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
Views: 537018 Computer Education For all
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: 268 vaishu raj
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.
This video reviews the scales of measurement covered in introductory statistics: nominal, ordinal, interval, and ratio (Part 1 of 2). Scales of Measurement Nominal, Ordinal, Interval, Ratio YouTube Channel: https://www.youtube.com/user/statisticsinstructor Subscribe today! Lifetime access to SPSS videos: http://tinyurl.com/m2532td Video Transcript: In this video we'll take a look at what are known as the scales of measurement. OK first of all measurement can be defined as the process of applying numbers to objects according to a set of rules. So when we measure something we apply numbers or we give numbers to something and this something is just generically an object or objects so we're assigning numbers to some thing or things and when we do that we follow some sort of rules. Now in terms of introductory statistics textbooks there are four scales of measurement nominal, ordinal, interval, and ratio. We'll take a look at each of these in turn and take a look at some examples as well, as the examples really help to differentiate between these four scales. First we'll take a look at nominal. Now in a nominal scale of measurement we assign numbers to objects where the different numbers indicate different objects. The numbers have no real meaning other than differentiating between objects. So as an example a very common variable in statistical analyses is gender where in this example all males get a 1 and all females get a 2. Now the reason why this is nominal is because we could have just as easily assigned females a 1 and males a 2 or we could have assigned females 500 and males 650. It doesn't matter what number we come up with as long as all males get the same number, 1 in this example, and all females get the same number, 2. It doesn't mean that because females have a higher number that they're better than males or males are worse than females or vice versa or anything like that. All it does is it differentiates between our two groups. And that's a classic nominal example. Another one is baseball uniform numbers. Now the number that a player has on their uniform in baseball it provides no insight into the player's position or anything like that it just simply differentiates between players. So if someone has the number 23 on their back and someone has the number 25 it doesn't mean that the person who has 25 is better, has a higher average, hits more home runs, or anything like that it just means they're not the same playeras number 23. So in this example its nominal once again because the number just simply differentiates between objects. Now just as a side note in all sports it's not the same like in football for example different sequences of numbers typically go towards different positions. Like linebackers will have numbers that are different than quarterbacks and so forth but that's not the case in baseball. So in baseball whatever the number is it provides typically no insight into what position he plays. OK next we have ordinal and for ordinal we assign numbers to objects just like nominal but here the numbers also have meaningful order. So for example the place someone finishes in a race first, second, third, and so on. If we know the place that they finished we know how they did relative to others. So for example the first place person did better than second, second did better than third, and so on of course right that's obvious but that number that they're assigned one, two, or three indicates how they finished in a race so it indicates order and same thing with the place finished in an election first, second, third, fourth we know exactly how they did in relation to the others the person who finished in third place did better than someone who finished in fifth let's say if there are that many people, first did better than third and so on. So the number for ordinal once again indicates placement or order so we can rank people with ordinal data. OK next we have interval. In interval numbers have order just like ordinal so you can see here how these scales of measurement build on one another but in addition to ordinal, interval also has equal intervals between adjacent categories and I'll show you what I mean here with an example. So if we take temperature in degrees Fahrenheit the difference between 78 degrees and 79 degrees or that one degree difference is the same as the difference between 45 degrees and 46 degrees. One degree difference once again. So anywhere along that scale up and down the Fahrenheit scale that one degree difference means the same thing all up and down that scale. OK so if we take eight degrees versus nine degrees the difference there is one degree once again. That's a classic interval scale right there with those differences are meaningful and we'll contrast this with ordinal in just a few moments but finally before we do let's take a look at ratio.
Views: 318211 Quantitative Specialists
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: 297737 Bart Baesens
http://expandknowledge.net/csc106/lecture3.pdf Audio, Images, Video
Views: 3044 Amos Johnson
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.
Views: 21070 IT Miner - Tutorials,GK & Facts
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: 359745 APMonitor.com
Get The Complete MATLAB Course Bundle for 1 on 1 help! https://josephdelgadillo.com/product/matlab-course-bundle/ Get the courses directly on Udemy! Go From Beginner to Pro with MATLAB! http://bit.ly/2v1e0lL Machine Learn Fundamentals with MATLAB! http://bit.ly/2v3sQs6 The Ultimate Guide for MATLAB App Development! http://bit.ly/2GOodDN MATLAB for Programming and Data Analysis! http://bit.ly/2IIwpWL Enroll in the FREE Teachable course! http://jtdigital.teachable.com/p/matlab Time Stamps 00:51 What is Matlab, how to download Matlab, and where to find help 07:52 Introduction to the Matlab basic syntax, command window, and working directory 18:35 Basic matrix arithmetic in Matlab including an overview of different operators 27:30 Learn the built in functions and constants and how to write your own functions 42:20 Solving linear equations using Matlab 53:33 For loops, while loops, and if statements 1:09:15 Exploring different types of data 1:20:27 Plotting data using the Fibonacci Sequence 1:30:45 Plots useful for data analysis 1:38:49 How to load and save data 1:46:46 Subplots, 3D plots, and labeling plots 1:55:35 Sound is a wave of air particles 2:05:33 Reversing a signal 2:12:57 The Fourier transform lets you view the frequency components of a signal 2:27:25 Fourier transform of a sine wave 2:35:14 Applying a low-pass filter to an audio stream 2:43:50 To store images in a computer you must sample the resolution 2:50:13 Basic image manipulation including how to flip images 2:57:29 Convolution allows you to blur an image 3:02:51 A Gaussian filter allows you reduce image noise and detail 3:08:55 Blur and edge detection using the Gaussian filter 3:16:39 Introduction to Matlab & probability 3:19:47 Measuring probability 3:26:53 Generating random values 3:35:40 Birthday paradox 3:43:25 Continuous variables 3:48:00 Mean and variance 3:55:24 Gaussian (normal) distribution 4:03:21 Test for normality 4:10:32 2 sample tests 4:16:28 Multivariate Gaussian
Views: 1010033 Joseph Delgadillo
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: 141869 SciShow
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: 420165 sentdex
CAREERS IN DATA ANALYTICS - Salary , Job Positions , Top Recruiters What IS DATA ANALYTICS? Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses. As a term, data analytics predominantly refers to an assortment of applications, from basic business intelligence (BI), reporting and online analytical processing (OLAP) to various forms of advanced analytics. In that sense, it's similar in nature to business analytics, another umbrella term for approaches to analyzing data -- with the difference that the latter is oriented to business uses, while data analytics has a broader focus. The expansive view of the term isn't universal, though: In some cases, people use data analytics specifically to mean advanced analytics, treating BI as a separate category. Data analytics initiatives can help businesses increase revenues, improve operational efficiency, optimize marketing campaigns and customer service efforts, respond more quickly to emerging market trends and gain a competitive edge over rivals -- all with the ultimate goal of boosting business performance. Depending on the particular application, the data that's analyzed can consist of either historical records or new information that has been processed for real-time analytics uses. In addition, it can come from a mix of internal systems and external data sources. Types of data analytics applications : At a high level, data analytics methodologies include exploratory data analysis (EDA), which aims to find patterns and relationships in data, and confirmatory data analysis (CDA), which applies statistical techniques to determine whether hypotheses about a data set are true or false. EDA is often compared to detective work, while CDA is akin to the work of a judge or jury during a court trial -- a distinction first drawn by statistician John W. Tukey in his 1977 book Exploratory Data Analysis. Data analytics can also be separated into quantitative data analysis and qualitative data analysis. The former involves analysis of numerical data with quantifiable variables that can be compared or measured statistically. The qualitative approach is more interpretive -- it focuses on understanding the content of non-numerical data like text, images, audio and video, including common phrases, themes and points of view. At the application level, BI and reporting provides business executives and other corporate workers with actionable information about key performance indicators, business operations, customers and more. In the past, data queries and reports typically were created for end users by BI developers working in IT or for a centralized BI team; now, organizations increasingly use self-service BI tools that let execs, business analysts and operational workers run their own ad hoc queries and build reports themselves. Keywords: being a data analyst, big data analyst, business analyst data warehouse, data analyst, data analyst accenture, data analyst accenture philippines, data analyst and data scientist, data analyst aptitude questions, data analyst at cognizant, data analyst at google, data analyst at&t, data analyst australia, data analyst basics, data analyst behavioral interview questions, data analyst business, data analyst career, data analyst career path, data analyst career progression, data analyst case study interview, data analyst certification, data analyst course, data analyst in hindi, data analyst in india, data analyst interview, data analyst interview questions, data analyst job, data analyst resume, data analyst roles and responsibilities, data analyst salary, data analyst skills, data analyst training, data analyst tutorial, data analyst vs business analyst, data mapping business analyst, global data analyst bloomberg, market data analyst bloomberg
Views: 25552 THE MIND HEALING
The University of Warwick is a founding partner of The Alan Turing Institute. In late 2017, Turing team members recently visited the University of Warwick to deliver a Town Hall meeting and meet new Fellows. During the visit Alex Buxton (Research Communications Manager, University of Warwick) spoke to Turing CEO Sir Alan Wilson about the future of data science in the UK. About Sir Alan Wilson Sir Alan Wilson FBA FAcSS FRS is CEO of The Alan Turing Institute and Professor of Urban and Regional Systems in the Centre for Advanced Spatial Analysis at University College London. Cities.
Views: 144 The Alan Turing Institute
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: 163301 Simplilearn
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: 591 ieeeComputerSociety
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: 130489 CBC News
Guide : Lec 3 | C.S. - Data Representation, Data Storage, Data Encoding ( Part 7 ) Free seo tools on bulkping for Site Search engine optimisation Movie computer, science, data, representation, storage, encoding, techniques, methods, internal, computers, definition, graphical, graphic, tabular, system, statistical, network, dimensionality, reduction, in, multiple, feature, architecture, representations, graphs, visual, classification, machine, learning, mining, text, audio, images, vector, raster, generalization, main, memory, circuitry, intelligent, pattern, recognition, neural, algorithms, inference, clustering, indexing, software, prediction, evaluation, storing C.S. Lecture 3: Data Representation, Data Storage, Data Encoding -- Contents -- 1. Numeric Data Representation. 2. Real Number Representation. 3. Main Memory. 4. Mass Storage and Magnetic Systems. 5. File Storage and Retrieval. 6. Analog and Digital Information. 7. Representing Text, Audio, Images, Graphics, and Video. 8. Data Compression. 9. Communication Errors. 10. Error-Correcting Codes. View Part 8: BulkPing Website: BulkPing/ Computer Science Forum: BulkPing
Views: 63 wetexclusion724y
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: 3830 SlideTalk
Download dataset from this link: https://drive.google.com/open?id=1yRTuRPLNpLQRI1zEcq9Gx3N6WTcBCqMP What is Machine Learning? Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. You should check this video tutorial to easily download Anaconda Navigator for Python Distribution. https://youtu.be/4v7Uke37QGs First of all, you have to download Anaconda Navigator Distribution for Python. For this go to this link and download for your computer depending on your operating system, Windows, Linux or Mac. https://www.anaconda.com/download/ We have used Python 3.6 Version for our course. So you should download that to cope up with us. The next video: https://www.youtube.com/watch?v=ohampM4H6fY&index=4&list=PLA-CsqNypl-SqtkfwXAK7trT_M2g5yAGe Data Proessing Complete Playlist: https://www.youtube.com/playlist?list=PLA-CsqNypl-SqtkfwXAK7trT_M2g5yAGe The previous video:https://www.youtube.com/watch?v=RaC85Y2kS5Q&list=PLA-CsqNypl-SqtkfwXAK7trT_M2g5yAGe&index=2 1/How can we Master Machine Learning on Python? 2/How can we Have a great intuition of many Machine Learning models? 3/How can we Make accurate predictions? 4/How can we Make powerful analysis? 5/How can we Make robust Machine Learning models? 6/How can we Create strong added value to your business? 7/How do we Use Machine Learning for personal purpose? 8/How can we Handle specific topics like Reinforcement Learning, NLP and Deep Learning? 9/How can we Handle advanced techniques like Dimensionality Reduction? 10/How do we Know which Machine Learning model to choose for each type of problem? 11/How can we Build an army of powerful Machine Learning models and know how to combine them to solve any problem? Subscribe to our channel to get video updates. সাবস্ক্রাইব করুন আমাদের চ্যানেলেঃ https://www.youtube.com/channel/UC50C-xy9PPctJezJcGO8q2g Follow us on Facebook: https://www.facebook.com/Planeter.Bangladesh/ Follow us on Instagram: https://www.instagram.com/planeter.bangladesh Follow us on Twitter: https://www.twitter.com/planeterbd Our Website: https://www.planeterbd.com For More Queries: [email protected] Phone Number: +8801727659044, +8801728697998 #machinelearning #bigdata #ML #DataScience #DataSet #XY #DeepLearning #robotics #রবোটিক্স #প্ল্যনেটার #Planeter #ieeeprotocols #DataProcessing #MissingData #SimpleLinearRegression #MultiplelinearRegression #PolynomialRegression #SupportVectorRegression(SVR) #DecisionTreeRegression #RandomForestRegression #EvaluationRegressionModelsPerformance #MachineLearningClassificatioModels #LogisticRegression #machinelearnigcourse #machinelearningcoursebangla #machinelearningforbeginners #banglamachinelearning #artificialintelligence #machinelearningtutorials #machinelearningcrashcourse #imageprocessing #SpyderIDE #BestBanglaMachineLearningTutorialSeries #ML #MachineLearning
Views: 553 Planeter
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: 23517 DataCamp
Professor Perry Samson, Atm, Oceanic & Space Sci. - CoE, University of Michigan The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
Views: 281 Michigan Engineering
Views: 223 Lilia Vernigora
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: 27705 Simplilearn
The original live broadcast lost audio momentarily. That audio is now available in this version. This chat will focus on the most practical tools to get you started with data science and healthcare.ai. Which tools are best for data analysis. Which tools are best for machine learning. Which tools enable team collaboration. Which tools let you work on open-source code.
Views: 4227 Healthcare AI
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: 1928 FutureLearn
The original set of videos were quiet so I made them 40x louder. I take no credit for the content. -- This is Lecture 18 of the CSE373 (Analysis of Algorithms) course taught by Professor Steven Skiena [http://www.cs.sunysb.edu/~skiena/] at Stony Brook University in 2012.
Views: 72 drew lee
Learn more with a course in Pure Data for games at: http://School.VideoGameAudio.com Advanced Topics in Video Game Audio: Leonard J. Paul Date: March 5, 2015 - 12:30pm - 1:30pm Location: CCRMA Classroom Event Type: Guest Lecture Composer, Sound Designer and Educator Leonard J. Paul visits CCRMA to present his work with procedural audio, generative music systems and Pure Data in the context of developing innovative audio and music systems for gaming. https://ccrma.stanford.edu/events/advanced-topics-in-video-game-audio-leonard-j-paul Powerpoint currently available by request by form or email from the School of Video Game Audio: http://School.VideoGameAudio.com
Views: 3325 School of Video Game Audio
2013 - Mining Representative Movement Patterns through Compression NhatHai Phan, Dino Ienco, Pascal Poncelet, and Maguelonne Teisseire. The 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013), Goal Coast, Australia, April 2013. (acceptance rate: 11.3%) 2012 - Mining Time Relaxed Gradual Moving Object Clusters NhatHai Phan, Dino Ienco, Pascal Poncelet, and Maguelonne Teisseire. In Proceedings of the 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS 2012), Redondo Beach, California, November 2012. [pdf] [demo] [code] (acceptance rate: 22%) 2012 - GeT_Move: An Efficient and Unifying Spatio-Temporal Pattern Mining Algorithm for Moving Objects NhatHai Phan, Pascal Poncelet, and Maguelonne Teisseire. In Proceedings of the 11th International Symposium on Intelligent Data Analysis (IDA 2012), Helsinki, Finland, October 2012. 2012 - Extracting Trajectories through an Efficient and Unifying Spatio-Temporal Pattern Mining System NhatHai Phan, Dino Ienco, Pascal Poncelet, and Maguelonne Teisseire. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2012), Demo Paper, Bristol, UK, September 2012.
Views: 499 nhathai phan
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: 441579 Brandon Weinberg
Tags are becoming one of the most common ways of characterizing music (as well as other types of multimedia data). In this lecture I will describe how tag information can be collected and used to build interesting music retrieval systems. I will also discuss how audio feature extraction and machine learning can be combined to build systems that assign tags automatically.
Lecture Series on Data Structures and Algorithms by Dr. Naveen Garg, Department of Computer Science & Engineering ,IIT Delhi.
Views: 1621787 nptelhrd
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: 45542 MATLAB
Google Tech Talks April 23, 2007 ABSTRACT Google engEDU Speaker: Douglas Eck
Views: 615 GoogleTalksArchive
PLEASE NOTE: Due to technical difficulties, audio and visuals of the speaker for this talk are missing until 18:24. Please follow this link to view the video from this timecode: https://youtu.be/6ucNiWiyzyg?t=1104 Plenary address from Cambridge Office of Scholarly Communication's Text & Data Mining Symposium, head at the Engineering Department of Cambridge University on Wednesday 12 July 2017. You can find all speaker presentations on the Apollo repository here: https://www.repository.cam.ac.uk/handle/1810/266221
Andrew Ng of Stanford University, Technion lecture: Machine Learning via Large-scale Brain Simulations Machine learning is a very successful technology, but applying it to a new problem usually means spending a long time hand-designing the input features to feed to the learning algorithm. This is true for applications in vision, audio, and text/NLP. To address this, researchers in machine learning have recently developed "deep learning" algorithms, which can automatically learn feature representations from unlabeled data, thus bypassing most of this time-consuming engineering. These algorithms are based on building massive artificial neural networks, that were loosely inspired by cortical (brain) computations. In this talk, I describe the key ideas behind deep learning, and also discuss the computational challenges of getting these algorithms to work. I'll also present a few case studies, and report on the results from a project that I led at Google to build massive deep learning algorithms, resulting in a highly distributed neural network trained on 16,000 CPU cores, and that learned by itself to discover high level concepts such as common objects in video.
Views: 15094 Technion
This video features the audio of a conversation on 2 Nov. 2014 between Chantal Lutz and Brian Tomasik about artificial general intelligence (AGI) and prospects for international cooperation. For more on this topic, see "Thoughts on Robots, AI, and Intelligence Explosion": http://foundational-research.org/robots-ai-intelligence-explosion/ The audio was originally recorded for Chantal's personal use, so the quality is pretty bad. I thought I would upload it for those who don't mind the audio's echo, but if you do find the echo bothersome, feel free to ignore this video. I also removed a lot of "umm"s and long pauses. Photo credit: "The IBM Blue Gene/P supercomputer installation at the Argonne Leadership Angela Yang Computing Facility located in the Argonne National Laboratory, in Lemont, Illinois, USA." By Argonne National Laboratory's Flickr page [CC-BY-SA-2.0 (http://creativecommons.org/licenses/by-sa/2.0)], via Wikimedia Commons: https://commons.wikimedia.org/wiki/File%3AIBM_Blue_Gene_P_supercomputer.jpg Brian Tomasik is a co-founder of and researcher at the Foundational Research Institute (http://foundational-research.org/), where he explores the best ways to reduce suffering in humanity's future. Previously he was a software engineer at Microsoft's Bing search engine, working on machine learning and data mining.
Views: 584 Brian Tomasik
New Version for update 0.5.3.0: 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: 14387 Yoshi_E
In conjunction with our first seminar, this presentation will pick up where the last one left off. In this seminar we’ll take that deep dive and look at hardware optimizations, network streaming based audio vs locally connected (USB,HDMI, SPDIF) audio, add-on applications such as Pure Music, Bit Perfect, Amarra, etc … as well as take a look at and examine more advanced applications, such as J River. In this seminar we’ll go beyond the basics and look at what it takes to get world-class, state-of-the-art playback from your computer audio system. Steve Silberman, Audioquest Steve Silberman got his first start in consumer electronics over 20 years ago when he took a sales position at his local high-end audio retailer. However, he can trace his love for music and passion for electronics back to his early childhood, when his parents gave him a pair of KHL speakers, a Dual turntable, and a Pioneer integrated amplifier.In addition to working on the retail side of the industry, Steve held the position of national sales and marketing manager for Ayre Acoustics, a leading-edge consumer electronics design and manufacturing company. Currently, Steve works for AudioQuest as VP of Development where he spends a great deal of his time focused on computer audio based technology and DragonFly — AudioQuest’s new family of digital to analog converters. Producer: Douglas Mechaber, VP LA/OC Audio Society Doug Mechaber writes for Tom’s IT Pro, is a lifetime member of the LA/OC Audiophile Society, an IT industry veteran, plays violin, and is a long time audiophile.
Views: 1002 The Home Entertainment Show
Guide : Lec 3 | C.S. - Data Representation, Data Storage, Data Encoding ( Part 9 ) Online seo tools on bulkping for Site Seo Video computer, science, data, representation, storage, encoding, techniques, methods, internal, computers, definition, graphical, graphic, tabular, system, statistical, network, dimensionality, reduction, in, multiple, feature, architecture, representations, graphs, visual, classification, machine, learning, mining, text, audio, images, vector, raster, generalization, main, memory, circuitry, intelligent, pattern, recognition, neural, algorithms, inference, clustering, indexing, software, prediction, evaluation, storing C.S. Lecture 3: Data Representation, Data Storage, Data Encoding -- Contents -- 1. Numeric Data Representation. 2. Real Number Representation. 3. Main Memory. 4. Mass Storage and Magnetic Systems. 5. File Storage and Retrieval. 6. Analog and Digital Information. 7. Representing Text, Audio, Images, Graphics, and Video. 8. Data Compression. 9. Communication Errors. 10. Error-Correcting Codes. View Part 10: BulkPing Website: BulkPing/ Computer Science Forum: BulkPing
Views: 377 miscreantcharla6Vpd
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng provides an overview of the course in this introductory meeting. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 CS 229 Course Website: http://www.stanford.edu/class/cs229/ Stanford University: http://www.stanford.edu/ Stanford University Channel on YouTube: http://www.youtube.com/stanford
Views: 2101484 Stanford