Search results “Data mining with big data pdf viewer”
Extract Structured Data from unstructured Text (Text Mining Using R)
A very basic example: convert unstructured data from text files to structured analyzable format.
Views: 11421 Stat Pharm
Introduction to Data Science with R - Data Analysis Part 1
Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 908039 David Langer
The Best Way to Prepare a Dataset Easily
In this video, I go over the 3 steps you need to prepare a dataset to be fed into a machine learning model. (selecting the data, processing it, and transforming it). The example I use is preparing a dataset of brain scans to classify whether or not someone is meditating. The challenge for this video is here: https://github.com/llSourcell/prepare_dataset_challenge Carl's winning code: https://github.com/av80r/coaster_racer_coding_challenge Rohan's runner-up code: https://github.com/rhnvrm/universe-coaster-racer-challenge Come join other Wizards in our Slack channel: http://wizards.herokuapp.com/ Dataset sources I talked about: https://github.com/caesar0301/awesome-public-datasets https://www.kaggle.com/datasets http://reddit.com/r/datasets More learning resources: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-prepare-data http://machinelearningmastery.com/how-to-prepare-data-for-machine-learning/ https://www.youtube.com/watch?v=kSslGdST2Ms http://freecontent.manning.com/real-world-machine-learning-pre-processing-data-for-modeling/ http://docs.aws.amazon.com/machine-learning/latest/dg/step-1-download-edit-and-upload-data.html http://paginas.fe.up.pt/~ec/files_1112/week_03_Data_Preparation.pdf Please subscribe! And like. And comment. That's what keeps me going. And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 159109 Siraj Raval
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: 359745 APMonitor.com
Here's a list of 10 must read book on Data Science & Machine Learning. Foundations of DATA SCIENCE Book www.cs.cornell.edu/jeh/book.pdf Understanding Machine Learning Book www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf The Elements of Statistical Learning Book web.stanford.edu/~hastie/Papers/ESLII.pdf An Introduction to Statistical Learning Book www-bcf.usc.edu/~gareth/ISL/ISLR%20First%20Printing.pdf Mining of Massive Data Sets Book infolab.stanford.edu/~ullman/mmds/book.pdf
Views: 1608 DATA SCIENCE
Text Analytics - Ep. 25 (Deep Learning SIMPLIFIED)
Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics. Deep Learning TV on Facebook: https://www.facebook.com/DeepLearningTV/ Twitter: https://twitter.com/deeplearningtv Some of the key tools of NLP are lemmatization, named entity recognition, POS tagging, syntactic parsing, fact extraction, sentiment analysis, and machine translation. NLP tools typically model the probability that a language component (such as a word, phrase, or fact) will occur in a specific context. An example is the trigram model, which estimates the likelihood that three words will occur in a corpus. While these models can be useful, they have some limitations. Language is subjective, and the same words can convey completely different meanings. Sometimes even synonyms can differ in their precise connotation. NLP applications require manual curation, and this labor contributes to variable quality and consistency. Deep Learning can be used to overcome some of the limitations of NLP. Unlike traditional methods, Deep Learning does not use the components of natural language directly. Rather, a deep learning approach starts by intelligently mapping each language component to a vector. One particular way to vectorize a word is the “one-hot” representation. Each slot of the vector is a 0 or 1. However, one-hot vectors are extremely big. For example, the Google 1T corpus has a vocabulary with over 13 million words. One-hot vectors are often used alongside methods that support dimensionality reduction like the continuous bag of words model (CBOW). The CBOW model attempts to predict some word “w” by examining the set of words that surround it. A shallow neural net of three layers can be used for this task, with the input layer containing one-hot vectors of the surrounding words, and the output layer firing the prediction of the target word. The skip-gram model performs the reverse task by using the target to predict the surrounding words. In this case, the hidden layer will require fewer nodes since only the target node is used as input. Thus the activations of the hidden layer can be used as a substitute for the target word’s vector. Two popular tools: Word2Vec: https://code.google.com/archive/p/word2vec/ Glove: http://nlp.stanford.edu/projects/glove/ Word vectors can be used as inputs to a deep neural network in applications like syntactic parsing, machine translation, and sentiment analysis. Syntactic parsing can be performed with a recursive neural tensor network, or RNTN. An RNTN consists of a root node and two leaf nodes in a tree structure. Two words are placed into the net as input, with each leaf node receiving one word. The leaf nodes pass these to the root, which processes them and forms an intermediate parse. This process is repeated recursively until every word of the sentence has been input into the net. In practice, the recursion tends to be much more complicated since the RNTN will analyze all possible sub-parses, rather than just the next word in the sentence. As a result, the deep net would be able to analyze and score every possible syntactic parse. Recurrent nets are a powerful tool for machine translation. These nets work by reading in a sequence of inputs along with a time delay, and producing a sequence of outputs. With enough training, these nets can learn the inherent syntactic and semantic relationships of corpora spanning several human languages. As a result, they can properly map a sequence of words in one language to the proper sequence in another language. Richard Socher’s Ph.D. thesis included work on the sentiment analysis problem using an RNTN. He introduced the notion that sentiment, like syntax, is hierarchical in nature. This makes intuitive sense, since misplacing a single word can sometimes change the meaning of a sentence. Consider the following sentence, which has been adapted from his thesis: “He turned around a team otherwise known for overall bad temperament” In the above example, there are many words with negative sentiment, but the term “turned around” changes the entire sentiment of the sentence from negative to positive. A traditional sentiment analyzer would probably label the sentence as negative given the number of negative terms. However, a well-trained RNTN would be able to interpret the deep structure of the sentence and properly label it as positive. Credits Nickey Pickorita (YouTube art) - https://www.upwork.com/freelancers/~0147b8991909b20fca Isabel Descutner (Voice) - https://www.youtube.com/user/IsabelDescutner Dan Partynski (Copy Editing) - https://www.linkedin.com/in/danielpartynski Marek Scibior (Prezi creator, Illustrator) - http://brawuroweprezentacje.pl/ Jagannath Rajagopal (Creator, Producer and Director) - https://ca.linkedin.com/in/jagannathrajagopal
Views: 42675 DeepLearning.TV
Creating a R Markdown File
Shows steps for creating a R markdown file in a html, pdf or word format. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 6513 Bharatendra Rai
Convert PDF to Text in Hadoop/BigData
Process complex data type in Hadoop. Convert millions of PDF files into text file in Hadoop Ecosystem.
Views: 1217 Vijay Garg
More Data Mining with Weka (2.4: Document classification)
More Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 4: Document classification http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/QldvyV https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 7651 WekaMOOC
Tutorial #2 - Platforms for Big Data Analytics
Platforms for Big Data Analytics with Dr. Chandan Reddy, Wayne State Tutorial Information: http://dmkd.cs.wayne.edu/TUTORIAL/Bigdata/ The paper is available at: http://dmkd.cs.wayne.edu/Papers/JBD14.pdf A Survey on Platforms for Big Data Analytics, Journal of Big Data, 2014
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: 441579 Brandon Weinberg
Import Data and Analyze with Python
Python programming language allows sophisticated data analysis and visualization. This tutorial is a basic step-by-step introduction on how to import a text file (CSV), perform simple data analysis, export the results as a text file, and generate a trend. See https://youtu.be/pQv6zMlYJ0A for updated video for Python 3.
Views: 199632 APMonitor.com
Algorithms for Big Data (COMPSCI 229r), Lecture 6
CountMin sketch, point query, heavy hitters, sparse approximation. Scribe: Mien Wang. [PDF][TeX][video]
Views: 1832 Harvard University
Data Mining with Weka (1.3: Exploring datasets)
Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 3: Exploring datasets http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 77677 WekaMOOC
Advanced Data Mining with Weka (1.5: Lag creation, and overlay data)
Advanced Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 5: Lag creation, and overlay data http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/JyCK84 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 2581 WekaMOOC
Advanced Data Mining with Weka (2.3: The MOA interface)
Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 3: The MOA interface http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3530 WekaMOOC
Importing Data into R - How to import csv and text files into R
In this video you will learn how to import your flat files into R. Want to take the interactive coding exercises and earn a certificate? Join DataCamp today, and start our intermediate R tutorial for free: https://www.datacamp.com/courses/importing-data-into-r In this first chapter, we'll start with flat files. They're typically simple text files that contain table data. Have a look at states.csv, a flat file containing comma-separated values. The data lists basic information on some US states. The first line here gives the names of the different columns or fields. After that, each line is a record, and the fields are separated by a comma, hence the name comma-separated values. For example, there's the state Hawaii with the capital Honolulu and a total population of 1.42 million. What would that data look like in R? Well, actually, the structure nicely corresponds to a data frame in R, that ideally looks like this: the rows in the data frame correspond to the records and the columns of the data frame correspond to the fields. The field names are used to name the data frame columns. But how to go from the CSV file to this data frame? The mother of all these data import functions is the read.table() function. It can read in any file in table format and create a data frame from it. The number of arguments you can specify for this function is huge, so I won't go through each and every one of these arguments. Instead, let's have a look at the read.table() call that imports states.csv and try to understand what happens. The first argument of the read.table() function is the path to the file you want to import into R. If the file is in your current working directory, simply passing the filename as a character string works. If your file is located somewhere else, things get tricky. Depending on the platform you're working on, Linux, Microsoft, Mac, whatever, file paths are specified differently. To build a path to a file in a platform-independent way, you can use the file.path() function. Now for the header argument. If you set this to TRUE, you tell R that the first row of the text file contains the variable names, which is the case here. read.table() sets this argument FALSE by default, which would mean that the first row is already an observation. Next, sep is the argument that specifies how fields in a record are separated. For our csv file here, the field separator is a comma, so we use a comma inside quotes. Finally, the stringsAsFactors argument is pretty important. It's TRUE by default, which means that columns, or variables, that are strings, are imported into R as factors, the data structure to store categorical variables. In this case, the column containing the country names shouldn't be a factor, so we set stringsAsFactors to FALSE. If we actually run this call now, we indeed get a data frame with 5 observations and 4 variables, that corresponds nicely to the CSV file we started with. The read table function works fine, but it's pretty tiring to specify all these arguments every time, right? CSV files are a common and standardized type of flat files. That's why the utils package also provides the read.csv function. This function is a wrapper around the read.table() function, so read.csv() calls read.table() behind the scenes, but with different default arguments to match with the CSV format. More specifically, the default for header is TRUE and for sep is a comma, so you don't have to manually specify these anymore. This means that this read.table() call from before is thus exactly the same as this read.csv() call. Apart from CSV files, there are also other types of flat files. Take this tab-delimited file, states.txt, with the same data: To import it with read.table(), you again have to specify a bunch of arguments. This time, you should point to the .txt file instead of the .csv file, and the sep argument should be set to a tab, so backslash t. You can also use the read.delim() function, which again is a wrapper around read.table; the default arguments for header and sep are adapted, among some others. The result of both calls is again a nice translation of the flat file to a an R data frame. Now, there's one last thing I want to discuss here. Have a look at this US csv file and its european counterpart, states_eu.csv. You'll notice that the Europeans use commas for decimal points, while normally one uses the dot. This means that they can't use the comma as the field-delimiter anymore, they need a semicolon. To deal with this easily, R provides the read.csv2() function. Both the sep argument as the dec argument, to tell which character is used for decimal points, are different. Likewise, for read.delim() you have a read.delim2() alternative. Can you spot the differences again? This time, only the dec argument had to change.
Views: 42848 DataCamp
ODS database (Operation data Store ), Its properties and purpose explained with examples
Most of the developers can't differentiate between ODS,Data warehouse, Data mart,OLTP systems and Data lakes. This video explains what exactly is an ODS, how is it different from the other systems. What are its properties that make it unique and if you have an ODS or a warehouse in your organisation
Views: 3718 Tech Coach
The best stats you've ever seen | Hans Rosling
http://www.ted.com With the drama and urgency of a sportscaster, statistics guru Hans Rosling uses an amazing new presentation tool, Gapminder, to present data that debunks several myths about world development. Rosling is professor of international health at Sweden's Karolinska Institute, and founder of Gapminder, a nonprofit that brings vital global data to life. (Recorded February 2006 in Monterey, CA.) TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes. TED stands for Technology, Entertainment, Design, and TEDTalks cover these topics as well as science, business, development and the arts. Closed captions and translated subtitles in a variety of languages are now available on TED.com, at http://www.ted.com/translate. Follow us on Twitter http://www.twitter.com/tednews Checkout our Facebook page for TED exclusives https://www.facebook.com/TED
Views: 2810696 TED
Introduction to Text Analytics with R: Our First Model
This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: - Tokenization, stemming, and n-grams - The bag-of-words and vector space models - Feature engineering for textual data (e.g. cosine similarity between documents) - Feature extraction using singular value decomposition (SVD) - Training classification models using textual data - Evaluating accuracy of the trained classification models Part 4 of this video series includes specific coverage of: - Correcting column names derived from tokenization to ensure smooth model training. - Using caret to set up stratified cross validation. - Using the doSNOW package to accelerate caret machine learning training by using multiple CPUs in parallel. - Using caret to train single decision trees on text features and tune the trained model for optimal accuracy. - Evaluating the results of the cross validation process. The data and R code used in this series is available via the public GitHub: https://github.com/datasciencedojo/In... -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5JNF0 See what our past attendees are saying here: https://hubs.ly/H0f5K120 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 14731 Data Science Dojo
Advanced Data Mining with Weka (3.5: Using R to preprocess data)
Advanced Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 5: Using R to preprocess data http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/8yXNiM https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 1876 WekaMOOC
ALERT NEWS, Big  Volcano, Weather, Space Update
HELP US SPREAD THE WORD Donate with PAYPAL Link: https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=G3Q8B3C374HXQ Shop HERE For Some Great Deals https://www.brighteonstore.com?rfsn=1811179.99b16&utm_source=refersion&utm_medium=affiliate&utm_campaign=1811179.99b16 Video's have been specifically authorized by the copyright owner. We are making such material available in an effort to advance understanding of The, News, environmental, political, human rights, economic, democracy, scientific, and social justice issues, etc, Today's Featured Links: Iron Dust Nova: https://arxiv.org/pdf/1901.03621.pdf Water-Mining Satellite: https://today.ucf.edu/steam-powered-a... They Can't Measure Black Hole Mass: https://arxiv.org/pdf/1901.03345.pdf 2 Billion Source Announcement: https://arxiv.org/pdf/1901.03337.pdf unWISE Viewer: http://legacysurvey.org/viewer Links for the News: TY WindMap: https://www.windy.com Earth WindMap: http://earth.nullschool.net SDO: http://sdo.gsfc.nasa.gov/data/ Helioviewer: http://www.helioviewer.org/ SOHO: http://sohodata.nascom.nasa.gov/cgi-b... STEREO: http://stereo.gsfc.nasa.gov/cgi-bin/i... GOES Satellites: http://rammb.cira.colostate.edu/ramsd... Earthquakes: https://earthquake.usgs.gov/earthquak... RSOE: http://hisz.rsoe.hu/alertmap/index2.php by our friend Suspicious0bservershttps://youtu.be/n76e5SCrIU8?t=1s Visit us on Patreon at https://www.patreon.com/NEWSCHANNEL428 HELP US SPREAD THE WORD Donate with PAYPAL Link: https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=G3Q8B3C374HXQ And on our channel here at youtube Don't Forget To Click The Bell So You Are Notified And Please Notice to all who wish to Donate to the Missionary Fund here is the Bitcoin Donation address 15TkDH574baEe8qnye67uwcJft59tSRDgm Ethereum Donation Address 0x9043862C5B868B2E0De192b9C687729FF596916a LiteCoin: Donation Address Le9rQybKFFRt3yoHjt5y1VqnCWAFhMDew2 ALSO THE LINK BELOW THIS MESSAGE CLICK ON THE BELL NEXT TO OUR CHANNEL NAME TO MAKE SURE YOU GET NEWS UPDATE EVERY DAY https://www.youtube.com/channel/UC4nb_mFsKxODrgsaqvOstqg?view_as=public https://twitter.com/CHANNEL428 https://plus.google.com/116974518336096258288 IN ORDER TO REACH AND INFORM AS MANY PEOPLE IN THE WORLD AS POSSIBLE WITH THE TRUTH. WE REPORT YOU DECIDE WHAT TO DO WITH THE INFORMATION AND PLEASE TAKE NOTICE AND READ. IF We Air a Video from someone else, IT IS BECAUSE We have PERMISSION To do so. IN ORDER TO REACH AND INFORM AS MANY PEOPLE IN THE WORLD AS POSSIBLE WITH THE TRUTH This Christian Channel Has Many Different News, Teaching, Preaching, shows.. Just like a regular TV Station. We have many different shows every day YOU may watch what you find interesting to you. and comment, But be a Civilized Person any cursing, or insult's or other hateful comments will be removed. ALERT IF ANY videos Offends you, then DO NOT WATCH IT.. .THIS Channel will NEVER be Politically Correct. we will tell the REAL TRUTH Regardless if you like it or not.
How to Build a Text Mining, Machine Learning Document Classification System in R!
We show how to build a machine learning document classification system from scratch in less than 30 minutes using R. We use a text mining approach to identify the speaker of unmarked presidential campaign speeches. Applications in brand management, auditing, fraud detection, electronic medical records, and more.
Views: 162160 Timothy DAuria
Predicting the Winning Team with Machine Learning
Can we predict the outcome of a football game given a dataset of past games? That's the question that we'll answer in this episode by using the scikit-learn machine learning library as our predictive tool. Code for this video: https://github.com/llSourcell/Predicting_Winning_Teams Please Subscribe! And like. And comment. More learning resources: https://arxiv.org/pdf/1511.05837.pdf https://doctorspin.me/digital-strategy/machine-learning/ https://dashee87.github.io/football/python/predicting-football-results-with-statistical-modelling/ http://data-informed.com/predict-winners-big-games-machine-learning/ https://github.com/ihaque/fantasy https://www.credera.com/blog/business-intelligence/using-machine-learning-predict-nfl-games/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 85109 Siraj Raval
Bloom Filters
Bloom filters and the analysis of the probability of false positives
Views: 48508 Yoav Freund
Advanced Data Mining with Weka (2.4: MOA classifiers and streams)
Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 4: MOA classifiers and streams http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 2890 WekaMOOC
Text analytics extract key phrases using Power BI and Microsoft Cognitive Services
Download the PDF to keep as reference http://theexcelclub.com/extract-key-phrases-from-text/ FREE Power BI course - Power BI - The Ultimate Orientation http://theexcelclub.com/free-excel-training/ Or on Udemy https://www.udemy.com/power-bi-the-ultimate-orientation Or on Android App https://play.google.com/store/apps/details?id=com.PBI.trainigapp Carry out a text analytics like the big brand...only for free with Power BI and Microsoft Cognitive Services. this video will cover Obtain a Text Analytics API Key from Microsoft Cognitive Services Power BI – Setting up the Text Data Setting up the Parameter in Power BI Setting up the Custom function Query(with code to copy) Grouping the text Running the Key Phrase Extraction by calling the custom function. Extracting the key phrases from the returned Json file. Sign up to our newsletter http://theexcelclub.com/newsletter/ Watch more Power BI videos https://www.youtube.com/playlist?list=PLJ35EHVzCuiEsQ-68y0tdnaU9hCqjJ5Dh Watch Excel Videos https://www.youtube.com/playlist?list=PLJ35EHVzCuiFFpjWeK7CE3AEXy_IRZp4y Join the online Excel and PowerBI community https://plus.google.com/u/0/communities/110804786414261269900
Views: 4331 Paula Guilfoyle
Data Analysis with Python for Excel Users
A common task for scientists and engineers is to analyze data from an external source. By importing the data into Python, data analysis such as statistics, trending, or calculations can be made to synthesize the information into relevant and actionable information. See http://apmonitor.com/che263/index.php/Main/PythonDataAnalysis
Views: 161530 APMonitor.com
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: 23517 DataCamp
Anomaly Detection in Telecommunications Using Complex Streaming Data | Whiteboard Walkthrough
In this Whiteboard Walkthrough Ted Dunning, Chief Application Architect at MapR, explains in detail how to use streaming IoT sensor data from handsets and devices as well as cell tower data to detect strange anomalies. He takes us from best practices for data architecture, including the advantages of multi-master writes with MapR Streams, through analysis of the telecom data using clustering methods to discover normal and anomalous behaviors. For additional resources on anomaly detection and on streaming data: Download free pdf for the book Practical Machine Learning: A New Look at Anomaly Detection by Ted Dunning and Ellen Friedman https://www.mapr.com/practical-machine-learning-new-look-anomaly-detection Watch another of Ted’s Whiteboard Walkthrough videos “Key Requirements for Streaming Platforms: A Microservices Advantage” https://www.mapr.com/blog/key-requirements-streaming-platforms-micro-services-advantage-whiteboard-walkthrough-part-1 Read technical blog/tutorial “Getting Started with MapR Streams” sample programs by Tugdual Grall https://www.mapr.com/blog/getting-started-sample-programs-mapr-streams Download free pdf for the book Introduction to Apache Flink by Ellen Friedman and Ted Dunning https://www.mapr.com/introduction-to-apache-flink
Views: 4506 MapR Technologies
Alteryx Analytics Azure Data Lake Input & Output Tools
Watch the Alteryx Azure Data Lake Input and Output tools in action! See how these tools are used to read Azure Data Lake files into an Alteryx workflow and write back out again!
Views: 238 Alteryx
Strategy Beyond the Hockey Stick
Mining the data from thousands of large companies, McKinsey Partners Chris Bradley, Martin Hirt and Sven Smit open the windows of the strategy room, and bring an "outside view." This is not another by-the-book approach to strategy. It's not another trudge through frameworks or small-scale case studies promising a secret formula for success. It's an irreverent, fact-driven, and humorous take on the real world of strategic decision making http://www.mckinsey.com/strategybeyondthehockeystick Reserve your copy Amazon - http://amzn.to/2lusnak Barnes and Noble - http://bit.ly/2zQcwZ3 Indiebound - http://bit.ly/2Cqbrfy 800-CEO-read - http://bit.ly/2lp0dyf
Views: 8502 McKinsey & Company
013 Template Context | django ecommerce | django
Python is at number one in top technologies in the world, with top rate python rest framework django. now a days it's bein use in almost every technology like Data Mining, Machine Learning, Internet Of THings (IOT), Data Sacience, Big Data, Data Analysis....etc If you are willing to learn Python Django framework for building your ECommerce Web Application or Learning Django Framework then this course is specially for you. You can build your Own ECommerce Application at free of Cost. Now the Django Comlete Course for Building Ecommerce Applicaion is Availabel for free Now Major Topics Covered in in this complete udemy course are listed Below. Topics: 1. Getting Started 2. Hello World 3. Products Component 4. Templates 5. Bootstrap Framework 6. Search Component 7. Cart Component 8. Checkout Process 9. Fast Track to jQuery 10. Products & Async 11. Custom User Model 12. Custom Analytics 13. Stripe Integration 14. Mailchimp Integration 15. Go Live 16. Account & Settings 17. Selling Digital Items 18. Graphs and Sales 19. Thank you Don't forget to subscribe my channel for more premium content related to technology, bussiness class study and other kind of premium courses for free of cost. Youtube Channel Link: https://www.youtube.com/channel/UCUGY8RiGqnWW9qBSnQ2c8TA Complete fiverr seo course link: https://www.youtube.com/watch?v=yBDOb80oeFo&list=PLV2_Iivd4jxYDgPAtossMcZrvnTY8EBVy Check out for other courses like: Internet marketing: https://www.youtube.com/watch?v=Kik2yfe2Oog&list=PLV2_Iivd4jxbsUwA9cOEaM804euT0OGbp Python django ecommerce: https://www.youtube.com/watch?v=5bTvseLFkAo&list=PLV2_Iivd4jxYVDWCcxmccusNaUx2kWCg1 Have a Nice Day. how to learn python? how ecommerce website design. how to do ecommerce website development? what is ecommerce website? ecommerce website templates? what is python ecommerce? best django tutorial. how to do ecommerce business? ecommerce website. django server. python tutorial for beginners. python tutorials. python projects. python web development. python language. python projects for begineers.
Views: 108 ePayMinds
A.I. Is Monitoring You Right Now and Here’s How It's Using Your Data
There's wisdom in crowds, and scientists are applying artificial intelligence and machine learning to better predict global crises and outbreaks. You Could Live On One Of These Moons With an Oxygen Mask and Heavy Jacket https://www.youtube.com/watch?v=9t0Cziw6AbI Subscribe! https://www.youtube.com/user/DNewsChannel Read More: Identifying Behaviors in Crowd Scenes Using Stability Analysis for Dynamical Systems http://crcv.ucf.edu/papers/pamiLatest.pdf “A method is proposed for identifying five crowd behaviors (bottlenecks, fountainheads, lanes, arches, and blocking) in visual scenes.” Tracking in High Density Crowds Data Set http://crcv.ucf.edu/data/tracking.php “The Static Floor Field is aimed at capturing attractive and constant properties of the scene. These properties include preferred areas, such as dominant paths often taken by the crowd as it moves through the scene, and preferred exit locations.” Can Crowds Predict the Future? https://www.smithsonianmag.com/smart-news/can-crowds-predict-the-future-180948116/ “The Good Judgement Project is using the IARPA game as “a vehicle for social-science research to determine the most effective means of eliciting and aggregating geopolitical forecasts from a widely dispersed forecaster pool.” ____________________ Seeker inspires us to see the world through the lens of science and evokes a sense of curiosity, optimism and adventure. Visit the Seeker website https://www.seeker.com/ Subscribe now! https://www.youtube.com/user/DNewsChannel Seeker on Twitter http://twitter.com/seeker Seeker on Facebook https://www.facebook.com/SeekerMedia/ Seeker http://www.seeker.com/
Views: 142945 Seeker
Data Mining with Weka (4.2: Linear regression)
Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 2: Linear regression http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/augc8F https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 41233 WekaMOOC
Big Volcano Rumbles, 2 Billion Sources | S0 News Jan.14.2019
Daily Sun, Earth and Science News OTF2019: https://otf.selz.com Info: https://www.observatoryproject.com Our Websites: http://www.Suspicious0bservers.org http://www.SpaceWeatherNews.com http://www.QuakeWatch.net http://www.ObservatoryProject.com http://www.MagneticReversal.org http://www.EarthChanges.org Facebook: https://www.facebook.com/observatoryproject/ Alerts on Twitter: https://twitter.com/TheRealS0s Wanted- Earthquake Forecasters: https://youtu.be/l1iGTd84oys Earthquake Forecasting Contest: https://youtu.be/Fsa_4jAyQsI Contest Information: http://www.quakewatch.net/contest Today's Featured Links: Iron Dust Nova: https://arxiv.org/pdf/1901.03621.pdf Water-Mining Satellite: https://today.ucf.edu/steam-powered-asteroid-hoppers-developed-ucf-collaboration/ They Can't Measure Black Hole Mass: https://arxiv.org/pdf/1901.03345.pdf 2 Billion Source Announcement: https://arxiv.org/pdf/1901.03337.pdf unWISE Viewer: http://legacysurvey.org/viewer Music by NEMES1S Links for the News: TY WindMap: https://www.windy.com Earth WindMap: http://earth.nullschool.net SDO: http://sdo.gsfc.nasa.gov/data/ Helioviewer: http://www.helioviewer.org/ SOHO: http://sohodata.nascom.nasa.gov/cgi-bin/soho_movie_theater STEREO: http://stereo.gsfc.nasa.gov/cgi-bin/images GOES Satellites: http://rammb.cira.colostate.edu/ramsdis/online/goes-16.asp Earthquakes: https://earthquake.usgs.gov/earthquakes/map RSOE: http://hisz.rsoe.hu/alertmap/index2.php suspicious observers suspicious0bservers
Views: 70417 Suspicious0bservers
Multimove - A Trajectory Data Mining Tool
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
Create Physical Data Objects with Informatica Developer
Informatica's new and powerful Developer tool is used to create Data Integration and Data Quality solutions. In this vidoe you learn how to create Data Base, Flat File, and Custom Physical Data Objects(PDOs) to read or write data. These can be used in Mappings with transformational logic, and can also be used in Logical Data Objects to create Virtual Objects.
Views: 13113 dataUtrust
Big Data, Small Apps
Big Data is mostly Big Talk. The real art, however, is in getting just the right data to its targeted consumer, with all the proper context, at the right time, and on the chosen device. We call this complex dance form: Big Data, Small Apps. Come to this session to see how this impacts software architecture and product design.
Views: 275 Zoho
Data Analytics for Beginners | Introduction to Data Analytics | Data Analytics Tutorial
Data Analytics for Beginners -Introduction to Data Analytics https://acadgild.com/big-data/data-analytics-training-certification?utm_campaign=enrol-data-analytics-beginners-THODdNXOjRw&utm_medium=VM&utm_source=youtube Hello and Welcome to data analytics tutorial conducted by ACADGILD. It’s an interactive online tutorial. Here are the topics covered in this training video: • Data Analysis and Interpretation • Why do I need an Analysis Plan? • Key components of a Data Analysis Plan • Analyzing and Interpreting Quantitative Data • Analyzing Survey Data • What is Business Analytics? • Application and Industry facts • Importance of Business analytics • Types of Analytics & examples • Data for Business Analytics • Understanding Data Types • Categorical Variables • Data Coding • Coding Systems • Coding, coding tip • Data Cleaning • Univariate Data Analysis • Statistics Describing a continuous variable distribution • Standard deviation • Distribution and percentiles • Analysis of categorical data • Observed Vs Expected Distribution • Identifying and solving business use cases • Recognizing, defining, structuring and analyzing the problem • Interpreting results and making the decision • Case Study Get started with Data Analytics with this tutorial. Happy Learning For more updates on courses and tips follow us on: Facebook: https://www.facebook.com/acadgild Twitter: https://twitter.com/acadgild LinkedIn: https://www.linkedin.com/company/acadgild
Views: 227851 ACADGILD
Data Mining  Association Rule - Basic Concepts
short introduction on Association Rule with definition & Example, are explained. Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database. Parts of Association rule is explained with 2 measurements support and confidence. types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples. Names of Association rule algorithm and fields where association rule is used is also mentioned.
Data Mining Job from Home   Training
It is very simple work. You can work from home and earn a decent income as a part time worker. It is just collecting details of product supplier and pasting into the given software. PAYMENT STRUCTURE: 4000 forms complete = Re.1/record Above 4000 forms complete = Rs.2/record Above 6000 forms complete = Rs.3/record Above 10000 forms complete = Rs.5/record mail me for more details: [email protected] Thanks.
Views: 1244 D M Jiva Barathi
Educational Data Mining song: Prof. Jack Mostow (CMU)
At the EDM2014 banquet in London,UK
Views: 93 rohitkumarcmu
K mean clustering algorithm with solve example
Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 321625 Last moment tuitions
More Data Mining with Weka (3.6: Evaluating clusters)
More Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 6: Evaluating clusters http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/nK6fTv https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 20841 WekaMOOC
Count min sketch | Efficient algorithm for counting stream of data | system design components
Count Min sketch is a simple technique to summarize large amounts of frequency data. which is widely used in many places where there is a streaming big data. Donate/Patreon: https://www.patreon.com/techdummies CODE: ---------------------------------------------------------------------------- By Varun Vats: https://gist.github.com/VarunVats9/7f379199d7658b96d479ee3c945f1b4a Applications of count min sketch: ---------------------------------------------------------------------------- http://theory.stanford.edu/~tim/s15/l/l2.pdf http://highscalability.com/blog/2016/1/25/design-of-a-modern-cache.html https://spark.apache.org/docs/2.0.1/api/java/org/apache/spark/util/sketch/CountMinSketch.html Applications using Count Tracking There are dozens of applications of count tracking and in particular, the Count-Min sketch datastructure that goes beyond the task of approximating data distributions. We give three examples. 1. A more general query is to identify the Heavy-Hitters, that is, the query HH(k) returns theset of items which have large frequency (say 1/k of the overall frequency). Count trackingcan be used to directly answer this query, by considering the frequency of each item. Whenthere are very many possible items, answering the query in this way can be quite slow. Theprocess can be sped up immensely by keeping additional information about the frequenciesof groups of items [6], at the expense of storing additional sketches. As well as being ofinterest in mining applications, finding heavy-hitters is also of interest in the context of signalprocessing. Here, viewing the signal as defining a data distribution, recovering the heavy-hitters is key to building the best approximation of the signal. As a result, the Count-Minsketch can be used in compressed sensing, a signal acquisition paradigm that has recentlyrevolutionized signal processing [7]. 2. One application where very large data sets arise is in Natural Language Processing (NLP).Here, it is important to keep statistics on the frequency of word combinations, such as pairsor triplets of words that occur in sequence. In one experiment, researchers compacted a large6 Page 7 90GB corpus down to a (memory friendly) 8GB Count-Min sketch [8]. This proved to be justas effective for their word similarity tasks as using the exact data. 3. A third example is in designing a mechanism to help users pick a safe password. To makepassword guessing difficult, we can track the frequency of passwords online and disallowcurrently popular ones. This is precisely the count tracking problem. Recently, this wasput into practice using the Count-Min data structure to do count tracking (see http://www.youtube.com/watch?v=qo1cOJFEF0U). A nice feature of this solution is that the impactof a false positive—erroneously declaring a rare password choice to be too popular and sodisallowing it—is only a mild inconvenience to the user
Twitter Data Mining Extreme
Another application we use. NOTE: this is all public information. All you have to do is read a bit on the Twitter API
Views: 275 chris23892
Data Mining with Weka (2.5: Cross-validation)
Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 5: Cross-validation http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/D3ZVf8 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 38932 WekaMOOC
Follow us on https://t.me/Learnerspage Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate to deal with them. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy. BIGDATA in TELUGU https://youtu.be/jdPhsYZU_5E?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate to deal with them. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy. Data science & Big data in Telugu https://youtu.be/5XQ3lmPVV8M?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to Knowledge Discovery in Databases (KDD). Tableau in Telugu https://youtu.be/iPvwRyeAGYA?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX In 2020 the world will generate 50 times the amount of data as in 2011. And 75 times the number of information sources (IDC, 2011). Within these data are huge, unparalleled opportunities for human advancement. But to turn opportunities into reality, people need the power of data at their fingertips. Tableau is building software to deliver exactly that. Big Data Tool R Installation in Telugu https://youtu.be/hdTLyC-KL_I?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX https://cran.r-project.org/bin/windows/base/ https://www.rstudio.com/products/rstudio/download/ R is a programming language and free software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Tableau in Telugu:How to Creat Groups in Charts https://youtu.be/i1z1lGJvQQU?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX Data Warehouse in Telugu https://youtu.be/xFLE1_V7u6M Business Intelligence is a technology based on customer and profit-oriented models that reduce operating costs and provide increased profitability by improving productivity, sales, service and helps to make decision-making capabilities at no time. Business Intelligence Models are based on multidimensional analysis and key performance indicators (KPI) of an enterprise R Programming in Telugu:How to Write CVS files and Extract Data from data.[Lesson-3] https://youtu.be/oeh9fyru9-o?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX This video is about: How to write CSV file in R . How to remove the columns from data set or data frame in R R Programming Tutorial in Telugu: How to Read data in R [Lesson2] https://youtu.be/CL0RG4NTuq4?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX In This R Tutorial you will find clear way, how to read the CVS files in R Studio. How to use commands SETWD(), READ.CSV(), HEAD(), TAIL(),VIEW() FETCH DATA FROM SQL TO EXCEL https://youtu.be/IqukX_hKEnE?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX Tableau in Telugu: Tableau Colors https://youtu.be/fHvg0irp1ds?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX Pareto Chart Analysis https://youtu.be/TPZaIX4S1TU Pareto Analysis is a statistical technique in decision-making used for the selection of a limited number of tasks that produce significant overall effect. It uses the Pareto Principle (also known as the 80/20 rule) the idea that by doing 20% of the work you can generate 80% of the benefit of doing the entire job. Population Pyramid Chart https://youtu.be/poWV5VsideI Download the file in below link https://drive.google.com/file/d/1eWu8zXxh1QRFQj4OJAkG_S7AuHIDqY04/view A population pyramid also called an "age pyramid" is a graphical illustration that shows the distribution of various age groups in a population
Views: 18245 Learners Page
Data Mining with Weka (3.2: Overfitting)
Data Mining with Weka: online course from the University of Waikato Class X - Lesson X: Overfitting http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/1LRgAI https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 26513 WekaMOOC

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