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 Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 192263 Siraj Raval
http://www.data.gov http://jmcauley.ucsd.edu/data/amazon/ https://github.com/fivethirtyeight/data https://snap.stanford.edu/data/ https://json-datasets.zeef.com/jdorfman https://www.sajari.com/public-data https://archive.ics.uci.edu/ml/machine-learning-databases/ http://eforexcel.com/wp/downloads-16-sample-csv-files-data-sets-for-testing/ http://www2.informatik.uni-freiburg.de/~cziegler/BX/ https://vincentarelbundock.github.io/Rdatasets/datasets.html https://www.yelp.com/dataset_challenge http://www3.dsi.uminho.pt/pcortez/data/ http://datacatalog.worldbank.org/ https://www.briandunning.com/sample-data/ http://aws.amazon.com/public-data-sets/ https://s3.amazonaws.com/awssampledb/LoadingDataSampleFiles.zip http://training.databricks.com/workshop/usb.zip New York City Taxi Data http://dx.doi.org/10.13012/J8PN93H8 http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml http://stat-computing.org/dataexpo/2009/the-data.html https://www.whitehouse.gov/briefing-room/disclosures/annual-records/2014 http://www.ics.uci.edu/~duboisc/stackoverflow/ https://perso.telecom-paristech.fr/eagan/class/igr204/datasets
Views: 6429 Bigdata Spark Online Training
One trick to find almost any dataset for Data Science project -Free Datasets Welcome to "The AI University". Subtitles available in: Hindi, English, French About this video: This video explain how to search or find out the right datasets for your machine learning model needs. You no more have to search for these datasets at several places rather you could find them at one place. Follow me on Twitter: https://twitter.com/theaiuniverse Facebook : https://www.facebook.com/theaiuniversity GitHub Repo : https://github.com/nitinkaushik01 About this Channel: The AI University is a channel which is on a mission to democratize the Artificial Intelligence, Big Data Hadoop and Cloud Computing education to the entire world. The aim of this channel is to impart the knowledge to the data science, data analysis, data engineering and cloud architecture aspirants as well as providing advanced knowledge to the ones who already possess some of this knowledge. Please share, comment, like and subscribe if you liked this video. If you have any specific questions then you can comment on the comment section and I'll definitely try to get back to you. #DataScience #AI #TheAIUniversity
Views: 20 The AI University
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Views: 21 Image-Processing-Projects
The Excel Data Mining Addin can be used to build predictive models such as Decisions Trees within Excel. The Excel Data Mining Addin sends data to SQL Server Analysis Services (SSAS) where the models are built. The completed model is then rendered within Excel. I also have a comprehensive 60 minute T-SQL course available at Udemy : https://www.udemy.com/t-sql-for-data-analysts/?couponCode=ANALYTICS50%25OFF
Views: 75180 Steve Fox
In this Parsehub Data scraping begginer tutorial Video, I am going to show you How to Scrap data from almost any website using this simple software. You will Learn to Scrap any website data which is publically available. You don't need to have coding knowledge for web scraping like python, vba script or anything.. There is Absolutely no coding required to scrape any website you want. Software used :Parsehub Free Download Best Data scraper Software Here: Website: https://goo.gl/w232Zm Using This Free Software You Can Easily extract data from any website. Build your own dataset or API, without writing code. You Can Download free Software to scrap data. Steps to scrap the Data: You just need to open the Url of website you want to scrap. Then Just select the data you need. Finally You can Access the data(output file) via JSON, Excel(CSV) and API. Data is collected by their servers. Features:forms, open drop downs, login to websites, click on maps and handle sites with infinite scroll, tabs and pop-ups. Trying to get data from a complex and laggy sites? No worries! You can get data from that websites too. _-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_--_ Special Offers and Deals: _-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_- Best SmartPhones in 2017 Xiaomi Redmi Note 3 - 32 GB (AFFILIATE) http://amzn.to/2ffAcwq Lenovo Phab 2 Plus Smartphone (AFFILIATE) http://amzn.to/2ffDhwv Moto G Plus, 4th Gen 32 GB (AFFILIATE) http://amzn.to/2ffDxeP Lenovo Vibe K4 Note (AFFILIATE) http://amzn.to/2fXUagO ________________________________________________ I hope You liked the video, Please Like, Share.. If you have any comment queries, ask me in comments. SUBSCRIBE This Channel if You Have Not Subscribe Yet.. ♥️ It works for you? Yess then You can donate small donation for my Awesome work..😊 As this channel is non monetized😑 ➡️ Donate ➡️ Paypal: https://bit.ly/2zodsbP
Views: 60808 Just2 Techno
( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Website: https://www.edureka.co/data-science Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 78113 edureka!
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Views: 11 Image-Processing-Projects
Dynamic Creation For Database Prediction Management using Data mining and Dataset Latest IEEE Projects 2018-2019 IEEE Java Projects Topics Free IEEE Projects download Thanks & Regards, 1Crore Projects Java IEEE Projects , Head Office: No.68 & 70 ,Ground Floor, Raahat Plaza, Vadapalani,Chennai - 26. Contact: +91 7708150152 , +91 9751800789 Web :- http://1croreprojects.com/ E-Mail ID : [email protected]
Views: 14 1 Crore Projects
This data cleaning tutorial will introduce you to Python's Pandas Library in 2018. Check out our website for the best Data Science tips in 2018: https://www.dataoptimal.com Subscribe for even more Data Science tutorials! https://bit.ly/2J2O5N8 Follow us on Twitter! https://twitter.com/DataOptimal **Video Resources** Full article: https://www.dataoptimal.com/data-cleaning-with-python-2018/ Dataset: https://github.com/dataoptimal/videos/tree/master/cleaning%20messy%20data%20with%20pandas Pandas link: http://pandas.pydata.org/pandas-docs/version/0.21/indexing.html#indexing-label Error handling in Python: https://docs.python.org/3/tutorial/errors.html Matt Brems material on missing values: https://github.com/matthewbrems/ODSC-missing-data-may-18/blob/master/Analysis%20with%20Missing%20Data.pdf It's the start of a new project and you're excited to apply some machine learning models. You take a look at the data and quickly realize it's an absolute mess. According to IBM Data Analytics you can expect to spend up to 80% of your time on a project cleaning data. There's all different types of messy data, but today we're going to focus on one of the most common, missing values. We'll take a look at standard types that Pandas recognizes out of the box. Next we'll take a look at some non-standard types. These are inputs that Pandas won't automatically recognize as missing values. After that we'll take a look at unexpected types. Let's say you have a column of names that contains a 12, technically that's a missing value. After we've finished detecting missing values we'll learn how to summarize and do simple replacements.
Views: 12290 DataOptimal
In this video we'll be building our own Twitter Sentiment Analyzer in just 14 lines of Python. It will be able to search twitter for a list of tweets about any topic we want, then analyze each tweet to see how positive or negative it's emotion is. The coding challenge for this video is here: https://github.com/llSourcell/twitter_sentiment_challenge Naresh's winning code from last episode: https://github.com/Naresh1318/GenderClassifier/blob/master/Run_Code.py Victor's Runner up code from last episode: https://github.com/Victor-Mazzei/ml-gender-python/blob/master/gender.py I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ More on TextBlob: https://textblob.readthedocs.io/en/dev/ Great info on Sentiment Analysis: https://www.quora.com/How-does-sentiment-analysis-work Great sentiment analysis api: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis Read over these course notes if you wanna become an NLP god: http://cs224d.stanford.edu/syllabus.html Best book to become a Python god: https://learnpythonthehardway.org/ Please share this video, like, comment and subscribe! That's what keeps me going. Feel free to support me on Patreon: https://www.patreon.com/user?u=3191693 Two Minute Papers Link: https://www.youtube.com/playlist?list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e 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 Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 281042 Siraj Raval
ChennaiSunday Systems Pvt.Ltd We are ready to provide guidance to successfully complete your projects and also download the abstract, base paper from our website IEEE 2014 Java Projects: http://www.chennaisunday.com/projectsNew.php?id=1&catName=IEEE_2014-2015_Java_Projects IEEE 2014 Dotnet Projects: http://www.chennaisunday.com/projectsNew.php?id=20&catName=IEEE_2014-2015_DotNet_Projects Output Videos: https://www.youtube.com/channel/UCCpF34pmRlZbAsbkareU8_g/videos IEEE 2013 Java Projects: http://www.chennaisunday.com/projectsNew.php?id=2&catName=IEEE_2013-2014_Java_Projects IEEE 2013 Dotnet Projects: http://www.chennaisunday.com/projectsNew.php?id=3&catName=IEEE_2013-2014_Dotnet_Projects Output Videos: https://www.youtube.com/channel/UCpo4sL0gR8MFTOwGBCDqeFQ/videos IEEE 2012 Java Projects: http://www.chennaisunday.com/projectsNew.php?id=26&catName=IEEE_2012-2013_Java_Projects Output Videos: https://www.youtube.com/user/siva6351/videos IEEE 2012 Dotnet Projects: http://www.chennaisunday.com/projectsNew.php?id=28&catName=IEEE_2012-2013_Dotnet_Projects Output Videos: https://www.youtube.com/channel/UC4nV8PIFppB4r2wF5N4ipqA/videos IEEE 2011 Java Projects: http://chennaisunday.com/projectsNew.php?id=29&catName=IEEE_2011-2012_Java_Project IEEE 2011 Dotnet Projects: http://chennaisunday.com/projectsNew.php?id=33&catName=IEEE_2011-2012_Dotnet_Projects Output Videos: https://www.youtube.com/channel/UCtmBGO0q5XZ5UsMW0oDhZ-A/videos IEEE PHP Projects: http://www.chennaisunday.com/projectsNew.php?id=41&catName=IEEE_PHP_Projects Output Videos: https://www.youtube.com/user/siva6351/videos Java Application Projects: http://www.chennaisunday.com/projectsNew.php?id=34&catName=Java_Application_Projects Dotnet Application Projects: http://www.chennaisunday.com/projectsNew.php?id=35&catName=Dotnet_Application_Projects Android Application Projects: http://www.chennaisunday.com/projectsNew.php?id=36&catName=Android_Application_Projects PHP Application Projects: http://www.chennaisunday.com/projectsNew.php?id=37&catName=PHP_Application_Projects Struts Application Projects: http://www.chennaisunday.com/projectsNew.php?id=38&catName=Struts_Application_Projects Java Mini Projects: http://www.chennaisunday.com/projectsNew.php?id=39&catName=Java_Mini_Projects Dotnet Mini Projects: http://www.chennaisunday.com/projectsNew.php?id=40&catName=Dotnet_Mini_Projects -- *Contact * * P.Sivakumar MCA Director Chennai Sunday Systems Pvt Ltd Phone No: 09566137117 No: 1,15th Street Vel Flats Ashok Nagar Chennai-83 Landmark R3 Police Station Signal (Via 19th Street) URL: www.chennaisunday.com
Views: 147 Chennai Sunday
Link to download data file: https://drive.google.com/open?id=0B5W8CO0Gb2GGUVNyZ1JqMW1NZjA Includes example of data partition or data splitting with R. - Shows steps for reading CSV file into R. - Illustrates developing linear regression model using training data and then making predictions using validation data set in r. - Discusses regression coefficients - Provides application example using an automobile warranty claims dataset 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: 32095 Bharatendra Rai
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: 82769 WekaMOOC
Watch complete tutorial: https://click.linksynergy.com/fs-bin/click?id=gD7cdGyIKG4&offerid=529351.12&type=3&subid=0 What topics will you cover:data analytics ,what is data mining ,data mining definition,introduction to data mining,decision tree in data mining,datasets for data mining, educational data mining, weka data mining, data mining techniques,data mining concepts and techniques,data mining process ,data mining analysis ,data mining methods ,data mining in business . Master data mining software,data mining tools ,data analytics tools. We offer free online courses with certificates, online training,online study ,free online classes ,distance education,online learning,distance learning .
Views: 9 Kan
In this video, Kaggle Data Scientist Rachael shows you how to search for the perfect dataset for your project using Kaggle's dataset listing. SUBSCRIBE: http://www.youtube.com/user/kaggledotcom?sub_confirmation=1&utm_medium=youtube&utm_source=channel&utm_campaign=yt-sub About Kaggle: Kaggle is the world's largest community of data scientists. Join us to compete, collaborate, learn, and do your data science work. Kaggle's platform is the fastest way to get started on a new data science project. Spin up a Jupyter notebook with a single click. Build with our huge repository of free code and data. Stumped? Ask the friendly Kaggle community for help. Follow Kaggle online: Visit the WEBSITE: http://www.kaggle.com/?utm_medium=youtube&utm_source=channel&utm_campaign=yt-kg Like Kaggle on FACEBOOK: http://www.facebook.com/kaggle?utm_medium=youtube&utm_source=channel&utm_campaign=yt-fb Follow Kaggle on TWITTER: http://twitter.com/kaggle?utm_medium=youtube&utm_source=channel&utm_campaign=yt-tw Check out our BLOG: http://blog.kaggle.com/?utm_medium=youtube&utm_source=channel&utm_campaign=yt-blog Connect with us on LINKEDIN: http://www.linkedin.com/company/kaggle?utm_medium=youtube&utm_source=channel&utm_campaign=yt-lkn Advance your data science skills: Take our free online courses: http://www.kaggle.com/learn/overview?utm_medium=youtube&utm_source=channel&utm_campaign=yt-learn Get started with Kaggle Kernels: http://www.kaggle.com/docs/kernels?utm_medium=youtube&utm_source=channel&utm_campaign=yt-krnl Download clean datasets from Kaggle: http://www.kaggle.com/docs/datasets?utm_medium=youtube&utm_source=channel&utm_campaign=yt-datast Sign up for a Kaggle Competition: http://www.kaggle.com/docs/competitions?utm_medium=youtube&utm_source=channel&utm_campaign=yt-comps Explore the Kaggle Public API: http://www.kaggle.com/docs/api?utm_medium=youtube&utm_source=channel&utm_campaign=yt-docs Getting Started on Kaggle: Finding datasets | Kaggle https://www.youtube.com/watch?v=r-KlpAunhgg Kaggle http://www.youtube.com/user/kaggledotcom
Views: 1789 Kaggle
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: 471250 Brandon Weinberg
Take the Full Course of Artificial Intelligence What we Provide 1) 28 Videos (Index is given down) 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in Artificial Intelligence Sample Notes : https://goo.gl/aZtqjh To buy the course click https://goo.gl/H5QdDU if you have any query related to buying the course feel free to email us : [email protected] Other free Courses Available : Python : https://goo.gl/2gftZ3 SQL : https://goo.gl/VXR5GX Arduino : https://goo.gl/fG5eqk Raspberry pie : https://goo.gl/1XMPxt Artificial Intelligence Index 1)Agent and Peas Description 2)Types of agent 3)Learning Agent 4)Breadth first search 5)Depth first search 6)Iterative depth first search 7)Hill climbing 8)Min max 9)Alpha beta pruning 10)A* sums 11)Genetic Algorithm 12)Genetic Algorithm MAXONE Example 13)Propsotional Logic 14)PL to CNF basics 15) First order logic solved Example 16)Resolution tree sum part 1 17)Resolution tree Sum part 2 18)Decision tree( ID3) 19)Expert system 20) WUMPUS World 21)Natural Language Processing 22) Bayesian belief Network toothache and Cavity sum 23) Supervised and Unsupervised Learning 24) Hill Climbing Algorithm 26) Heuristic Function (Block world + 8 puzzle ) 27) Partial Order Planing 28) GBFS Solved Example
Views: 294450 Last moment tuitions
Support Vector Machine (SVM) - Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS COURSE - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML ►MACHINE LEARNING COURSES -http://augmentedstartups.info/machine-learning-courses ------------------------------------------------------------------------ A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. To understand SVM’s a bit better, Lets first take a look at why they are called support vector machines. So say we got some sample data over here of features that classify whether a observed picture is a dog or a cat, so we can for example look at snout length or and ear geometry if we assume that dogs generally have longer snouts and cat have much more pointy ear shapes. So how do we decide where to draw our decision boundary? Well we can draw it over here or here or like this. Any of these would be fine, but what would be the best? If we do not have the optimal decision boundary we could incorrectly mis-classify a dog with a cat. So if we draw an arbitrary separation line and we use intuition to draw it somewhere between this data point for the dog class and this data point of the cat class. These points are known as support Vectors – Which are defined as data points that the margin pushes up against or points that are closest to the opposing class. So the algorithm basically implies that only support vector are important whereas other training examples are ‘ignorable’. An example of this is so that if you have our case of a dog that looks like a cat or cat that is groomed like a dog, we want our classifier to look at these extremes and set our margins based on these support vectors. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 213357 Augmented Startups
A/Prof Zahid Islam presents a freely downloadable software for building a decision forest from a cost-sensitive dataset.
Views: 34 Zahid's Data Mining Channel
Video from “Practical XGBoost in Python” ESCO Course. FREE COURSE: http://education.parrotprediction.teachable.com/courses/practical-xgboost-in-python
Views: 7021 Parrot Prediction Ltd.
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: 1021295 David Langer
Mining Competitors from Large Unstructured Datasets IEEE 2017 2018 PROJECTS Abstract—In any competitive business, success is based on the ability to make an item more appealing to customers than the competition. A number of questions arise in the context of this task: how do we formalize and quantify the competitiveness between two items? Who are the main competitors of a given item? What are the features of an item that most affect its competitiveness? Despite the impact and relevance of this problem to many domains, only a limited amount of work has been devoted toward an effective solution. In this paper, we present a formal definition of the competitiveness between two items, based on the market segments that they can both cover. Our evaluation of competitiveness utilizes customer reviews, an abundant source of information that is available in a wide range of domains. We present efficient methods for evaluating competitiveness in large review datasets and address the natural problem of finding the top-k competitors of a given item. Finally, we evaluate the quality of our results and the scalability of our approach using multiple datasets from different domains. http://www.micansinfotech.com/index.html http://www.micansinfotech.com/VIDEOS-2017-2018.html http://www.micansinfotech.com/VIDEOS-ANDROID-2017-2018.html http://www.micansinfotech.com/VIDEOS-APPLICATION-PROJECT-2017-2018#PHP http://www.micansinfotech.com/VIDEOS-APPLICATION-PROJECT-2017-2018.html http://www.micansinfotech.com/IEEE-PROJECTS-CSE-2017-2018.html http://www.micansinfotech.com/IEEE-PROJECTS-POWERELECTRONICS-2017-2018.html http://www.micansinfotech.com/IEEE-PROJECTS-MECHANICAL-FABRICATION-2017-2018.html http://www.micansinfotech.com/CONTACT-US.html MICANS INFOTECH offers Projects in CSE ,IT, EEE, ECE, MECH , MCA. MPHIL , BSC, in various domains JAVA ,PHP, DOT NET , ANDROID , MATLAB , NS2 , EMBEDDED , VLSI , APPLICATION PROJECTS , IEEE PROJECTS. CALL : +91 90036 28940 +91 94435 11725 [email protected] WWW.MICANSINFOTECH.COM
Views: 1645 MICANS INFOTECH PVT LTD
Naive Bayes Classifier- Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS COURSE - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML ►MACHINE LEARNING COURSES - http://augmentedstartups.info/machine-learning-courses -------------------------------------------------------------------------------- Now Naïve Bayes is based on Bayes Theorem also known as conditional Theorem, which you can think of it as an evidence theorem or trust theorem. So basically how much can you trust the evidence that is coming in, and it’s a formula that describes how much you should believe the evidence that you are being presented with. An example would be a dog barking in the middle of the night. If the dog always barks for no good reason, you would become desensitized to it and not go check if anything is wrong, this is known as false positives. However if the dog barks only whenever someone enters your premises, you’d be more likely to act on the alert and trust or rely on the evidence from the dog. So Bayes theorem is a mathematic formula for how much you should trust evidence. So lets take a look deeper at the formula, • We can start of with the Prior Probability which describes the degree to which we believe the model accurately describes reality based on all of our prior information, So how probable was our hypothesis before observing the evidence. • Here we have the likelihood which describes how well the model predicts the data. This is term over here is the normalizing constant, the constant that makes the posterior density integrate to one. Like we seen over here. • And finally the output that we want is the posterior probability which represents the degree to which we believe a given model accurately describes the situation given the available data and all of our prior information. So how probable is our hypothesis given the observed evidence. So with our example above. We can view the probability that we play golf given it is sunny = the probability that we play golf given a yes times the probability it being sunny divided by probability of a yes. This uses the golf example to explain Naive Bayes. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 167796 Augmented Startups
Social network analysis with several simple examples in R. R file: https://goo.gl/CKUuNt Data file: https://goo.gl/Ygt1rg Includes, - Social network examples - Network measures - Read data file - Create network - Histogram of node degree - Network diagram - Highlighting degrees & different layouts - Hub and authorities - Community detection 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: 23527 Bharatendra Rai
First video of our latest course by Daniel Chen: Cleaning Data in Python. Like and comment if you enjoyed the video! A vital component of data science involves acquiring raw data and getting it into a form ready for analysis. In fact, it is commonly said that data scientists spend 80% of their time cleaning and manipulating data, and only 20% of their time actually analyzing it. This course will equip you with all the skills you need to clean your data in Python, from learning how to diagnose your data for problems to dealing with missing values and outliers. At the end of the course, you'll apply all of the techniques you've learned to a case study in which you'll clean a real-world Gapminder dataset! So you've just got a brand new dataset and are itching to start exploring it. But where do you begin, and how can you be sure your dataset is clean? This chapter will introduce you to the world of data cleaning in Python! You'll learn how to explore your data with an eye for diagnosing issues such as outliers, missing values, and duplicate rows. Try the first chapter for free: https://www.datacamp.com/courses/cleaning-data-in-python
Views: 16055 DataCamp
In this video, you will see how to do some basic data analysis with Microsoft Excel. You'll see how to use various functions and get an introduction to use pivot tables Data source: https://www.kaggle.com/c/titanic/data Please comment if you want such set of videos. Cheers!
Views: 4152 Windows Tech Channel
Welcome to a Python for Finance tutorial series. In this series, we're going to run through the basics of importing financial (stock) data into Python using the Pandas framework. From here, we'll manipulate the data and attempt to come up with some sort of system for investing in companies, apply some machine learning, even some deep learning, and then learn how to back-test a strategy. I assume you know the fundamentals of Python. If you're not sure if that's you, click the fundamentals link, look at some of the topics in the series, and make a judgement call. If at any point you are stuck in this series or confused on a topic or concept, feel free to ask for help and I will do my best to help. https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 333696 sentdex
Provides steps for carrying handling class imbalance problem when developing classification and prediction models Download R file: https://goo.gl/ns7zNm data: https://goo.gl/d5JFtq Includes, - What is Class Imbalance Problem? - Data partitioning - Data for developing prediction model - Developing prediction model - Predictive model evaluation - Confusion matrix, - Accuracy, sensitivity, and specificity - Oversampling, undersampling, synthetic sampling using random over sampling examples predictive models are important machine learning and statistical tools related to analyzing big data or working in data science field. 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: 16165 Bharatendra Rai
www.learnanalytics.in demostrates use of an free and open source platform to build sophisticated predictive models. We demonstrate using R package Rattle to do data analysis without writing a line of r code. We cover hypothesis testing, descriptive statistics, linear and logistic regression with a flavor of machine learning (Random Forest, SVM etc.). Also using graphs such as ROC curves and Area under curves (AUC) to compare various models. To download the dataset and follow on your own follow http://www.learnanalytics.in/datasets/Credit_Scoring.zip
Views: 43945 Learn Analytics
( Data Science Training - https://www.edureka.co/data-science ) Watch the sample class recording: http://www.edureka.co/data-science?utm_source=youtube&utm_medium=referral&utm_campaign=problem-datasets-datasc In this Video, a problem dataset titled ‘Products and Retail’ is taken. The domain of the dataset being Communications and Media. The problem statement being Clustering/Grouping documents based on their contents. Topics covered in the video are: 1. What are Datasets 2. Problem Dataset: Products and Retail Related blogs: http://www.edureka.co/blog/application-of-clustering-in-data-science-using-real-life-examples/?utm_source=youtube&utm_medium=referral&utm_campaign=problem-datasets-datasc http://www.edureka.co/blog/who-can-take-up-a-data-science-tutorial/?utm_source=youtube&utm_medium=referral&utm_campaign=problem-datasets-datasc Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would prefer a hassle free and self paced learning environment, accessible from any part of the world. The topics related to ‘Problem Datasets in Data Science’ have been covered in our course ‘Data science’. For more information, please write back to us at [email protected] Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004
Views: 3114 edureka!
Whenever we do classification in ML, we often assume that target label is evenly distributed in our dataset. This helps the training algorithm to learn the features as we have enough examples for all the different cases. For example, in learning a spam filter, we should have good amount of data which corresponds to emails which are spam and non spam. SMOTE synthesises new minority instances between existing (real) minority instances. If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer those. If you enjoy these tutorials & would like to support them then the easiest way is to simply like the video & give it a thumbs up & also it's a huge help to share these videos with anyone who you think would find them useful. Please consider clicking the SUBSCRIBE button to be notified for future videos & thank you all for watching. You can find me on: GitHub - https://github.com/bhattbhavesh91 Medium - https://medium.com/@bhattbhavesh91 #ClassImbalance #SMOTE #SyntheticMinorityOversamplingTechnique #machinelearning #python #deeplearning #datascience #youtube
Views: 415 Bhavesh Bhatt
BEST WAY TO STUDY is a student friendly channel....it provides videos related to education such as important topics,tips to score well,tutorials etc... SUBSCRIBE AND HIT THE BELL ICON FOR MORE SUCH VIDEOS:) Disclaimer: The content written and spoken in this video are the soul property of BEST WAY TO STUDY . In case of any resemblance to any sites or any videos are mere coincidence. "IEEE","IEEE paper","final year project","final year project topics","computer engineering","information technology","project topic","computer engineering project","how to find project","research papers","#final year projects","#live projects","#major projects","#java projects","#php projects","#cse projects","#project abstracts","#mini projects","#ieee projects","#free projects","#project ideas","#civil projects","#mechanical projects"
Views: 30 best way to study
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Views: 7218 Clickmyproject
Analysis and prediction of users Behaviour by Mining Twitter Data Using Dataset using java Latest IEEE Projects 2018-2019 IEEE Java Projects Topics Free IEEE Projects download Thanks & Regards, 1Crore Projects Java IEEE Projects , Head Office: No.68 & 70 ,Ground Floor, Raahat Plaza, Vadapalani,Chennai - 26. Contact: +91 7708150152 , +91 9751800789 Web :- http://1croreprojects.com/ E-Mail ID : [email protected]
Views: 35 1 Crore Projects
This video will show you how to create and load dataset in weka tool. weather data set excel file https://eric.univ-lyon2.fr/~ricco/tanagra/fichiers/weather.xls
Views: 44351 HowTo
Look what we have for you! Another complete project in Machine Learning! In today's tutorial, we will be building a Credit Card Fraud Detection System from scratch! It is going to be a very interesting project to learn! It is one of the 10 projects from our course 'Projects in Machine Learning' which is currently running on Kickstarter. For this project, we will be using the several methods of Anomaly detection with Probability Densities. We will be implementing the two major algorithms namely, 1. A local out wire factor to calculate anomaly scores. 2. Isolation forced algorithm. To get started we will first build a dataset of over 280,000 credit card transactions to work on! You can access the source code of this tutorial here: https://github.com/eduonix/creditcardML Learn It Up! Summer’s Hottest Learning Sale Is Here! Pick Any Sun-sational Course & Get Other Absolutely FREE! Link: http://bit.ly/summer-bogo-2019 Want to learn Machine learning in detail? Then try our course Machine Learning For Absolute Beginners at just $10. New Machine Learning Project Course for Beginners - http://bit.ly/2V8edMT You can even check FREE course on Predict Board Game Reviews with Machine Learning on http://bit.ly/2Wm2uKW Kickstarter Campaign on AI and ML E-Degree is Launched. Back this Campaign and Explore all the Courses with over 58 Hours of Learning. Link- http://bit.ly/aimledegree Thank you for watching! We’d love to know your thoughts in the comments section below. Also, don’t forget to hit the ‘like’ button and ‘subscribe’ to ‘Eduonix Learning Solutions’ for regular updates. https://goo.gl/BCmVLG Follow Eduonix on other social networks: ■ Facebook: http://bit.ly/2nL2p59 ■ Linkedin: http://bit.ly/2nKWhKa ■ Instagram: http://bit.ly/2nL8TRu | @eduonix ■ Twitter: http://bit.ly/2eKnxq8
Views: 125630 Eduonix Learning Solutions
Mining Competitors from Large Unstructured Datasets To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org In any competitive business, success is based on the ability to make an item more appealing to customers than the competition. A number of questions arise in the context of this task: how do we formalize and quantify the competitiveness between two items? Who are the main competitors of a given item? What are the features of an item that most affect its competitiveness? Despite the impact and relevance of this problem to many domains, only a limited amount of work has been devoted toward an effective solution. In this paper, we present a formal definition of the competitiveness between two items, based on the market segments that they can both cover. Our evaluation of competitiveness utilizes customer reviews, an abundant source of information that is available in a wide range of domains. We present efficient methods for evaluating competitiveness in large review datasets and address the natural problem of finding the top-k competitors of a given item. Finally, we evaluate the quality of our results and the scalability of our approach using multiple datasets from different domains.
Views: 1752 jpinfotechprojects
Learn how to calculate the interquartile range, which is a measure of the spread of data in a data set. Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/cc-6th/e/calculating-the-interquartile-range--iqr-?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Watch the next lesson: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/cc-6-mad/v/mean-absolute-deviation?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Missed the previous lesson? https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/cc-6th-box-whisker-plots/v/interpreting-box-plots?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Grade 6th on Khan Academy: By the 6th grade, you're becoming a sophisticated mathemagician. You'll be able to add, subtract, multiply, and divide any non-negative numbers (including decimals and fractions) that any grumpy ogre throws at you. Mind-blowing ideas like exponents (you saw these briefly in the 5th grade), ratios, percents, negative numbers, and variable expressions will start being in your comfort zone. Most importantly, the algebraic side of mathematics is a whole new kind of fun! And if that is not enough, we are going to continue with our understanding of ideas like the coordinate plane (from 5th grade) and area while beginning to derive meaning from data! (Content was selected for this grade level based on a typical curriculum in the United States.) About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to Khan AcademyÂÃÂªs 6th grade channel: https://www.youtube.com/channel/UCnif494Ay2S-PuYlDVrOwYQ?sub_confirmation=1 Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 429620 Khan Academy
-- Created using PowToon -- Free sign up at http://www.powtoon.com/join -- Create animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 1723 Bonnie Mott
Complete project details with full project source code and database visit at : https://www.freeprojectz.com/paid-project/python-django-mysql-project/student-performance-prediction-system If you need this project then you call or whatsapp me on +91-8470010001. You can also write email us on [email protected] Premium Projects : https://www.freeprojectz.com/premium-projects Free Project Source Code and Database: https://www.freeprojectz.com/free-projects-with-source-code
Views: 564 FreeProjectz
Large data sets often present unique challenges. Processes may fail or take long to complete. This session will cover how to use the free R package to manipulate large geographic data sets as a set of repeatable processes -- helping you to automate your workflow and overcome these unique challenges. Ready to improve your scripting kung fu? During this webinar, you will learn: -how to read and manipulate point datasets (geocoded locations) -how to aggregate points to polygons (such as a census geographies) -how to generate a heatmap -how to combine these steps into a reusable R script Audience: This webinar is targeted at a more technical audience and is appropriate for anyone who has some experience with command line tools or scripting. Level of Difficulty: Experienced
Views: 893 azavea
Import Data, Copy Data from Excel (or other spreadsheets) to R: CSV & TXT Files with Free Practice Dataset: (https://bit.ly/2rOfgEJ) Need More Statistics and R Programming Tutorials? (https://bit.ly/2Fhu9XU) How to Import CSV data into R or How to Import TXT files into R from Excel or other spreadsheets using function in R ►How to import CSV data into R? We will be using "read.table" function to import comma separated data into R ► How to import txt data file into R? You will learn to use "read.delim" function to import the tab-delimited text file into R ► You will also learn to use "file.choose" argument for file location, "header" argument to let R know the data has headers or variable names and "sep" argument to let R know how the data values are separated. ►►Download the dataset here: https://statslectures.com/r-scripts-datasets ►►Like to support us? You can Donate https://bit.ly/2CWxnP2 or Share the Videos! ►► Watch More: ► Intro to Statistics Course: https://bit.ly/2SQOxDH ►R Tutorials for Data Science https://bit.ly/1A1Pixc ►Getting Started with R (Series 1): https://bit.ly/2PkTneg ►Graphs and Descriptive Statistics in R (Series 2): https://bit.ly/2PkTneg ►Probability distributions in R (Series 3): https://bit.ly/2AT3wpI ►Bivariate analysis in R (Series 4): https://bit.ly/2SXvcRi ►Linear Regression in R (Series 5): https://bit.ly/1iytAtm ►ANOVA series https://bit.ly/2zBwjgL ►Linear Regression Concept and with R https://bit.ly/2z8fXg1 ►Puppet Master of Statistics: https://bit.ly/2RDAAv4 ►Hypothesis Testing: Concepts in Statistics https://bit.ly/2Ff3J9e ◼︎ Table of Content 0:00:17 What are the two main file types for saving a data file (CSV and TXT) 0:00:36 How to save an Excel file as a CSV file (comma-separated value) 0:01:10 How to open a CSV data file in Excel 0:01:20 How to open a CSV file in a text editor 0:01:36 How to import CSV file into R? using read.csv function 0:01:44 How to access the help menu for different commands/functions in R 0:02:04 How to specify file location for R? using file.choose argument on read.csv function 0:02:31 How to let R know our data has headers or variable names when importing the data into R? By using the “header” argument on read.csv function 0:03:22 How to import CSV file into R? using read.table function 0:03:38 How to specify the file location for the read.table function in R? using file.choose argument 0:03:46 How to specify how variables/columns are separated when importing data into R? the "sep" argument on read.table function will do that; for example if you don't specify that your data is comma separated, R ends up reading it all in as one variable 0:04:10 How to save a file in Excel as tab-delimited text (TXT) file 0:04:50 How to open a tab-delimited (.TXT) data file in a text editor 0:05:07 How to open a tab-delimited (.TXT) data file in excel 0:05:20 How to import tab-delimited (.TXT) data file into R? using read.delim function 0:05:44 How to specify the file path for read.delim function in R? using file.choose argument 0:06:06 How to import tab-delimited (.TXT) data file into R? using read.table function 0:06:23 How to specify that the data has headers or variable names when importing the data into R? using header argument on read.table function This video is a tutorial for programming in R Statistical Software for beginners, using RStudio. Follow MarinStatsLectures Subscribe: https://goo.gl/4vDQzT website: https://statslectures.com Facebook:https://goo.gl/qYQavS Twitter:https://goo.gl/393AQG Instagram: https://goo.gl/fdPiDn Our Team: Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC. Producer and Creative Manager: Ladan Hamadani (B.Sc., BA., MPH) These videos are created by #marinstatslectures to support some courses at The University of British Columbia (UBC) (#IntroductoryStatistics and #RVideoTutorials for Health Science Research), although we make all videos available to the everyone everywhere for free. Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!
Views: 595573 MarinStatsLectures- R Programming & Statistics
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577 Shop Now @ https://myprojectbazaar.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 120 myproject bazaar
Please feel free to get in touch with me :) If it helped you, please like my facebook page and don't forget to subscribe to Last Minute Tutorials. Thaaank Youuu. Facebook: https://www.facebook.com/Last-Minute-Tutorials-862868223868621/ Website: www.lmtutorials.com For any queries or suggestions, kindly mail at: [email protected]
Views: 99137 Last Minute Tutorials
More Data Mining with Weka: online course with FutureLearn from the University of Waikato. First session starts 8 May 2017 https://www.futurelearn.com/courses/more-data-mining-with-weka/ https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 10250 WekaMOOC