or images, find the most unusual time series/image. As we will show, both these problems have applications in fields as diverse as anthropology, crime...
Views: 4707 Google
How CNN (Convolutional Neural Networks - Deep Learning) algorithm works
 
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In this video I present a simple example of a CNN (Convolutional Neural Network) applied to image classification of digits. CNN is one of the well known Deep Learning algorithms. I firstly explain the basics of Neural Networks, i.e. the artificial neuron, followed by the concept of convolution, and the common layers in a CNN, such as convolutional, pooling, fully connected, and softmax classification. I read several references to prepare this material, but the main references are: * Towards better exploiting convolutional neural networks for Remote Sensing scene classification. By Keiller Nogueira, Otávio Penatti, Jefersson dos Santos * Everything you wanted to know about Deep Learning for computer vision but were afraid to ask. By Moacir Ponti, Leonardo Ribeiro, Tiago Nazaré, Tu Bui, John Collomosse I also created an Octave (Matlab like) source code to implement the basic CNN showed in this video, which are available at my github. Please follow the link for more details on the source code: https://github.com/tkorting/youtube/tree/master/deep-learning-cnn This presentation is available at my Prezi site, at this link: http://prezi.com/n_r8p1ytanyh/?utm_campaign=share&utm_medium=copy Thanks for watching this video, please like and share, and subscribe to my channel. Regards
Views: 42602 Thales Sehn Körting
Getting Started with Weka - Machine Learning Recipes #10
 
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Hey everyone! In this video, I’ll walk you through using Weka - The very first machine learning library I’ve ever tried. What’s great is that Weka comes with a GUI that makes it easy to visualize your datasets, and train and evaluate different classifiers. I’ll give you a quick walkthrough of the tool, from installation all the way to running experiments, and show you some of what it can do. This is a helpful library to have while you’re learning ML, and I still find it useful today to experiment with new datasets. Note: In the video, I quickly went through testing. This is an important topic in ML, and how you design and evaluate your experiments is even more important than the classifier you use. Although I publish these videos at turtle speed, I’ve started working on an experimental design one, and that’ll be next! Also, we will soon publish some testing tips and best practices on tensorflow.org (https://goo.gl/nZcS5R). Links from the video: Weka → https://goo.gl/2TYjGZ Ready to use datasets → https://goo.gl/PM8DtH More on evaluating classifiers, particularly in the medical domain → https://goo.gl/TwTYyk Check out the Machine Learning Recipes playlist → https://goo.gl/KewA03 Follow Josh on Twitter → https://twitter.com/random_forests Subscribe to the Google Developers channel → http://goo.gl/mQyv5L
Views: 74104 Google Developers
Data Science & Machine Learning - Method Types in Regression Trees - DIY- 16 -of-50
 
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Data Science & Machine Learning - Method Types in Regression Trees - DIY- 16 -of-50 Do it yourself Tutorial by Bharati DW Consultancy cell: +1-562-646-6746 (Cell & Whatsapp) email: [email protected] website: http://bharaticonsultancy.in/ Google Drive- https://drive.google.com/open?id=0ByQlW_DfZdxHeVBtTXllR0ZNcEU Regression trees rpart(Target ~ Predictors, data = DataSets, method=“Types"). Method Types: class – categorical classification of data. anova – continuous values. poisson – based on counts of values, like count of employed exp – exponential - Survival method Hands On – R Machine Learning Ex-12 Extend the hands-on exercise -11 Implement Regression Trees Model using different methods for target variable - Spend using predictor variables Age, Income, Job, Auto Loan Indicator, Gender, Marital Status. Calculate Mean Square Error for each method. Data Science & Machine Learning - Getting Started - DIY- 1 -of-50 Data Science & Machine Learning - R Data Structures - DIY- 2 -of-50 Data Science & Machine Learning - R Data Structures - Factors - DIY- 3 -of-50 Data Science & Machine Learning - R Data Structures - List & Matrices - DIY- 4 -of-50 Data Science & Machine Learning - R Data Structures - Data Frames - DIY- 5 -of-50 Data Science & Machine Learning - Frequently used R commands - DIY- 6 -of-50 Data Science & Machine Learning - Frequently used R commands contd - DIY- 7 -of-50 Data Science & Machine Learning - Installing RStudio- DIY- 8 -of-50 Data Science & Machine Learning - R Data Visualization Basics - DIY- 9 -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(a) -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(b) -of-50 Data Science & Machine Learning - Multiple Linear Regression Model - DIY- 11 -of-50 Data Science & Machine Learning - Evaluate Model Performance - DIY- 12 -of-50 Data Science & Machine Learning - RMSE & R-Squared - DIY- 13 -of-50 Data Science & Machine Learning - Numeric Predictions using Regression Trees - DIY- 14 -of-50 Data Science & Machine Learning - Regression Decision Trees contd - DIY- 15 -of-50 Data Science & Machine Learning - Method Types in Regression Trees - DIY- 16 -of-50 Machine learning, data science, R programming, Deep Learning, Regression, Neural Network, R Data Structures, Data Frame, RMSE & R-Squared, Regression Trees, Decision Trees
Machine Learning & Artificial Intelligence: Crash Course Computer Science #34
 
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So we've talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical diagnosis and real-time language translation, there has been an increasing need for our computers to learn from data and apply that knowledge to make predictions and decisions. This is the heart of machine learning which sits inside the more ambitious goal of artificial intelligence. We may be a long way from self-aware computers that think just like us, but with advancements in deep learning and artificial neural networks our computers are becoming more powerful than ever. Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios Want to know more about Carrie Anne? https://about.me/carrieannephilbin The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list=PL1mtdjDVOoOqJzeaJAV15Tq0tZ1vKj7ZV Want to find Crash Course elsewhere on the internet? Facebook - https://www.facebook.com/YouTubeCrash... Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 428008 CrashCourse
Learning the Comparison of Image Mining Technique and Data Mining Technique
 
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Invited Talk: Learning the Comparison of Image Mining Technique and Data Mining Technique
Time Series Analysis in Python | Time Series Forecasting | Data Science with Python | Edureka
 
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** Python Data Science Training : https://www.edureka.co/python ** This Edureka Video on Time Series Analysis n Python will give you all the information you need to do Time Series Analysis and Forecasting in Python. Below are the topics covered in this tutorial: 1. Why Time Series? 2. What is Time Series? 3. Components of Time Series 4. When not to use Time Series 5. What is Stationarity? 6. ARIMA Model 7. Demo: Forecast Future Subscribe to our channel to get video updates. Hit the subscribe button above. Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm #timeseries #timeseriespython #machinelearningalgorithms - - - - - - - - - - - - - - - - - About the Course Edureka’s Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Python Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR. During our Python Certification Training, our instructors will help you to: 1. Master the basic and advanced concepts of Python 2. Gain insight into the 'Roles' played by a Machine Learning Engineer 3. Automate data analysis using python 4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 6. Explain Time Series and it’s related concepts 7. Perform Text Mining and Sentimental analysis 8. Gain expertise to handle business in future, living the present 9. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 73120 edureka!
Mod-01 Lec-38 Genetic Algorithms
 
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Design and Optimization of Energy Systems by Prof. C. Balaji , Department of Mechanical Engineering, IIT Madras. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 157097 nptelhrd
Bioinformatics part 3 Sequence alignment introduction
 
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This Bioinformatics lecture explains the details about the sequence alignment. The mechanism and protocols of sequence alignment is explained in this video lecture on Bioinformatics. For more information, log on to- http://shomusbiology.weebly.com/ Download the study materials here- http://shomusbiology.weebly.com/bio-materials.html In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences.[1] Aligned sequences of nucleotide or amino acid residues are typically represented as rows within a matrix. Gaps are inserted between the residues so that identical or similar characters are aligned in successive columns. Sequence alignments are also used for non-biological sequences, such as those present in natural language or in financial data. Very short or very similar sequences can be aligned by hand. However, most interesting problems require the alignment of lengthy, highly variable or extremely numerous sequences that cannot be aligned solely by human effort. Instead, human knowledge is applied in constructing algorithms to produce high-quality sequence alignments, and occasionally in adjusting the final results to reflect patterns that are difficult to represent algorithmically (especially in the case of nucleotide sequences). Computational approaches to sequence alignment generally fall into two categories: global alignments and local alignments. Calculating a global alignment is a form of global optimization that "forces" the alignment to span the entire length of all query sequences. By contrast, local alignments identify regions of similarity within long sequences that are often widely divergent overall. Local alignments are often preferable, but can be more difficult to calculate because of the additional challenge of identifying the regions of similarity. A variety of computational algorithms have been applied to the sequence alignment problem. These include slow but formally correct methods like dynamic programming. These also include efficient, heuristic algorithms or probabilistic methods designed for large-scale database search, that do not guarantee to find best matches. Global alignments, which attempt to align every residue in every sequence, are most useful when the sequences in the query set are similar and of roughly equal size. (This does not mean global alignments cannot end in gaps.) A general global alignment technique is the Needleman--Wunsch algorithm, which is based on dynamic programming. Local alignments are more useful for dissimilar sequences that are suspected to contain regions of similarity or similar sequence motifs within their larger sequence context. The Smith--Waterman algorithm is a general local alignment method also based on dynamic programming. Source of the article published in description is Wikipedia. I am sharing their material. Copyright by original content developers of Wikipedia. Link- http://en.wikipedia.org/wiki/Main_Page
Views: 171874 Shomu's Biology
Transportation problem [ MODI method - U V method - Optimal  Solution ] :-by #kauserwise
 
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NOTE: Formula "pij = ui+vi-Cij" according to this formula the optimal values should be Zero or less than Zero which mean Zero or negative values, and in this formula if we did not reach the optimality then we should select the maximum positive value to proceed further. If you use this Cij-(u1+vj) formula then the values should be zero or positive value to reach the optimality, and in this formula if we did not reach the optimality then we should select the maximum negative value to proceed further. We can apply either any any one of the formula to find out the optimality. So both the formulas are doing same thing only but the values of sign (- +) will be differ. Here is the video about Transportation problem in Modi method-U V method using north west corner method, optimum solution in operation research, with sample problem in simple manner. Hope this will help you to get the subject knowledge at the end. Thanks and All the best. To watch more tutorials pls visit: www.youtube.com/c/kauserwise * Financial Accounts * Corporate accounts * Cost and Management accounts * Operations Research * Statistics ▓▓▓▓░░░░───CONTRIBUTION ───░░░▓▓▓▓ If you like this video and wish to support this kauserwise channel, please contribute via, * Paytm a/c : 6383617203 * Western Union / MoneyGram [ Name: Kauser, Country: India & Email: [email protected] ] [Every contribution is helpful] Thanks & All the Best!!! ───────────────────────────
Views: 2141012 Kauser Wise
Integrating Data Mining with Image Analysis: Definiens Image Miner 2
 
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ARNO SCHÄPE We propose that genetic abnormalities are manifested in subtle phenotypic variations which, with single-cell multiparametric feature analysis, can be identified and leveraged to investigate somatic evolution. Evaluation of distinct subpopulations of cells allows investigators to discern specific morphotypes of cells with dissimilar levels of proliferation, vessel area and density, and prognostic biomarker expression. Furthermore, the development of in-depth feature outlier analysis demonstrates localization patterns for physical features which will allow researchers to probe the underlying biology and details of cancer morphotypic changes July 11, 2012
Views: 1772 DefiniensLifeTV
The Emerging Theory of Algorithmic Fairness
 
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As algorithms reach ever more deeply into our daily lives, increasing concern that they be “fair” has resulted in an explosion of research in the theory and machine learning communities. This talk surveys key results in both areas and traces the arc of the emerging theory of algorithmic fairness. See more at https://www.microsoft.com/en-us/research/video/the-emerging-theory-of-algorithmic-fairness/
Views: 1354 Microsoft Research
Pedro Domingos: "The Master Algorithm" | Talks at Google
 
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Machine learning is the automation of discovery, and it is responsible for making our smartphones work, helping Netflix suggest movies for us to watch, and getting presidents elected. But there is a push to use machine learning to do even more—to cure cancer and AIDS and possibly solve every problem humanity has. Domingos is at the very forefront of the search for the Master Algorithm, a universal learner capable of deriving all knowledge—past, present and future—from data. In this book, he lifts the veil on the usually secretive machine learning industry and details the quest for the Master Algorithm, along with the revolutionary implications such a discovery will have on our society. Pedro Domingos is a Professor of Computer Science and Engineering at the University of Washington, and he is the cofounder of the International Machine Learning Society. https://books.google.com/books/about/The_Master_Algorithm.html?id=glUtrgEACAAJ This Authors at Google talk was hosted by Boris Debic. eBook https://play.google.com/store/books/details/Pedro_Domingos_The_Master_Algorithm?id=CPgqCgAAQBAJ
Views: 117201 Talks at Google
Mod-01 Lec-09 Similarity Coefficient based clustering algorithm
 
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Manufacturing Systems Management by Prof. G. Srinivasan, Department of Management, IITmadras. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 5254 nptelhrd
ConTour: Data-Driven Exploration of Multi-Relational Datasets for Drug Discovery
 
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Demonstration Video for ConTour, the VAST 2014 paper by Christian Partl, Alexander Lex, Marc Streit, Hendrik Strobelt, Anne-Mai Wassermann, Hanspeter Pfister, and Dieter Schmalstieg Details at: http://contour.caleydo.org Large scale data analysis is nowadays a crucial part of drug discovery. Biologists and chemists need to quickly explore and evaluate potentially effective yet safe compounds based on many datasets that are in relationship with each other. However, there is a is a lack of tools that support them in these processes. To remedy this, we developed ConTour, an interactive visual analytics technique that enables the exploration of these complex, multi-relational datasets. At its core ConTour lists all items of each dataset in a column. Relationships between the columns are revealed through interaction: selecting one or multiple items in one column highlights and re-sorts the items in other columns. Filters based on relationships enable drilling down into the large data space. To identify interesting items in the first place, ConTour employs advanced sorting strategies, including strategies based on connectivity strength and uniqueness, as well as sorting based on item attributes. ConTour also introduces interactive nesting of columns, a powerful method to show the related items of a child column for each item in the parent column. Within the columns, ConTour shows rich attribute data about the items as well as information about the connection strengths to other datasets. Finally, ConTour provides a number of detail views, which can show items from multiple datasets and their associated data at the same time. We demonstrate the utility of our system in case studies conducted with a team of chemical biologists, who investigate the effects of chemical compounds on cells and need to understand the underlying mechanisms.
Views: 688 Caleydo Project
Data Science & Machine Learning - Naive Bayes Exercise- DIY- 34 -of-50
 
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Data Science & Machine Learning - Naive Bayes Exercise- DIY- 34 -of-50 Do it yourself Tutorial by Bharati DW Consultancy cell: +1-562-646-6746 (Cell & Whatsapp) email: [email protected] website: http://bharaticonsultancy.in/ Google Drive- https://drive.google.com/open?id=0ByQlW_DfZdxHeVBtTXllR0ZNcEU Hands On – R Machine Learning Ex-15 Use the same dataset as used for k-NN & C5.0 algorithms, but using randomForest predict the values in the following dataset. http://archive.ics.uci.edu/ml/datasets/Glass+Identification Using the Balance Scale Data Set, predict using k-NN & C5.0 trees. http://archive.ics.uci.edu/ml/datasets/Balance+Scale Using the different “types” in the prediction step in randomForest. Compare k-NN, C5.0 and randomForest models for the above datasets. Data Science & Machine Learning - Getting Started - DIY- 1 -of-50 Data Science & Machine Learning - R Data Structures - DIY- 2 -of-50 Data Science & Machine Learning - R Data Structures - Factors - DIY- 3 -of-50 Data Science & Machine Learning - R Data Structures - List & Matrices - DIY- 4 -of-50 Data Science & Machine Learning - R Data Structures - Data Frames - DIY- 5 -of-50 Data Science & Machine Learning - Frequently used R commands - DIY- 6 -of-50 Data Science & Machine Learning - Frequently used R commands contd - DIY- 7 -of-50 Data Science & Machine Learning - Installing RStudio- DIY- 8 -of-50 Data Science & Machine Learning - R Data Visualization Basics - DIY- 9 -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(a) -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(b) -of-50 Data Science & Machine Learning - Multiple Linear Regression Model - DIY- 11 -of-50 Data Science & Machine Learning - Evaluate Model Performance - DIY- 12 -of-50 Data Science & Machine Learning - RMSE & R-Squared - DIY- 13 -of-50 Data Science & Machine Learning - Numeric Predictions using Regression Trees - DIY- 14 -of-50 Data Science & Machine Learning - Regression Decision Trees contd - DIY- 15 -of-50 Data Science & Machine Learning - Method Types in Regression Trees - DIY- 16 -of-50 Data Science & Machine Learning - Real Time Project 1 - DIY- 17 -of-50 Data Science & Machine Learning - KNN Classification - DIY- 21 -of-50 Data Science & Machine Learning - KNN Classification Hands on - DIY- 22 -of-50 Data Science & Machine Learning - KNN Classification HandsOn Contd - DIY- 23 -of-50 Data Science & Machine Learning - KNN Classification Exercise - DIY- 24 -of-50 Data Science & Machine Learning - C5.0 Decision Tree Intro - DIY- 25 -of-50 Data Science & Machine Learning - C5.0 Decision Tree Use Case - DIY- 26 -of-50 Data Science & Machine Learning - C5.0 Decision Tree Exercise - DIY- 27 -of-50 Data Science & Machine Learning - Random Forest Intro - DIY- 28 -of-50 Data Science & Machine Learning - Random Forest Hands on - DIY- 29 -of-50 Data Science & Machine Learning - Naive Bayes - DIY- 31 -of-50 Data Science & Machine Learning - Naive Bayes Handson- DIY- 32 -of-50 Data Science & Machine Learning - Naive Bayes Handson contd- DIY- 33 -of-50 Data Science & Machine Learning - Naive Bayes Exercise- DIY- 34 -of-50 Machine learning, data science, R programming, Deep Learning, Regression, Neural Network, R Data Structures, Data Frame, RMSE & R-Squared, Regression Trees, Decision Trees, Real-time scenario, KNN, C5.0 Decision Tree, Random Forest, Naive Bayes
Complete Data Science Course | What is Data Science? | Data Science for Beginners | Edureka
 
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** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification ** This Edureka video on "Data Science" provides an end to end, detailed and comprehensive knowledge on Data Science. This Data Science video will start with basics of Statistics and Probability and then move to Machine Learning and Finally end the journey with Deep Learning and AI. For Data-sets and Codes discussed in this video, drop a comment. This video will be covering the following topics: 1:23 Evolution of Data 2:14 What is Data Science? 3:02 Data Science Careers 3:36 Who is a Data Analyst 4:20 Who is a Data Scientist 5:14 Who is a Machine Learning Engineer 5:44 Salary Trends 6:37 Road Map 9:06 Data Analyst Skills 10:41 Data Scientist Skills 11:47 ML Engineer Skills 12:53 Data Science Peripherals 13:17 What is Data ? 15:23 Variables & Research 17:28 Population & Sampling 20:18 Measures of Center 20:29 Measures of Spread 21:28 Skewness 21:52 Confusion Matrix 22:56 Probability 25:12 What is Machine Learning? 25:45 Features of Machine Learning 26:22 How Machine Learning works? 27:11 Applications of Machine Learning 34:57 Machine Learning Market Trends 36:05 Machine Learning Life Cycle 39:01 Important Python Libraries 40:56 Types of Machine Learning 41:07 Supervised Learning 42:27 Unsupervised Learning 43:27 Reinforcement Learning 46:27 Supervised Learning Algorithms 48:01 Linear Regression 58:12 What is Logistic Regression? 1:01:22 What is Decision Tree? 1:11:10 What is Random Forest? 1:18:48 What is Naïve Bayes? 1:30:51 Unsupervised Learning Algorithms 1:31:55 What is Clustering? 1:34:02 Types of Clustering 1:35:00 What is K-Means Clustering? 1:47:31 Market Basket Analysis 1:48:35 Association Rule Mining 1:51:22 Apriori Algorithm 2:00:46 Reinforcement Learning Algorithms 2:03:22 Reward Maximization 2:06:35 Markov Decision Process 2:08:50 Q-Learning 2:18:19 Relationship Between AI and ML and DL 2:20:10 Limitations of Machine Learning 2:21:19 What is Deep Learning ? 2:22:04 Applications of Deep Learning 2:23:35 How Neuron Works? 2:24:17 Perceptron 2:25:12 Waits and Bias 2:25:36 Activation Functions 2:29:56 Perceptron Example 2:31:48 What is TensorFlow? 2:37:05 Perceptron Problems 2:38:15 Deep Neural Network 2:39:35 Training Network Weights 2:41:04 MNIST Data set 2:41:19 Creating a Neural Network 2:50:30 Data Science Course Masters Program Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS Machine Learning Podcast: https://castbox.fm/channel/id1832236 Instagram: https://www.instagram.com/edureka_learning Slideshare: https://www.slideshare.net/EdurekaIN/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka #edureka #DataScienceEdureka #whatisdatascience #Datasciencetutorial #Datasciencecourse #datascience - - - - - - - - - - - - - - About the Master's Program This program follows a set structure with 6 core courses and 8 electives spread across 26 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands on expertise. By the end of the program you will be ready for seasoned Data Science job roles. - - - - - - - - - - - - - - Topics Covered in the curriculum: Topics covered but not limited to will be : Machine Learning, K-Means Clustering, Decision Trees, Data Mining, Python Libraries, Statistics, Scala, Spark Streaming, RDDs, MLlib, Spark SQL, Random Forest, Naïve Bayes, Time Series, Text Mining, Web Scraping, PySpark, Python Scripting, Neural Networks, Keras, TFlearn, SoftMax, Autoencoder, Restricted Boltzmann Machine, LOD Expressions, Tableau Desktop, Tableau Public, Data Visualization, Integration with R, Probability, Bayesian Inference, Regression Modelling etc. - - - - - - - - - - - - - - For more information, Please write back to us at [email protected] or call us at: IND: 9606058406 / US: 18338555775 (toll free)
Views: 29238 edureka!
Database Clustering Tutorial 3 - Setting up a Database Cluster
 
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Read the Blog: https://www.calebcurry.com/blogs/database-clustering/setting-up-a-database-cluster Get ClusterControl: http://bit.ly/ClusterControl How do you actually go about setting up a cluster? Well, unfortunately, it’s not the easiest process in the world. The good part, though, is that it is quite the learning experience, and it’s not really hard, it just takes some time. One of the reasons it can take so much time is because not everyone has a bunch of database servers laying around to setup a cluster. But don’t worry, I’m gonna be teach you everything you need to know to set up a database cluster and start working with your data. I’ll also show you how to do it using virtual machines so you don’t have to set up a bunch of computers. To top it off, I’ll do it completely free of charge. Your welcome. :) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Support me! http://www.patreon.com/calebcurry Subscribe to my newsletter: http://bit.ly/JoinCCNewsletter Donate!: http://bit.ly/DonateCTVM2. ~~~~~~~~~~~~~~~Additional Links~~~~~~~~~~~~~~~ More content: http://CalebCurry.com Facebook: http://www.facebook.com/CalebTheVideoMaker Google+: https://plus.google.com/+CalebTheVideoMaker2 Twitter: http://twitter.com/calebCurry Amazing Web Hosting - http://bit.ly/ccbluehost (The best web hosting for a cheap price!)
Views: 4735 Caleb Curry
Data Science & Machine Learning - Naive Bayes - DIY- 31 -of-50
 
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Data Science & Machine Learning - Naive Bayes - DIY- 31 -of-50 Do it yourself Tutorial by Bharati DW Consultancy cell: +1-562-646-6746 (Cell & Whatsapp) email: [email protected] website: http://bharaticonsultancy.in/ Google Drive- https://drive.google.com/open?id=0ByQlW_DfZdxHeVBtTXllR0ZNcEU Naïve Bayes – Probabilistic Classification Naïve Bayes technique descended from the work of a mathematician Thomas Bayes, who developed foundational principles to describe the probability of events, and how probabilities they change with additional information. This method utilizes training data to calculate an observed probability of each outcome based on the feature values. p(A|B) = (p(A) * p(B|A) ) / p(B) This trained classifier when applied to an unlabeled data, predicts the most likely class for the new features using the observed probabilities. It assumes that all of the features in the dataset are equally important and independent. These assumptions aren’t always correct. Naïve Bayes model still preforms well, when these assumptions are violated, as it’s very versatile and accurate across many types of conditions it is often a strong first candidate for classification learning tasks. Naïve Bayes algorithm works best on categorical data as it uses frequency table to learn the number of occurrences. However, this feature doesn’t work best on Numeric data; a solution to this problem is to create bins. Get the data from UCI YouTube+Spam+Collection Dataset Citation Request: We would appreciate:  1. If you find this collection useful, make a reference to the paper below and the web page: [Web Link].  2. Send us a message either to talmeida AT ufscar.br or tuliocasagrande AT acm.org in case you make use of the corpus.  http://dcomp.sor.ufscar.br/talmeida/youtubespamcollection/ Load and clean up the data, divide the text into individual words using tm_map(). #lower case #remove stopwords / filler words such as to, and, but etc. #remove punctuations #remove numbers #strip white spaces; #Stemming; Create a DTM sparse matrix – a table with the frequency of words in each line. Naïve Bayes Model – without & with Laplace Estimator Data Science & Machine Learning - Getting Started - DIY- 1 -of-50 Data Science & Machine Learning - R Data Structures - DIY- 2 -of-50 Data Science & Machine Learning - R Data Structures - Factors - DIY- 3 -of-50 Data Science & Machine Learning - R Data Structures - List & Matrices - DIY- 4 -of-50 Data Science & Machine Learning - R Data Structures - Data Frames - DIY- 5 -of-50 Data Science & Machine Learning - Frequently used R commands - DIY- 6 -of-50 Data Science & Machine Learning - Frequently used R commands contd - DIY- 7 -of-50 Data Science & Machine Learning - Installing RStudio- DIY- 8 -of-50 Data Science & Machine Learning - R Data Visualization Basics - DIY- 9 -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(a) -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(b) -of-50 Data Science & Machine Learning - Multiple Linear Regression Model - DIY- 11 -of-50 Data Science & Machine Learning - Evaluate Model Performance - DIY- 12 -of-50 Data Science & Machine Learning - RMSE & R-Squared - DIY- 13 -of-50 Data Science & Machine Learning - Numeric Predictions using Regression Trees - DIY- 14 -of-50 Data Science & Machine Learning - Regression Decision Trees contd - DIY- 15 -of-50 Data Science & Machine Learning - Method Types in Regression Trees - DIY- 16 -of-50 Data Science & Machine Learning - Real Time Project 1 - DIY- 17 -of-50 Data Science & Machine Learning - KNN Classification - DIY- 21 -of-50 Data Science & Machine Learning - KNN Classification Hands on - DIY- 22 -of-50 Data Science & Machine Learning - KNN Classification HandsOn Contd - DIY- 23 -of-50 Data Science & Machine Learning - KNN Classification Exercise - DIY- 24 -of-50 Data Science & Machine Learning - C5.0 Decision Tree Intro - DIY- 25 -of-50 Data Science & Machine Learning - C5.0 Decision Tree Use Case - DIY- 26 -of-50 Data Science & Machine Learning - C5.0 Decision Tree Exercise - DIY- 27 -of-50 Data Science & Machine Learning - Random Forest Intro - DIY- 28 -of-50 Data Science & Machine Learning - Random Forest Hands on - DIY- 29 -of-50 Data Science & Machine Learning - Naive Bayes - DIY- 31 -of-50 Machine learning, data science, R programming, Deep Learning, Regression, Neural Network, R Data Structures, Data Frame, RMSE & R-Squared, Regression Trees, Decision Trees, Real-time scenario, KNN, C5.0 Decision Tree, Random Forest, Naive Bayes
Statistical Aspects of Data Mining (Stats 202) Day 1
 
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Google Tech Talks June 26, 2007 ABSTRACT This is the Google campus version of Stats 202 which is being taught at Stanford this summer. I will follow the material from the Stanford class very closely. That material can be found at www.stats202.com. The main topics are exploring and visualizing data, association analysis, classification, and clustering. The textbook is Introduction to Data Mining by Tan, Steinbach and Kumar. Googlers are welcome to attend any classes which they think might be of interest to them. Credits: Speaker:David Mease
Views: 216430 GoogleTechTalks
Metro Maps of Plant Disease Dynamics - Automated Mining of Differences Using Hyperspectral Images
 
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Mirwaes Wahabzada, Anne-Katrin Mahlein, Christian Bauckhage, Ulrike Steiner, Erich-Christian Oerke, Kristian Kersting PLoS ONE 10(1): e0116902
Views: 284 sciTrailer
SMS SPAM-HAM Detection using Artificial Neural Networks
 
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This project was done as part of the Data Mining course at USF.
Views: 1677 Vishal Punjabi
LSTM Part 1
 
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Using Keras to implement LSTMs. LSTMs are a certain set of RNNs that perform well compared to vanilla LSTMs. Code: https://github.com/sachinruk/PyData_Keras_Talk The following two blogs have really helped me understand LSTMs and are valuable resources. Please read both. LSTM theory: colah.github.io/posts/2015-08-Understanding-LSTMs/ Code reference: http://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/
Views: 24385 The Math Student
The similarity distance on graphs and graphons
 
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The similarity distance measures how "similar" two nodes in a dense graph are. Selecting an epsilon-net with respect to this metric is a useful tool in algorithms for very large graphs. For example, the Voronoi cells of such a set form a weak regularity partition. One can introduce the same distance on graph limits (graphons). This defines a compact metric space, whose dimension is an important complexity measure of the graphon and of any graph sequence converging to it. Graphons for which this dimension is finite have polynomial-size weak regularity partitions. We will state some sufficient conditions, some proven and some conjectured, for this dimension to be finite.
Views: 981 Microsoft Research
Luc Rocher - bandicoot: a toolbox to analyze mobile phone metadata
 
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PyData London 2016 Bandicoot is a free and open-source toolbox to process mobile phone metadata. It provides standardized and privacy-preserving methods to analyze such datasets, returning more than 160 behavioral indicators. Bandicoot is a complete easy-to-use environment for researchers and developers, allowing them to load their data, perform analysis, and export their results with a few lines of code. The metadata generated at large scale by cellphones and collected by literally every carrier around the world have the potential to fundamentally transform the way we fight diseases, design transportation systems, and do research. Scientists have compared the recent availability of these large-scale behavioral data sets to the invention of the microscope and new fields such as Computational Social Science have recently emerged. Mobile phone metadata have, for example, already been used to study human mobility and behavior in cities, understand the propagation of viruses such as malaria and dengue fever. They have been combined with machine learning algorithms to predict people's age, gender, personality, loan repayments, and crime. Bandicoot is a free and open-source Python toolbox to extract more than 160 features from standard mobile phone metadata. bandicoot focuses on making it easy for researchers and practitioners to load mobile phone data, analyze them, as well as compute and extract robust features from them. Emphasis is put on ease of use, consistency, and documentation. bandicoot has no dependencies and is distributed under the MIT license. bandicoot indicators: individual, spatial, and network features In this talk, we provide an introduction to bandicoot via real life case studies, showing you how to visualise and analyze large scale data sets, or directly metadata from your own phone. Materials available here: https://github.com/cynddl/pydata-london-2016
Views: 867 PyData
9. Modeling and Discovery of Sequence Motifs
 
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MIT 7.91J Foundations of Computational and Systems Biology, Spring 2014 View the complete course: http://ocw.mit.edu/7-91JS14 Instructor: Christopher Burge This lecture by Prof. Christopher Burge covers modeling and discovery of sequence motifs. He gives the example of the Gibbs sampling algorithm. He covers information content of a motif, and he ends with parameter estimation for motif models. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 10693 MIT OpenCourseWare
MIMO Technology in Wireless Communication | MIMO in LTE Explained in Hindi
 
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Welcome Guys, We will see MIMO technology in wireless communication in details. What is MIMO in Hindi? MIMO (multiple inputs, multiple outputs) is an antenna technology for wireless communications in which multiple antennas are used at both the source (transmitter) and the destination (receiver). The antennas at each end of the communications circuit are combined to minimize errors and optimize data speed. The existence of multiple antennas in a system means the existence of different propagation paths. Aiming at improving the reliability of the system, we may choose to send same data across the different propagation (spatial) paths. This is called spatial diversity or simply diversity. Aiming at improving the data rate of the system, we may choose to place different portions of the data on different propagation paths (spatial-multiplexing). advantages of MIMO: ➨The higher data rate can be achieved with the help of multiple antennas and SM (Spatial Multiplexing) technique. This helps in achieving higher downlink and uplink throughput. ➨MIMO based system minimize fading effects seen by the information traveling from transmitting to receiving end. This is due to various diversity techniques such as time, frequency and space. disadvantages of MIMO: ➨The resource requirements and hardware complexity are higher compared to the single antenna based system. Each antenna requires individual RF units for radio signal processing. Moreover, advanced DSP chip is needed to run advanced mathematical signal processing algorithms. ➨The hardware resources increase power requirements. Battery gets drain faster due to the processing of complex and computationally intensive signal processing algorithms. This reduces battery lifetime of MIMO based devices. ➨MIMO based systems cost higher compared to the single antenna based system due to increased hardware and advanced software requirements. If you like this video plz LIKE SHARE AND SUBSCRIBE my channel :)
Views: 9114 Thapa Technical
Productionizing Machine learning | visualization of Machine Learning | Hackerearth Webinar
 
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Participate in # UNITEDBYHCL Hackathon : https://goo.gl/Z7oSKK About the Webinar : This tutorial would give a quick recap of what Machine learning is and various learning branches and algorithms at a broader level. Start with understanding the top on the ground challenges faced while applying machine learning in the real-world problems and how data appropriateness, scale, and real-time responsiveness become key to the solution. We will touch upon an architecture approaches (like Lambda, Kappa Blackboard, and Semantic architectures) that are evolving and how they solve the need for scale in real-time. Finally, a brief on how visualization determines the success of a machine learning project. About the Host : The Speaker is Sunila Gollapudi. She is Vice President, Technology (Broadridge), Author (Machine learning & Big Data) and Enterprise Architecture Evangelist. More webinars and updates : https://goo.gl/MEAALs
Views: 1257 HackerEarth
Horizontal Aggregations in SQL to Prepare Data Sets for Data Mining Analysis
 
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To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, 45, KAMARAJ SALAI, THATTANCHAVADY, PUDUCHERRY-9 Landmark: Opposite to Thattanchavady Industrial Estate, Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com Horizontal Aggregations in SQL to Prepare Data Sets for Data Mining Analysis Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations. Horizontal aggregations build data sets with a horizontal denormalized layout (e.g. point-dimension, observation-variable, instance-feature), which is the standard layout required by most data mining algorithms. We propose three fundamental methods to evaluate horizontal aggregations: CASE: Exploiting the programming CASE construct; SPJ: Based on standard relational algebra operators (SPJ queries); PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables compare the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, whereas the SPJ method does not.
Symposium on Blockchain for Robotic Systems
 
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Robotic systems are revolutionizing applications from transportation to health care. However, many of the characteristics that make robots ideal for future applications—such as autonomy, self-learning, and knowledge sharing—also raise concerns about the evolution of the technology. Blockchain, an emerging technology that originated in the digital currency field, shows great potential to make robotic operations more secure, autonomous, flexible, and even profitable, thereby bridging the gap between purely scientific domains and real-world applications. This symposium seeks to move beyond the classical view of robotic systems to advance our understanding about the possibilities and limitations of combining state-of-the art robotic systems with blockchain technology. More information at: https://www.media.mit.edu/events/symposium-on-blockchain-for-robotics/ License: CC-BY-4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
Views: 2323 MIT Media Lab
Azure Machine Learning Studio: One-v-all multiclass
 
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Dataset: http://www.ishelp.info/sites/yt/bikebuyers.csv Next video: https://www.youtube.com/watch?v=ZQN6P_zcp5I&index=27&list=PLe9UEU4oeAuXMUWqhhJQrGVWzUWY6pS9j Prior video: https://www.youtube.com/watch?v=V3OLeGpmHfE&list=PLe9UEU4oeAuXMUWqhhJQrGVWzUWY6pS9j&index=25
Views: 1520 Mark Keith

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