Search results “Audio lectures on data mining”
Data Structures and Algorithms Complete Tutorial Computer Education for All
Computer Education for all provides complete lectures series on Data Structure and Applications which covers Introduction to Data Structure and its Types including all Steps involves in Data Structures:- Data Structure and algorithm Linear Data Structures and Non-Linear Data Structure on Stack Data Structure on Arrays Data Structure on Queue Data Structure on Linked List Data Structure on Tree Data Structure on Graphs Abstract Data Types Introduction to Algorithms Classifications of Algorithms Algorithm Analysis Algorithm Growth Function Array Operations Two dimensional Arrays Three Dimensional Arrays Multidimensional arrays Matrix operations Operations on linked lists Applications of linked lists Doubly linked lists Introductions to stacks Operations on stack Array based implementation of stack Queue Data Structures Operations on Queues Linked list based implementation of queues Application of Trees Binary Trees Types of Binary Trees Implementation of Binary Trees Binary Tree Traversal Preorder Post order In order Binary Search Tree Introduction to Sorting Analysis of Sorting Algorithms Bubble Sort Selection Sort Insertion Sort Shell Sort Heap Sort Merge Sort Quick Sort Applications of Graphs Matrix representation of Graphs Implementations of Graphs Breadth First Search Topological Sorting Subscribe for More https://www.youtube.com/channel/UCiV37YIYars6msmIQXopIeQ Find us on Facebook: https://web.facebook.com/Computer-Education-for-All-1484033978567298 Java Programming Complete Tutorial for Beginners to Advance | Complete Java Training for all https://youtu.be/gg2PG3TwLx4
Current trends in Data Mining..
Topic described here are: Multimedia datamining Ubiquitous datamining Distributed datamining Spatial datamining Time series datamining Text mining Video mining Image mining Audio mining multimedia issues Submitted by: A. Vaishnavi II Msc cs A 175214141
Views: 367 vaishu raj
Introduction to Database Management Systems 1: Fundamental Concepts
This is the first chapter in the web lecture series of Prof. dr. Bart Baesens: Introduction to Database Management Systems. Prof. dr. Bart Baesens holds a PhD in Applied Economic Sciences from KU Leuven University (Belgium). He is currently an associate professor at KU Leuven, and a guest lecturer at the University of Southampton (United Kingdom). He has done extensive research on data mining and its applications. For more information, visit http://www.dataminingapps.com In this lecture, the fundamental concepts behind databases, database technology, database management systems and data models are explained. Discussed topics entail: applications, definitions, file based vs. databased data management approaches, the elements of database systems and the advantages of database design.
Views: 306245 Bart Baesens
"Data Science":What Is Data Mining | Types of Data Mining | Data Science(2019) -ExcelR
#Datamining #Dataminingmethods #datamining(2019) ExcelR : Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Random forest algorithm is a supervised classification algorithm. As the name suggest, this algorithm creates the forest with a number of trees. Things we will learn in this video: 1)What is Data Mining? 2)Types of Data Mining 3)What is supervised learning? 4)What is Unsupervised Learning? 5)Process for Data mining To buy eLearning course on Data Science 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 Introduction To Data mining using R click here https://goo.gl/muRASy For Peer-To-Peer Network Analysis click here https://goo.gl/HcAjqu ----- For More Information: 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 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
Labeled vs Unlabeled data || Machine Learning || Lecture # 04
This is the fourth lecture of “Machine Learning” tutorials. In this lecture we define difference between Labeled and Unlabeled data. Unlabeled Data. Raw form of the data is called unlabeled data. Or The data to which tag or label is not attached is called unlabeled data. For example, video streams, audio, photos, and tweets among others. This form of data usually has no explanation of the meaning attached. Unsupervised learning adopts unlabeled data. Semi-supervised learning and deep learning techniques apply a combination of labeled and unlabeled data in a variety of ways to build accurate models. Labeled Data. The unlabeled data becomes labeled data the moment a meaning is attached. Here, we are talking about attaching a "tag" or "label" that is required, and is mandatory, to interpret and define the relevance. For example, labels for a photo can be the details of what it contains, such as animal, tree, college, and so on, or, in the context of an audio file, a political meeting, a farewell party, and so on. More often, the labels are mapped or defined by humans. Supervised learning adopts labeled data. Semi-supervised learning and deep learning techniques apply a combination of labeled and unlabeled data in a variety of ways to build accurate models.
Views: 254 Tech Series
Mod-01 Lec-04 Clustering vs. Classification
Pattern Recognition by Prof. C.A. Murthy & Prof. Sukhendu Das,Department of Computer Science and Engineering,IIT Madras.For more details on NPTEL visit http://nptel.ac.in
Views: 21125 nptelhrd
Sketching Streaming Data: Efficient Collection & Processing | Lectures On-Demand
Professor Anna Gilbert, Department of Mathematics - University of Michigan Data Mining- 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.
UVic MIR Course Data Mining I
In this lecture we will cover the basic concepts behind machine learning and data mining with emphasis on supervised learning and classification. Without going into details about classification we will talk about how to evaluate classification performance.
Web Mining - Tutorial
Web Mining Web Mining is the use of Data mining techniques to automatically discover and extract information from World Wide Web. There are 3 areas of web Mining Web content Mining. Web usage Mining Web structure Mining. Web content Mining Web content Mining is the process of extracting useful information from content of web document.it may consists of text images,audio,video or structured record such as list & tables. screen scaper,Mozenda,Automation Anywhere,Web content Extractor, Web info extractor are the tools used to extract essential information that one needs. Web Usage Mining Web usage Mining is the process of identifying browsing patterns by analysing the users Navigational behaviour. Techniques for discovery & pattern analysis are two types. They are Pattern Analysis Tool. Pattern Discovery Tool. Data pre processing,Path Analysis,Grouping,filtering,Statistical Analysis, Association Rules,Clustering,Sequential Pattterns,classification are the Analysis done to analyse the patterns. Web structure Mining Web structure Mining is a tool, used to extract patterns from hyperlinks in the web. Web structure Mining is also called link Mining. HITS & PAGE RANK Algorithm are the Popular Web structure Mining Algorithm. By applying Web content mining,web structure Mining & Web usage Mining knowledge is extracted from web data.
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language. The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text. NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more! Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK! Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 448367 sentdex
Data Warehousing and Data Mining
This course aims to introduce advanced database concepts such as data warehousing, data mining techniques, clustering, classifications and its real time applications. SlideTalk video created by SlideTalk at http://slidetalk.net, the online solution to convert powerpoint to video with automatic voice over.
Views: 4670 SlideTalk
Applications of Predictive Analytics in Legal | Litigation Analytics, Data Mining & AI | Great Lakes
#PredictiveAnalytics | Learn the prediction of outcome or treatment of a case by legal courts of Appeals based on historical data using predictive analytics. Watch the video to understand analytics in legal using case study on real-life data set. How litigation analytics can flourish with the use of data mining and AI. Know more about our analytics Program: PGP- Business Analytics: https://goo.gl/V9RzVD PGP- Big Data Analytics: https://goo.gl/rRyjj4 Business Analytics Certification Program: https://goo.gl/7HPoUY #LegalTech #LegalAnalytics #GreatLearning #GreatLakes About Great Learning: - Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. - Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM - What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U - Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670 - Want to know more about the careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU - For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1 - Learn More at: https://www.greatlearning.in/ For more updates on courses and tips follow us on: - Google Plus: https://plus.google.com/u/0/108438615307549697541 - Facebook: https://www.facebook.com/GreatLearningOfficial/ - LinkedIn: https://www.linkedin.com/company/great-learning/ - Follow our Blog: https://www.greatlearning.in/blog/?utm_source=Youtube
Views: 940 Great Learning
Data Mining: How You're Revealing More Than You Think
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: 146975 SciShow
Decision Tree Algorithm With Example | Decision Tree In Machine Learning | Data Science |Simplilearn
This Decision Tree algorithm in Machine Learning tutorial video will help you understand all the basics of Decision Tree along with what is Machine Learning, problems in Machine Learning, what is Decision Tree, advantages and disadvantages of Decision Tree, how Decision Tree algorithm works with solved examples and at the end we will implement a Decision Tree use case/ demo in Python on loan payment prediction. This Decision Tree tutorial is ideal for both beginners as well as professionals who want to learn Machine Learning Algorithms. Below topics are covered in this Decision Tree Algorithm Tutorial: 1. What is Machine Learning? ( 02:25 ) 2. Types of Machine Learning? ( 03:27 ) 3. Problems in Machine Learning ( 04:43 ) 4. What is Decision Tree? ( 06:29 ) 5. What are the problems a Decision Tree Solves? ( 07:11 ) 6. Advantages of Decision Tree ( 07:54 ) 7. How does Decision Tree Work? ( 10:55 ) 8. Use Case - Loan Repayment Prediction ( 14:32 ) What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Decision-Tree-Algorithm-With-Example-RmajweUFKvM&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Decision-Tree-Algorithm-With-Example-RmajweUFKvM&utm_medium=Tutorials&utm_source=youtube #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems - - - - - - - Who should take this Machine Learning Training Course? We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning - - - - - - For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 38986 Simplilearn
Audio Mining Enabler - Presentation
A short presentation of the Audio Mining FIWARE Enabler developed by Fraunhofer IAIS, under the FIcontent project.
Views: 257 FIcontent
UVic MIR Course - Data Mining III
In this lecture I will talk about discriminative classifiers. More specifically perceptrons, artificial neural networks and support vector machines.
R tutorial: What is text mining?
Learn more about text mining: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words Hi, I'm Ted. I'm the instructor for this intro text mining course. Let's kick things off by defining text mining and quickly covering two text mining approaches. Academic text mining definitions are long, but I prefer a more practical approach. So text mining is simply the process of distilling actionable insights from text. Here we have a satellite image of San Diego overlaid with social media pictures and traffic information for the roads. It is simply too much information to help you navigate around town. This is like a bunch of text that you couldn’t possibly read and organize quickly, like a million tweets or the entire works of Shakespeare. You’re drinking from a firehose! So in this example if you need directions to get around San Diego, you need to reduce the information in the map. Text mining works in the same way. You can text mine a bunch of tweets or of all of Shakespeare to reduce the information just like this map. Reducing the information helps you navigate and draw out the important features. This is a text mining workflow. After defining your problem statement you transition from an unorganized state to an organized state, finally reaching an insight. In chapter 4, you'll use this in a case study comparing google and amazon. The text mining workflow can be broken up into 6 distinct components. Each step is important and helps to ensure you have a smooth transition from an unorganized state to an organized state. This helps you stay organized and increases your chances of a meaningful output. The first step involves problem definition. This lays the foundation for your text mining project. Next is defining the text you will use as your data. As with any analytical project it is important to understand the medium and data integrity because these can effect outcomes. Next you organize the text, maybe by author or chronologically. Step 4 is feature extraction. This can be calculating sentiment or in our case extracting word tokens into various matrices. Step 5 is to perform some analysis. This course will help show you some basic analytical methods that can be applied to text. Lastly, step 6 is the one in which you hopefully answer your problem questions, reach an insight or conclusion, or in the case of predictive modeling produce an output. Now let’s learn about two approaches to text mining. The first is semantic parsing based on word syntax. In semantic parsing you care about word type and order. This method creates a lot of features to study. For example a single word can be tagged as part of a sentence, then a noun and also a proper noun or named entity. So that single word has three features associated with it. This effect makes semantic parsing "feature rich". To do the tagging, semantic parsing follows a tree structure to continually break up the text. In contrast, the bag of words method doesn’t care about word type or order. Here, words are just attributes of the document. In this example we parse the sentence "Steph Curry missed a tough shot". In the semantic example you see how words are broken down from the sentence, to noun and verb phrases and ultimately into unique attributes. Bag of words treats each term as just a single token in the sentence no matter the type or order. For this introductory course, we’ll focus on bag of words, but will cover more advanced methods in later courses! Let’s get a quick taste of text mining!
Views: 26019 DataCamp
Real Time Data Mining on FPGA
Gidel FPGA acceleration platforms company and Xelera a big data analytics acceleration company are joining forces to show a complete data flow for Real Time Data Mining on FPGA targeting the Data Centers Market
Data mining and Preprocessing using python
About data mining and why do we use it before making ML models out of data.
Views: 81 Mayank Kumar
Data Mining with Canvas | InstructureCon 2011
Seth Gurell demonstrates how Utah Valley University is using data mining within Instructure Canvas to improve student learning.
Views: 579 CanvasLMS
Import Data and Analyze with MATLAB
Data are frequently available in text file format. This tutorial reviews how to import data, create trends and custom calculations, and then export the data in text file format from MATLAB. Source code is available from http://apmonitor.com/che263/uploads/Main/matlab_data_analysis.zip
Views: 377613 APMonitor.com
Facebook CEO Mark Zuckerberg testifies before Congress on data scandal
Facebook CEO Mark Zuckerberg will testify today before a U.S. congressional hearing about the use of Facebook data to target voters in the 2016 election. Zuckerberg is expected to offer a public apology after revelations that Cambridge Analytica, a data-mining firm affiliated with Donald Trump's presidential campaign, gathered personal information about 87 million users to try to influence elections. »»» Subscribe to CBC News to watch more videos: http://bit.ly/1RreYWS Connect with CBC News Online: For breaking news, video, audio and in-depth coverage: http://bit.ly/1Z0m6iX Find CBC News on Facebook: http://bit.ly/1WjG36m Follow CBC News on Twitter: http://bit.ly/1sA5P9H For breaking news on Twitter: http://bit.ly/1WjDyks Follow CBC News on Instagram: http://bit.ly/1Z0iE7O Download the CBC News app for iOS: http://apple.co/25mpsUz Download the CBC News app for Android: http://bit.ly/1XxuozZ »»»»»»»»»»»»»»»»»» For more than 75 years, CBC News has been the source Canadians turn to, to keep them informed about their communities, their country and their world. Through regional and national programming on multiple platforms, including CBC Television, CBC News Network, CBC Radio, CBCNews.ca, mobile and on-demand, CBC News and its internationally recognized team of award-winning journalists deliver the breaking stories, the issues, the analyses and the personalities that matter to Canadians.
Views: 132462 CBC News
Weka Data Mining Tutorial for First Time & Beginner Users
23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 456038 Brandon Weinberg
Data Science vs Big Data vs Data Analytics | Simplilearn
Data is everywhere. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. According to IBM, 2.5 billion gigabytes (GB) of data was generated every day in 2012. An article by Forbes states that Data is growing faster than ever before and by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet. Which makes it extremely important to at least know the basics of the field. After all, here is where our future lies. In this video, we will differentiate between the Data Science, Big Data, and Data Analytics, based on what it is, where it is used, the skills you need to become a professional in the field, and the salary prospects in each field. For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 178625 Simplilearn
Data Mining - Learning & Selecting Features Jointly w/ Pointwise Gated Boltzmann Machines
Honglak Lee, Assistant Professor - Computer Science and Engineering, 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.
Ted Dunning on Twitter's Storm, Part 2/3
On March 29, 2012 at SF Data Mining *video camera cuts out for a few minutes and audio is included to fill the gap*
Views: 1461 SF Data Mining
Mining Domain Knowledge
Author Jane Cleland-Huang provides an audio recording of the Requirements column, in which she discusses how basic data-mining skills can be useful for processing domain documents early during requirements engineering. From IEEE Software's May/June 2015 issue: http://www.computer.org/csdl/mags/so/2015/03/index.html. Visit IEEE Software: http://www.computer.org/software.
Views: 611 ieeeComputerSociety
#7 Audio Normalization by MATLAB
Audio Processing #7 - Jarvus Studio / 音訊處理 將音訊進行正規化至特定強度 1. Normalize speeches 2. Scale speech by its peak value Source Code https://www.mathworks.com/matlabcentral/fileexchange/69958-audio-normalization-by-matlab Sessions Overview http://jarvus.dragonbeef.net/note/noteAudio.php Case Discussion [email protected]
Views: 192 Yiwen Chen
Deep Learning through Examples Screencast with Audio 9/11/14
Screencast for the Silicon Valley Big Data Science Meetup held by 0xdata, Inc., the makers of H2O, at Vendavo, Inc. in Mountain View on Sep 11 2014. Meetup Link: http://www.meetup.com/Silicon-Valley-Big-Data-Science/events/200244562/ Slides: http://www.slideshare.net/0xdata/deep-learning-through-examples-kaggle-1 Don’t just consume, contribute your code and join the movement: https://github.com/h2oai User conference slides on open source machine learning software from H2O.ai at: http://www.slideshare.net/0xdata
Views: 5775 H2O.ai
Audio Files Clustering
This project presents an automated sound clustering method depending on machine learning and music information retrieval (MIR). Allowing people to search their favorite song or music and listen to the most similar ones throw a graph of songs, where each node represent a song and the size of the node indicate to more similarity. Using: ElasticSearch - Spark - Hadoop - Laravel By : Usama Albaghdady https://www.linkedin.com/in/usama-albaghdady-76944057 Ola Tabbal https://www.facebook.com/ola.tabbal1
Views: 356 Usama Albaghdady
Big Data: from Data to Decisions - free online course at FutureLearn.com
Sign up now at http://bit.ly/2oDQp2g 'Big Data: from Data to Decisions' is a free online course by Queensland University of Technology available on FutureLearn.com Data is everywhere and can be obtained from many different sources. Digital data can be obtained from social media, images, audio recordings and sensors, and electronic data is quite often available as real-time data streams. Many of these datasets have the potential to provide solutions to important problems, and advice in making decisions in health, science, sociology, engineering, business, information technology, and government. However, the size, complexity, quality and diversity of these datasets often make them difficult to process and analyse using standard statistical methods, software or equipment. This course is one of four in the Big Data Analytics program on FutureLearn from the ARC Centre of Excellence for Mathematical and Statistical Frontiers at Queensland University of Technology (QUT). The program enables you to understand how big data is collected and managed, before exploring statistical inference, machine learning, mathematical modelling and data visualisation. #FLbigdataD2D At FutureLearn, we want to inspire learning for life. We offer a diverse selection of free, high quality online courses from some of the world's leading universities and other outstanding cultural institutions. Browse all courses and sign up here: http://www.futurelearn.com
Views: 1942 FutureLearn
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Views: 44817 LearnEveryone
Natural Language Processing (NLP) Tutorial | Data Science Tutorial | Simplilearn
Natural language processing (NLP) is a field of computer science, artificial intelligence and computational linguistics concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora. Python for Data Science Certification Training Course: https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training?utm_campaign=Data-Science-NLP-6WpnxmmkYys&utm_medium=SC&utm_source=youtube The Data Science with Python course is designed to impart an in-depth knowledge of the various libraries and packages required to perform data analysis, data visualization, web scraping, machine learning, and natural language processing using Python. The course is packed with real-life projects, assignment, demos, and case studies to give a hands-on and practical experience to the participants. Mastering Python and using its packages: The course covers PROC SQL, SAS Macros, and various statistical procedures like PROC UNIVARIATE, PROC MEANS, PROC FREQ, and PROC CORP. You will learn how to use SAS for data exploration and data optimization. Mastering advanced analytics techniques: The course also covers advanced analytics techniques like clustering, decision tree, and regression. The course covers time series, it's modeling, and implementation using SAS. As a part of the course, you are provided with 4 real-life industry projects on customer segmentation, macro calls, attrition analysis, and retail analysis. Who should take this course? There is a booming demand for skilled data scientists across all industries that make this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: 1. Analytics professionals who want to work with Python 2. Software professionals looking for a career switch in the field of analytics 3. IT professionals interested in pursuing a career in analytics 4. Graduates looking to build a career in Analytics and Data Science 5. Experienced professionals who would like to harness data science in their fields 6. Anyone with a genuine interest in the field of Data Science For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 25805 Simplilearn
World of Warships Sound Effects / Files (Version 2016-02-03) Download (Data Mining)
New Version for update https://www.youtube.com/playlist?list=PLGGvy-amUMOYUwts1i4OgQ7lLW1ktSLj7 Find a full list of all sounds under: gamesound.eu Download (Files in .oog): https://www.dropbox.com/sh/17xs8vf0sc2dogy/AADqE00Jedmrjgs-LdEQI7eca?dl=0 19:04 Standing by for Instructions, over 19:06 Returning 19:07 Scout destroyed 19:08 destroyed 19:09 Returning 19:11 Group 5 19:11 Airborne 19:13 Target Destroyed 19:15 Scout returning to ship 19:16 Target Destroyed 19:18 We are under attack 19:20 Target Destroyed 19:21 Awaiting Instructions 19:22 Group 1 19:23 Target Destroyed 19:25 Ready 19:26 Group 6 19:27 Approaching Target 19:29 Scout returning 19:30 Group 4 19:31 Group 2 19:32 Destroyed 19:34 Maintaining present course 19:36 Attacking Target 19:38 Group 5 19:39 Group 8 19:41 Group 5 19:41 Taking fire 19:42 Approaching Target 19:44 Engaging Enemy 19:45 Group 8 19:45 Airborne 19:46 Group 7 19:46 Standing by for Instructions, Over 19:49 Engaging Enemy 19:50 Awaiting Instructions 19:51 Approaching Target 19:53 Group 2 19:54 Ready for takeoff 19:55 Engaging Enemy 19:56 Returning to Ship 19:57 Maintaining present course 19:59 Group 1 20:00 Ready 20:01 Group 3 20:02 Group 8 20:03 Scout Airborne 20:05 Group 9 20:06 Destroyed 20:06 Returning to Ship 20:10 Ready for takeoff 20:14 Group 6 20:14 Group 4 20:16 We are under attack 20:19 Fighter returning to ship 20:22 Group 3 20:23 Group 2 20:24 Group 7 20:44 Fire Gun Sound 20:58 Plane Engine Starting 21:13 Jet fighter Engine 21:18 Old Plane Engine 21:26 Ricochets 21:30 Jet fighter starting 22:13 Bomb hit 23:12 Underwater - In-ship Stress sound (sound of metal under stress) 23:39 Wave sound 23:40 Engine Sound (fast) 23:39 Engine Sound (more hp) 23:50 Bigger Engine 24:30 Metal under Stress sound 33:32 All Forces provide cover for that Target 33:35 Roger 33:37 Attention, I need help 33:40 All stations, reporting the position of a strategic target 33:43 dash it all 33:44 All stations, concentrate fire on that target 33:48 Good luck everyone 33:49 Nice work! 33:50 I am any thanks 33:52 Critical Engine Damage 33:53 Good show 33:56 All stations defend the base 33:57 Capture that area 34:00 Reporting the position of a priority target 34:02 All stations, concentrate fire on the target 34:05 We sunk an allied destroyer 34:07 Oh, Neptune's beard 34:09 Enemy Destroyer sunk 34:12 Attention, all forces, defend the base 34:15 All forces, requesting assistance 34:17 All stations, requesting fire on the designated target 34:19 Torpedoes, direct front 34:21 Main turret blown up 34:23 Wilco 33:24 That's how it's done 33:26 Objective Capture sound effect 33:28 Fire burning sound effect 34:47 Our Team is taking the lead 34:51 All stations capture the base 34:54 unable to maneuver 34:55 Roger, proceeding to assist you 34:58 Support the allied Target 34:59 We destroyed an enemy battleship 35:01 Hull breach! We are taking in a lot of water quickly 35:04 All stations protect that target 35:06 Torpedoes to starboard 35:07 Dont fire on your allies 35:10 Attention, support that Target 35:13 I owe you one 35:14 Negative 35:15 Our Victory is in sight! 35:20 We destroyed an enemy cruiser 35:21 Attention, defending the base is our primary objective 35:24 Enemy severely damaged 35:27 Enemy aircraft carrier blown up! 35:28 A Discipline Penalty was imposed on you for friendly fire 35:32 Main turret critically damaged 35:34 Its a go! 35:35 Attention, reporting the target position 35:38 Our Team depends up on you 35:58 The enemy is about to win 36:03 Enemy battleship foundered 36:05 Enemy Cruiser destroyed 36:06 All forces, proceed to the base 36:08 Enemy heavily damaged 36:10 We destroyed an allied battleship 36:12 Set a course for that area 36:14 Way point reached 36:16 Stone the crows 36:17 Torpedoes to Stern 36:19 Torpedoes! A Stern 36:20 Lets give them a hard time 36:22 Affirmative 36:23 cant click action waypoint 36:25 Autopilot mode enabled 36:26 Received, I am on it 36:28 We sunk an allied aircraft carrier 36:30 Son of a gun! 36:32 You are on your own! 36:34 Confirmed Penetration 36:36 Concentrate fire on the designate target 36:38 Hang in there, help is on the way 36:40 Hold the base by any means necessary 36:43 Torpedoes to port 36:35 Set a course for the base! 36:46 We destroyed an allied cruiser 36:48 Proceed to that area 36:50 fire burning sound 37:07 Concentrate fire on the designated target 37:11 All stations proceed to capture that area 37:14 Attention, concentrate all efforts on protecting the base 37:18 Rotten lock 37:19 Concentrate fire on the enemy warship 40:18 Ear Ring Damge 40:38 Citadel hit - kill 41:05 Engine Boost 41:08 Switching to full speed 41:11 Critical Base capture progress 41:20 Adjusting Speed 41:42 Penetration 41:48 Whistle 42:00 Horn blowing 42:02 Dramatic Music 42:15 Adjusting Speed
Views: 15296 Yoshi_E
4. What is Integration (Hindi) |
What is Integration and need for it.
Views: 377136 Lighthouse
Audio Mining Trip | Minecraft solo survival #6
Sorry guys just like to apologize again for this I just didn't want to make another episiode
Views: 9 Glitchmaster360
Working with Time Series Data in MATLAB
See what's new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1 Download a trial: https://goo.gl/PSa78r A key challenge with the growing volume of measured data in the energy sector is the preparation of the data for analysis. This challenge comes from data being stored in multiple locations, in multiple formats, and with multiple sampling rates. This presentation considers the collection of time-series data sets from multiple sources including Excel files, SQL databases, and data historians. Techniques for preprocessing the data sets are shown, including synchronizing the data sets to a common time reference, assessing data quality, and dealing with bad data. We then show how subsets of the data can be extracted to simplify further analysis. About the Presenter: Abhaya is an Application Engineer at MathWorks Australia where he applies methods from the fields of mathematical and physical modelling, optimisation, signal processing, statistics and data analysis across a range of industries. Abhaya holds a Ph.D. and a B.E. (Software Engineering) both from the University of Sydney, Australia. In his research he focused on array signal processing for audio and acoustics and he designed, developed and built a dual concentric spherical microphone array for broadband sound field recording and beam forming.
Views: 50866 MATLAB
20 Predictive Analytics Training with Weka (Nearest neighbor)
Data Mining and Predictive Analytics training course using the open source Weka tool. Videos producted by the University of Waikato, New Zealand. Posted by Rapid Progress Marketing and Modeling, LLC (RPM2) under CC BY 3.0 RPM2 is a full-service Predictive Analytics and Data Sciences Services company specializing in Model Development, Consulting, Direct Marketing Services, and Professional Training. Visit us at http://www.RPMSquared.com/
Views: 3663 Predictive Analytics
Data Mining Lecture 13 Part 1
Frequent Itemsets : Apriori Algorithm
Views: 1803 Utah Data
O'Reilly Webcast: Python for Data Analysis
*Note: Audio quality a little poor. Finding great data analysts is difficult. Despite the explosive growth of data in industries ranging from manufacturing and retail to high technology, finance, and healthcare, learning and accessing data analysis tools has remained a challenge. This webcast will highlight one of the most important tools in the field—Python. In this hands-on webcast presented by Wes McKinney, author of "Python for Data Analysis", he will showcase a number of examples and you will receive an introduction to some of the most important tools in the Python language for: data preparation data analysis data visualization Learn about the growing field of data analysis from an expert in the community. About Wes McKinney Wes McKinney is CTO and Cofounder of Lambda Foundry, Inc. From 2010 to 2012, he served as a Python consultant to hedge funds and banks while developing pandas, a widely used Python data analysis library. From 2007 to 2010, he researched global macro and credit trading strategies at AQR Capital Management. He graduated from MIT with an S.B. in Mathematics. He is on leave from the Duke University Ph.D program in Statistics. Produced by: Yasmina Grecp
Views: 5464 O'Reilly
Phantasma Chain Live with Lee Kai Lee - Do you have SOUL?
Support the stream: https://streamlabs.com/cryptocrow #Phantasma #SOUL #LeeKaiLee Crypto Crow Audio Podcast: https://www.spreaker.com/show/the-crypto-crow-radio-show https://crowsnestex.com ACUITAS Trading Bot: LIVE! https://www.acuitas.app/ref/cryptocrow/ NOTICE: All Paid Reviews And Features on my channel were paid for by the crypto companies in the form of Bitcoin ranging from .2 to 1 BTC or in some cases equal or double value in the project's tokens. Paid reviews do not mean a positive review so as to maintain the integrity of the Crypto Crow Channel. My New eBook - Amazon https://goo.gl/kSzvUL CryptoCrow Telegram: https://t.me/cryptocrowgroup Twitter: https://twitter.com/jasonappleton How To Build a 6 GPU Mining Rig and Mine AION https://chainwise.us/how-to-build-a-6-gpu-mining-rig/ ACUITAS Trading Bot: Presale https://www.acuitas.app/ref/cryptocrow/ HushMail - https://goo.gl/hdseJb My NEW Udemy Course Is now LIVE!: $12.99 : https://bit.ly/2KrxpQx LEARN ABOUT CRYPTO IN MY UDEMY COURSE: $10 https://tinyurl.com/CCudemyBeg HoneyMiner - Mine BTC Easily https://honeyminer.com/referred/59p8j Crypto Firewall: https://www.cryptowall.ca/ - Save 10% with code: CRYPTOCROW10 Get $10 in Free Bitcoin joining Coinbase: https://tinyurl.com/CrowCbase Support The Crypto Crow https://www.patreon.com/cryptocrow http://JasonAppleton.com - Crows Website ########USEFUL LINKS###### CEX Exchange: https://tinyurl.com/CrowCEX The Best Crypto Trading Tool https://tinyurl.com/CCcoinigy Setup Your Binance Exchange Account: https://tinyurl.com/CCbinance Coin Tracker to Help Track It All For Taxes: https://tinyurl.com/CrowTracking ###### SOCIAL MEDIA ####### I AM NOT A FINANCIAL ADVISER - NOTHING WITHIN THIS VIDEO OR ON MY SITES SHOULD BE CONSIDERED FINANCIAL ADVICE AS ITS ALL A MATTER OF PERSONAL OPINION ONLY. WHAT WORKS FOR ME TODAY MAY NOT WORK FOR YOU TOMORROW. Twitter: https://twitter.com/jasonappleton Crypto Crow Merchandise: https://tinyurl.com/CrowMerch CryptoCrow Telegram: https://t.me/cryptocrowgroup CryptoCrow Facebook: https://www.facebook.com/cryptocrow/ ###COINS I LOVE##### TRON @Tronfoundation https://tron.network/ ADA @CardanoStiftung https://www.cardano.org/ Qtum @QtumOfficial https://qtum.org/ Stellar @StellarOrg https://www.stellar.org/ Power Ledger @PowerLedger_io https://powerledger.io/ OMG @omise_go https://omg.omise.co/ Mainframe @Mainframe_HQ https://mainframe.com/ DENT @dentcoin https://www.dentcoin.com/ Matrix AI @MATRIXAINetwork https://www.matrix.io/ NEO @NEO_Blockchain https://neo.org/ DragonChain @dragonchaingang https://dragonchain.com/ UTRUST @UTRUST_Official https://utrust.com/ Ontology @OntologyNetwork https://ont.io/ QuarkChain @Quark_Chain https://quarkchain.io/ Phantasma @phantasmachain https://phantasma.io/ Deep Brain Chain @DeepBrainChain https://www.deepbrainchain.org/ AION @Aion_Network https://aion.network/ GoChain @go_chain https://gochain.io/ OPEN @OpenPlatformICO https://www.openfuture.io/ IvyKoin @ivykoin https://www.ivykoin.com/ Celsius @CelsiusNetwork https://celsius.network/ Debitum @DebitumNetwork https://debitum.network/ Neurotoken @neuromation_io https://neuromation.io/ EdenChain @edenchainio https://edenchain.io/ Reward Token @Rewardsdotcom https://www.rewardstoken.io/ Monarch Token @Monarchtoken https://monarchtoken.io/ Blockchain Terminal https://www.bct.io/ Ripple @Ripple https://ripple.com/xrp/ NOTICE: All Paid Reviews And Features on my channel were paid for by the crypto companies in the form of Bitcoin ranging from .2 to 1 BTC or in some cases equal or double value in the project's tokens.
Views: 732 Crypto Crow