Search results “Public datasets for data mining”
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 Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 185418 Siraj Raval
AWS Public Datasets: Learnings from Staging Petabytes of Data for Analysis in AWS
AWS Public Sector Summit 2018 - Washington, D.C. AWS hosts a variety of public data sets that anyone can access for free. Previously, large datasets such as satellite imagery or genomic data have required hours or days to locate, download, customize, and analyze. When data is made publicly available on AWS, anyone can analyze any volume of data without needing to download or store it themselves. The AWS Open Data Team will share tips and tricks, patterns and anti-patterns and tools to help you most effectively stage your data for analysis in the cloud. Speakers: Joe Flasher, Dave Rocamora, Jed Sundwall
Views: 449 Amazon Web Services
Analyzing Public Datasets 2: Finding the Data
In this new series, we'll learn how to access and analyze public datasets resulting from next-generation sequencing techniques such as Illumina and 454. This video shows how to find a sample dataset, upload it to Galaxy, and process it for alignment.
Views: 12663 David Coil
Datasets : How to Download?
Datasets : How to Download?
Views: 7141 Social Networks
How to download Dataset from UCI Repository
The video has sound issues. please bare with us. This video will help in demonstrating the step-by-step approach to download Datasets from the UCI repository.
Views: 11409 Santhosh Shanmugam
8/17/18 Using Analytic Solver Data Mining to Gain Insights from Your Data in Excel 1
Live Webinar Recording: Do you want to learn and get results quickly from data mining and predictive analytics for your business? Have you found that "enterprise data mining" software involves far too much cost, risk and learning time? Do you want to apply traditional time series forecasting and regression, and also new data mining methods? Is the data you need found in SQL databases, data warehouses, public datasets, and Excel spreadsheets? Easily draw samples and build data mining models for all your data using Excel, PowerPivot and Analytic Solver Data Mining Use XLMiner's tools to uncover and visualize hidden relationships, clean and transform, and cluster your data Forecast future trends with a full range of traditional ARIMA, exponential smoothing, and regression methods Create classification and prediction models using the full spectrum of data mining methods -- from discriminant analysis and logistic regression to classification/regression trees, association rules, and neural networks
Views: 800 FrontlineSolvers
Access and Analyze Large-scale Public Datasets on Google Cloud (Cloud Next '18)
Come learn about the public datasets hosted on Google Cloud. We will demonstrate how to access and join a variety of public datasets, including NOAA's near-time weather datasets. DA102 Event schedule → http://g.co/next18 Watch more Data Analytics sessions here → http://bit.ly/2KXMtcJ Next ‘18 All Sessions playlist → http://bit.ly/Allsessions Subscribe to the Google Cloud channel! → http://bit.ly/NextSub
Extract Facebook Data and save as CSV
Extract data from the Facebook Graph API using the facepager tool. Much easier for those of us who struggle with API keys ;) . Blog Post: http://davidsherlock.co.uk/using-facepager-find-comments-facebook-page-posts/
Views: 206756 David Sherlock
Finding Datasets
Learn how to find datasets with RoperExpress in the Roper Center polling data archive
Mining Patterns from Complex Datasets via Sampling by Dr. Zaki
Symposium of Data Mining Applications on 8th of May 2014 Dr. Mohammed Zaki, Keynote Speaker
Views: 219 Megdam Center
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: 210851 APMonitor.com
Analyzing Big Data in less time with Google BigQuery
Most experienced data analysts and programmers already have the skills to get started. BigQuery is fully managed and lets you search through terabytes of data in seconds. It’s also cost effective: you can store gigabytes, terabytes, or even petabytes of data with no upfront payment, no administrative costs, and no licensing fees. In this webinar, we will: - Build several highly-effective analytics solutions with Google BigQuery - Provide a clear road map of BigQuery capabilities - Explain how to quickly find answers and examples online - Share how to best evaluate BigQuery for your use cases - Answer your questions about BigQuery
Views: 75891 Google Cloud Platform
Mining Public Datasets Using Open Source Tools - Alexander Bezzubov - FOSSASIA Summit 2016
Speaker: Alexander Bezzubov (NFLabs Inc, Apache Software Foundation) About the talk: There are plenty of public datasets out there available, in this talk we will showcase opensource tools from BigData ecosystem available for practitioner to mine them, at scale and on a budget. About Alexander Bezzubov: Apache Zeppelin (incubating) committer and PPMC, Engineer @NFLabs, Event page: http://2016.fossasia.org Produced by Engineers.SG
Views: 58 FOSSASIA
Data Mining Tools for Extremely Large Datasets - WBTShowcase 2010
Presentation on Data Mining Tools for Extremely Large Datasets by NDSU Research Foundation at the WBTShowcase on 3/15/2010.
Views: 795 InnovationArlington
Using Apache Spark 2.0 to Analyze the City of San Francisco's Open Data
Sameer Farooqui delivers a hands-on tutorial using Spark SQL and DataFrames to retrieve insights and visualizations from datasets published by the City of San Francisco. [Topics Indexed Below] The labs are targeted for an audience with some general programming or SQL query experience, but little to no experience with Spark. Sameer will begin with some brief theory and lecture on Spark, before diving into several demos performing visualizations and analysis on calls made to the San Francsico Fire Department on July 4th. Follow Along: + Databricks Community Edition: https://databricks.com/try + Labs: https://bit.ly/sfopenlabs + Learning Material: https://bit.ly/sfopenreadalong -----Jump to Topic----- 00:00:06 - Workshop Intro & Environment Setup 00:13:06 - Brief Intro to Spark 00:17:32 - Analysis Overview: SF Fire Department Calls for Service 00:23:22 - Analysis with PySpark DataFrames API 00:29:32 - Doing Date/Time Analysis 00:47:53 - Memory, Caching and Writing to Parquet 01:00:40 - SQL Queries 01:21:11 - Convert a Spark DataFrame to a Pandas DataFrame -----Q & A----- 01:24:43 - Spark DataFrames vs. SQL: Pros and Cons? 01:26:57 - Workflow for Chaining Databricks notebooks into Pipeline? 01:30:27 - Is Spark 2.0 ready to use in production? ---------------------------------------------------------------------------------------------- SPARK 2.0 TRAINING | NewCircle | Onsite & Public Classes ---------------------------------------------------------------------------------------------- + Programming for Spark 2.0 (3 days) + Spark 2.0 for Machine Learning & Data Science (3 days) Learn more: https://newcircle.com/category/apache-spark ++Code for San Francisco++ http://www.meetup.com/Code-for-San-Francisco-Civic-Hack-Night/ ++Learn more about Databricks++ https://databricks.com/product/databricks
Views: 101792 InfoQ
Data Visualization Tutorial - Communication Networks Gephi
High level tutorial providing an insight into data visualization from communication network analysis algorithms applied on datasets. I hope you enjoy the video. This example is based on the Enron Email Dataset. The corpus is public and available online. Complete project description (Data Mining the Enron Email Dataset): http://www.philipstarritt.com/enron Main language / technology: Java Don't forget to check out all my new 2016 Java, Spring Boot and Camel Videos. They are recorded with a professional microphone ! 🙂
Views: 28700 Philip Starritt
How to Search for Datasets Using the Dataset Discovery Page
This video tutorial demonstrates step-by-step instructions on how to search for datasets using the Dataset Discovery Page on the Gulf of Mexico Research Initiative Information & Data Cooperative (GRIIDC) system. Users can search for datasets by location, institution, researcher, and/or a free text search. GRIIDC datasets are available for download to members of the public. For more information about GRIIDC please visit https://data.gulfresearchinitiative.org.
Views: 123 GRIIDC
Download Various Datasets for Hadoop from Websites | Easylearning.guru
In this dataset tutorial video, information to download datasets for analysis is provided. Some of the main points discussed in this video are: • What are datasets and how to download different dataset repository • Step by step procedure to download datasets for data mining • Websites from where you can download datasets • How to analyse downloaded dataset Subscribe to our YouTube channel to watch more interesting and informative videos. Email - [email protected] Phone call - 0124 - 4763660
Python for Data Science Tutorial | Missing Values - 1
Python for Data Science/ Data Mining/ Data Analysis . This is part 1 of handling the missing values in a dataset using Python and the python package "Pandas". This video shows how to count the number of missing values in a data set using the "isnull" and "sum" function.
Views: 714 i am biomed
How to Make Data Amazing - Intro to Deep Learning #5
In this video, we'll go through data preprocessing steps for 3 different datasets. We'll also go in depth on a dimensionality reduction technique called Principal Component Analysis. Coding challenge for this video: https://github.com/llSourcell/How_to_Make_Data_Amazing Charles-David's Winning Code: https://github.com/alkaya/earthquake-cotw Siby Jack Grove's Runner-up code: https://github.com/sibyjackgrove/Earthquake_predict/blob/master/earthquake_predict.ipynb Please subscribe. And like. And comment. That's what keeps me going. More Learning Resources: http://www.cs.ccsu.edu/~markov/ccsu_courses/datamining-3.html http://www.slideshare.net/jasonrodrigues/data-preprocessing-5609305 http://iasri.res.in/ebook/win_school_aa/notes/Data_Preprocessing.pdf http://staffwww.itn.liu.se/~aidvi/courses/06/dm/lectures/lec2.pdf http://ufldl.stanford.edu/wiki/index.php/Data_Preprocessing http://machinelearningmastery.com/how-to-prepare-data-for-machine-learning/ https://plot.ly/ipython-notebooks/principal-component-analysis/ Public datasets: https://github.com/caesar0301/awesome-public-datasets https://aws.amazon.com/public-datasets/ http://archive.ics.uci.edu/ml/index.html https://dreamtolearn.com/ryan/1001_datasets Join us in our 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 Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 49581 Siraj Raval
GEO DataSets
( http://www.abnova.com ) - The Gene Expression Omnibus (GEO) is a public repository that stores original submitter-supplied curated gene expression DataSets. This video shows you how to enter search terms to locate experiments of interest and interpret GEO DataSets results pages. More videos at Abnova http://www.abnova.com
Views: 9723 Abnova
Complete Data Science Course | What is Data Science? | Data Science for Beginners | Edureka
** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification ** This Edureka video on "Data Science" provides an end to end, detailed and comprehensive knowledge on Data Science. This Data Science video will start with basics of Statistics and Probability and then move to Machine Learning and Finally end the journey with Deep Learning and AI. For Data-sets and Codes discussed in this video, drop a comment. This video will be covering the following topics: 1:23 Evolution of Data 2:14 What is Data Science? 3:02 Data Science Careers 3:36 Who is a Data Analyst 4:20 Who is a Data Scientist 5:14 Who is a Machine Learning Engineer 5:44 Salary Trends 6:37 Road Map 9:06 Data Analyst Skills 10:41 Data Scientist Skills 11:47 ML Engineer Skills 12:53 Data Science Peripherals 13:17 What is Data ? 15:23 Variables & Research 17:28 Population & Sampling 20:18 Measures of Center 20:29 Measures of Spread 21:28 Skewness 21:52 Confusion Matrix 22:56 Probability 25:12 What is Machine Learning? 25:45 Features of Machine Learning 26:22 How Machine Learning works? 27:11 Applications of Machine Learning 34:57 Machine Learning Market Trends 36:05 Machine Learning Life Cycle 39:01 Important Python Libraries 40:56 Types of Machine Learning 41:07 Supervised Learning 42:27 Unsupervised Learning 43:27 Reinforcement Learning 46:27 Supervised Learning Algorithms 48:01 Linear Regression 58:12 What is Logistic Regression? 1:01:22 What is Decision Tree? 1:11:10 What is Random Forest? 1:18:48 What is Naïve Bayes? 1:30:51 Unsupervised Learning Algorithms 1:31:55 What is Clustering? 1:34:02 Types of Clustering 1:35:00 What is K-Means Clustering? 1:47:31 Market Basket Analysis 1:48:35 Association Rule Mining 1:51:22 Apriori Algorithm 2:00:46 Reinforcement Learning Algorithms 2:03:22 Reward Maximization 2:06:35 Markov Decision Process 2:08:50 Q-Learning 2:18:19 Relationship Between AI and ML and DL 2:20:10 Limitations of Machine Learning 2:21:19 What is Deep Learning ? 2:22:04 Applications of Deep Learning 2:23:35 How Neuron Works? 2:24:17 Perceptron 2:25:12 Waits and Bias 2:25:36 Activation Functions 2:29:56 Perceptron Example 2:31:48 What is TensorFlow? 2:37:05 Perceptron Problems 2:38:15 Deep Neural Network 2:39:35 Training Network Weights 2:41:04 MNIST Data set 2:41:19 Creating a Neural Network 2:50:30 Data Science Course Masters Program Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS Machine Learning Podcast: https://castbox.fm/channel/id1832236 Instagram: https://www.instagram.com/edureka_learning Slideshare: https://www.slideshare.net/EdurekaIN/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka #edureka #DataScienceEdureka #whatisdatascience #Datasciencetutorial #Datasciencecourse #datascience - - - - - - - - - - - - - - About the Master's Program This program follows a set structure with 6 core courses and 8 electives spread across 26 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands on expertise. By the end of the program you will be ready for seasoned Data Science job roles. - - - - - - - - - - - - - - Topics Covered in the curriculum: Topics covered but not limited to will be : Machine Learning, K-Means Clustering, Decision Trees, Data Mining, Python Libraries, Statistics, Scala, Spark Streaming, RDDs, MLlib, Spark SQL, Random Forest, Naïve Bayes, Time Series, Text Mining, Web Scraping, PySpark, Python Scripting, Neural Networks, Keras, TFlearn, SoftMax, Autoencoder, Restricted Boltzmann Machine, LOD Expressions, Tableau Desktop, Tableau Public, Data Visualization, Integration with R, Probability, Bayesian Inference, Regression Modelling etc. - - - - - - - - - - - - - - For more information, Please write back to us at [email protected] or call us at: IND: 9606058406 / US: 18338555775 (toll free)
Views: 30874 edureka!
O'Reilly Webcast: How We Build Data Mining Teams at Yelp
Starting and growing a data science team doesn't have to be a risky proposition. By balancing long term strategy and technology goals with immediate business demands, your data science team can quickly become productive and enjoy sustained growth. To accomplish this you need to: Find the right people Set business context Slowly expand scope Have a roadmap Share your secrets This webcast presented by Jim Blomo will include examples and tips, and you will hear stories from successfully growing data teams at Yelp using this advice. About Jim Blomo Jim Blomo (@jimblomo) is passionate about putting data to work by developing robust, elegant systems. At Yelp, he manages a growing data mining team that uses Hadoop, mrjob, and oddjob to process TBs of data. Before Yelp, he built infrastructure for startups and Amazon. Jim also lectures at UC Berkeley's School of information on Data Mining and Web Architecture and has presented at conferences such as AWS re:Invent and Wolfram Alpha Data Summit. Produced by: Yasmina Greco
Views: 1524 O'Reilly
Applications of Predictive Analytics in Legal | Litigation Analytics, Data Mining & AI | Great Lakes
#PredictiveAnalytics | Learn the prediction of outcome or treatment of a case by legal courts of Appeals based on historical data using predictive analytics. Watch the video to understand analytics in legal using case study on real-life data set. How litigation analytics can flourish with the use of data mining and AI. Know more about our analytics Program: PGP- Business Analytics: https://goo.gl/V9RzVD PGP- Big Data Analytics: https://goo.gl/rRyjj4 Business Analytics Certification Program: https://goo.gl/7HPoUY #LegalTech #LegalAnalytics #GreatLearning #GreatLakes About Great Learning: - Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. - Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM - What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U - Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670 - Want to know more about the careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU - For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1 - Learn More at: https://www.greatlearning.in/ For more updates on courses and tips follow us on: - Google Plus: https://plus.google.com/u/0/108438615307549697541 - Facebook: https://www.facebook.com/GreatLearningOfficial/ - LinkedIn: https://www.linkedin.com/company/great-learning/ - Follow our Blog: https://www.greatlearning.in/blog/?utm_source=Youtube
Views: 1034 Great Learning
The Library as Dataset: Text Mining at Million-Book Scale
What do you do with a library? The large-scale digital collections scanned by Google and the Internet Archive have opened new ways to interact with books. The scale of digitization, however, also presents a challenge. We must find methods that are powerful enough to model the complexity of culture, but simple enough to scale to millions of books. In this talk I'll discuss one method, statistical topic modeling. I'll begin with an overview of the method. I will then demonstrate how to use such a model to measure changes over time and distinctions between sub-corpora. Finally, I will describe hypothesis tests that help us to distinguish consistent patterns from random variations. David Mimno is a postdoctoral researcher in the Computer Science department at Princeton University. He received his PhD from the University of Massachusetts, Amherst. Before graduate school, he served as Head Programmer at the Perseus Project, a digital library for cultural heritage materials, at Tufts University. He is supported by a CRA Computing Innovation fellowship.
Views: 2361 YaleUniversity
How to Make a Data Science Project with Kaggle (AI Adventures)
It can take a lot of tools to do data science, but Kaggle is a one-stop shop that provides all the tools to share and collaborate on data science projects. In the episode of AI Adventures, Yufeng is joined by Megan Risdal, product lead for datasets at Kaggle. They’ll teach you how to make a data science project with Kaggle, and more! Associated blog post → http://bit.ly/2u18Tyh Get started with Kaggle → https://kaggle.com/datasets Introduction to Kaggle Kernels → http://bit.ly/2z409xm [Dataset] LA County Health Code Violations → http://bit.ly/2MFwyvO [Kernel] Exploring LA County Health Code Violations → http://bit.ly/2KIBz6e Watch more AI Adventures → http://bit.ly/AIAdventures Subscribe to the Google Cloud Platform channel → http://bit.ly/GCloudPlatform
Views: 45061 Google Cloud Platform
Introduction to Big Data and the Data Lifecycle
Dr. Mark Musen from Stanford University presents "Introduction to Big Data and the Data Life Cycle" Lecture Description Data are created, they persist for a period of time, and they may be lost or destroyed. With luck, they may be reused and re-explored to yield new insights and to spark new investigations. This talk will highlight major themes in the management and use of scientific data, and the ways in which investigators can ensure that their data will have maximum benefit to the scientific community. The talk also will provide background for future presentations in this series. View slides from this lecture: https://drive.google.com/open?id=0B4IAKVDZz_JUaVp5cG5rcUszTzg About Our Speaker Dr. Musen is Professor of Biomedical Informatics at Stanford University, where he is Director of the Stanford Center for Biomedical Informatics Research. Dr. Musen conducts research related to intelligent systems, reusable ontologies, metadata for publication of scientific data sets, and biomedical decision support. Please join our weekly meetings from your computer, tablet or smartphone. Visit our website to learn how to join! http://www.bigdatau.org/data-science-seminars
yelp dataset challenge
Multilabel classification of reviews into relevant categories
Views: 3600 vaibhav saini
How to Start an AI Startup
How are you supposed to get in on the AI hype? Deep learning has enabled a whole new breed of applications, and there are still so many different opportunities to apply it in fields that are completely untapped. I'll go through the steps you need to take to start your own AI startup using a combination of my own experiences and best practices from the industry as a guide. From data collection to model training to picking a problem, we'll try to understand this challenging task. Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval Sources: https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A/playlists https://www.deeplearning.ai/ http://www.fast.ai/ http://www.deeplearningbook.org/ https://www.kaggle.com/datasets https://github.com/awesomedata/awesome-public-datasets https://archive.ics.uci.edu/ml/datasets.html More learning resources: https://www.youtube.com/watch?v=CBYhVcO4WgI https://www.youtube.com/watch?v=bNpx7gpSqbY https://www.youtube.com/watch?v=JqxzLUE6pP8 https://www.youtube.com/watch?v=ii1jcLg-eIQ https://www.youtube.com/watch?v=ia8arCDoxZ8 https://www.youtube.com/watch?v=677ZtSMr4-4 Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 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: 262281 Siraj Raval
Kaggle Live-Coding: Making text sound old-timey with transformers (Python)! | Kaggle
Join Kaggle data scientist Rachael live as she works on data science projects! This week we're going to start on a project to make current English sound a little more like early Modern English (think Shakespeare) with transformers. 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 Kaggle Live-Coding: Making text sound old-timey with transformers (Python)! | Kaggle https://www.youtube.com/watch?v=VLUmmI_K1Uw Kaggle http://www.youtube.com/user/kaggledotcom
Views: 1084 Kaggle
VC-Dimension and Rademacher Averages - Part 1
Author: Matteo Riondato, Eli Upfal Abstract: Rademacher Averages and the Vapnik-Chervonenkis dimension are fundamental concepts from statistical learning theory. They allow to study simultaneous deviation bounds of empirical averages from their expectations for classes of functions, by considering properties of the functions, of their domain (the dataset), and of the sampling process. In this tutorial, we survey the use of Rademacher Averages and the VC-dimension in sampling-based algorithms for graph analysis and pattern mining. We start from their theoretical foundations at the core of machine learning, then show a generic recipe for formulating data mining problems in a way that allows to use these concepts in efficient randomized algorithms for those problems. Finally, we show examples of the application of the recipe to graph problems (connectivity, shortest paths, betweenness centrality) and pattern mining. Our goal is to expose the usefulness of these techniques for the data mining researcher, and to encourage research in the area. ACM DL: http://dl.acm.org/citation.cfm?id=2789984 DOI: http://dx.doi.org/10.1145/2783258.2789984
Ramachandran Outliers: Data Mining and Analysis using the Python Language
David Vavrinak '18 delivers his presentation titled. "Ramachandran Outliers: Data Mining and Analysis using the Python Language" at Wabash College's 18th Annual Celebration of Student Research, Scholarship, and Creative Work.
Views: 79 Rob Shook
#254 Allen Day: Google's Mission to Provide Open Datasets for Public Blockchains
Support the show, consider donating: BTC: 1CD83r9EzFinDNWwmRW4ssgCbhsM5bxXwg (https://epicenter.tv/tipbtc) BCC: 1M4dvWxjL5N9WniNtatKtxW7RcGV73TQTd (http://epicenter.tv/tipbch) ETH: 0x8cdb49ca5103Ce06717C4daBBFD4857183f50935 (https://epicenter.tv/tipeth) Public blockchains produce enormous amounts of data. In theory, anyone can access the raw contents of transaction and blocks. In practice, however, querying blockchains can prove to be a daunting task. The difficulty lies in the fact that blockchains are particular types of distributed databases and thus carry several limitations. Most, if not all, blockchains lack the most basic SQL querying capabilities supported by nearly every off-the-shelf database system. Take Bitcoin as an example. Its API lacks even the most basic calls which would allow a user to query any address and receive the balance. In order to achieve this, block explorers and alike have developed sophisticated middleware infrastructure that parses the blockchain, normalizes the data, and stores it in a database, where it can be queried. In the best of cases, companies offer API calls for only a limited set of operations. Google hopes to change this by freeing blockchain datasets. We're joined by Allen Day, Science Advocate at Google's Singapore office. Earlier this year, he and his team released the Bitcoin blockchain as a public dataset in Big Query, Google big data IaaS offering. In August, they added Ethereum to their list of freely available public datasets, which includes US census data, cannabis genomes, and the entirety of Reddit and Github. Anyone wishing to query the data can do so in SQL on the Big Query website or via an API. For instance, a relatively simple query would return the daily mean transaction fees since the Genesis Block in just a few seconds. Coupled with Google's AI and Machine Learning infrastructure and other open data sets, one can only imagine the potentially groundbreaking insights we could gain from this data. Topics discussed in this episode: - Allen's background as a geneticist - The similarities between blockchains and evolution process in lifeforms - Google's cloud platform and its various components - Big Query and its publicly available datasets - The Bitcoin and Ethereum datasets in Big Query - Why this data is useful to the public and for what it may be used - The particular challenges in implementing Ethereum as opposed to Bitcoin - Insights we may gain by crossing blockchain dataset with other data - How machine learning and AI could help us better understand specific transaction patterns Links mentioned in this episode: - Bitcoin in BigQuery: blockchain analytics on public data: https://cloud.google.com/blog/products/gcp/bitcoin-in-bigquery-blockchain-analytics-on-public-data - Bitcoin Blockchain Public Dataset: https://bigquery.cloud.google.com/dataset/bigquery-public-data:bitcoin_blockchain - Ethereum in BigQuery: a Public Dataset for smart contract analytics: https://cloud.google.com/blog/products/data-analytics/ethereum-bigquery-public-dataset-smart-contract-analytics - Ethereum in BigQuery: how we built this dataset: https://cloud.google.com/blog/products/data-analytics/ethereum-bigquery-how-we-built-dataset - Ethereum Blockchain Public Dataset: https://bigquery.cloud.google.com/dataset/bigquery-public-data:ethereum_blockchain - Change Agent by Daniel Suarez: https://www.goodreads.com/book/show/31396262-change-agent - Real-time Ethereum Notifications for Everyone for Free: https://medium.com/google-cloud/real-time-ethereum-notifications-for-everyone-for-free-a76e72e45026 - ethjs-abi library, compiled for use in Google BigQuery: https://github.com/Arachnid/ethjs-abi-bigquery - Kaggle: Your Home for Data Science: https://www.kaggle.com/ - The Strange Inevitability of Evolution - Issue 20: Creativity - Nautilus: http://nautil.us/issue/20/creativity/the-strange-inevitability-of-evolution - Google Cloud: https://cloud.google.com/ Sponsors: - DutchX: The open, decentralized trading protocol for ERC20 tokens using the Dutch auction mechanism - https://epicenter.tv/dutchx - Azure: Deploy enterprise-ready consortium blockchain networks that scale in just a few clicks - http://aka.ms/epicenter This episode is also available on : - Epicenter.tv: https://epicenter.tv/254 - YouTube: http://youtu.be/KEnPTtemons - Souncloud: http://soundcloud.com/epicenterbitcoin/eb-254 Watch or listen, Epicenter is available wherever you get your podcasts. Epicenter is hosted by Brian Fabian Crain, Sébastien Couture, Meher Roy & Sunny Aggarwal.
Views: 897 Epicenter Podcast
Weka Tutorial 02: Data Preprocessing 101 (Data Preprocessing)
This tutorial demonstrates various preprocessing options in Weka. However, details about data preprocessing will be covered in the upcoming tutorials.
Views: 171220 Rushdi Shams
Finding & Accessing Datasets, Indexing & Identifiers
Dr. Lucila Ohno-Machado from the University of California San Diego, presents a lecture on "Finding and Accessing Datasets, Indexing & Identifiers. To view slides from this presentation, please visit: https://drive.google.com/open?id=0B4IAKVDZz_JUNlNtZDZPa1pWbDA To learn how to attend our next live webinar lecture, please visit our webpage at: http://www.bigdatau.org/data-science-seminars About the Presenter: Lucila Ohno-Machado, MD, MBA, PhD is a Professor of Medicine and founding chief of the Division of Biomedical Informatics at UCSD. She is associate dean for informatics and technology and has experience leading multidisciplinary projects at the intersections of biomedicine and quantitative sciences. Her research group focuses on biomedical pattern recognition from large data sets, statistical learning, and privacy technology.
TICTeC 2018 - The personal and the public: protecting sensitive data in huge datasets
Felipe Hoffa (Google) When releasing a public dataset, practitioners need to walk a fine line between utility and the protection of individuals. Felipe moves us from theory to the real life practicalities of handling massive public datasets, showcasing newly available cloud-based tools that help with the detection of personal information, and bringing concepts like k-anonymity and l-diversity into context. See the Q&A for this session here: https://youtu.be/SA5C5oiqf7c To find out more about TICTeC please visit: http://tictec.mysociety.org
Views: 27 mySociety
Data Mining Clintons Emails
On Wednesday, July 20, 2016 Columbia Entrepreneurship and Text.IQ Hosted Columbia University History Professor Matt Connolly at the Columbia Startup Lab for a talk on Applying New Tools to an Old Domain. Whether official secrecy is random or predictable is a matter of great public controversy. But the question has not been explored using Natural Language Processing and Machine Learning methods. We report the results of an experiment with nearly one million State Department cables from the 1970s to identify diplomatic communications that were originally classified as containing sensitive national security information.
Student's t-test
Excel file: https://dl.dropboxusercontent.com/u/561402/TTEST.xls In this video Paul Andersen explains how to run the student's t-test on a set of data. He starts by explaining conceptually how a t-value can be used to determine the statistical difference between two samples. He then shows you how to use a t-test to test the null hypothesis. He finally gives you a separate data set that can be used to practice running the test. Do you speak another language? Help me translate my videos: http://www.bozemanscience.com/translations/ Music Attribution Intro Title: I4dsong_loop_main.wav Artist: CosmicD Link to sound: http://www.freesound.org/people/CosmicD/sounds/72556/ Creative Commons Atribution License Outro Title: String Theory Artist: Herman Jolly http://sunsetvalley.bandcamp.com/track/string-theory All of the images are licensed under creative commons and public domain licensing: Critical Values of the Student’s-t Distribution. (n.d.). Retrieved April 12, 2016, from http://www.itl.nist.gov/div898/handbook/eda/section3/eda3672.htm File:Hordeum-barley.jpg - Wikimedia Commons. (n.d.). Retrieved April 11, 2016, from https://commons.wikimedia.org/wiki/File:Hordeum-barley.jpg Keinänen, S. (2005). English: Guinness for strenght. Retrieved from https://commons.wikimedia.org/wiki/File:Guinness.jpg Kirton, L. (2007). English: Footpath through barley field. A well defined and well used footpath through the fields at Nuthall. Retrieved from https://commons.wikimedia.org/wiki/File:Footpath_through_barley_field_-_geograph.org.uk_-_451384.jpg pl.wikipedia, U. W. on. ([object HTMLTableCellElement]). English: William Sealy Gosset, known as “Student”, British statistician. Picture taken in 1908. Retrieved from https://commons.wikimedia.org/wiki/File:William_Sealy_Gosset.jpg The T-Test. (n.d.). Retrieved April 12, 2016, from http://www.socialresearchmethods.net/kb/stat_t.php
Views: 519507 Bozeman Science
Predicting Instructor Performance Using Data Mining Techniques in Higher Education
Predicting Instructor Performance Using Data Mining Techniques in Higher Education -- Data mining applications are becoming a more common tool in understanding and solving educational and administrative problems in higher education. In general, research in educational mining focuses on modeling student's performance instead of instructors' performance. One of the common tools to evaluate instructors' performance is the course evaluation questionnaire to evaluate based on students' perception. In this paper, four different classication techniquesdecision tree algorithms, support vector machines, articial neural networks, and discriminant analysisare used to build classier models. Their performances are compared over a data set composed of responses of students to a real course evaluation questionnaire using accuracy, precision, recall, and specicity performance metrics. Although all the classier models show comparably high classication performances, C5.0 classier is the best with respect to accuracy, precision, and specicity. In addition, an analysis of the variable importance for each classier model is done. Accordingly, it is shown that many of the questions in the course evaluation questionnaire appear to be irrelevant. Furthermore, the analysis shows that the instructors' success based on the students' perception mainly depends on the interest of the students in the course. The ndings of this paper indicate the effectiveness and expressiveness of data mining models in course evaluation and higher education mining. Moreover, these ndings may be used to improve the measurement instruments. Articial neural networks, classication algorithms, decision trees, linear discriminant analysis, performance evaluation, support vector machines. -- For More Details Contact Us -- S.Venkatesan Arihant Techno Solutions Pudukkottai www.arihants.com Mobile: +91 75984 92789
How to create a dataset on data world
How-to video for creating datasets on data.world
Views: 2775 datadotworld
[Data on the Mind 2017] Working with heterogenous datasets: a real-world example
Abstract: The analysis of real-world data often involves acquiring, cleaning, and merging heterogeneous datasets from disparate sources. Often times, our behavioral questions require combining datasets that are in different formats, timescales, geographic levels of granularity, and even different dimensionality (e.g., spatial versus temporal data). Using a series of worked examples from my ongoing research examining the risk-taking behavior of New York City residents, this tutorial outlines common challenges and approaches for dealing with these heterogeneous datasets, as well as explores some public data sources which are can be critically useful for elucidating real-world behavioral questions. This tutorial will assume some familiarity with Python, ‘pandas’ data structures, and basic web queries. Instructor: Ross Otto (McGill University) --- Part of the Data on the Mind 2017 summer workshop: http://www.dataonthemind.org/2017-workshop Funded by the Estes Fund: http://www.psychonomic.org/page/estesfund Organized in collaboration with Data on the Mind: http://www.dataonthemind.org Videography by DeNoise Studios: http://www.denoise.com Workshop hashtag: #dataonthemind
iPython Pandas Data Analytics - World Bank Datasets
How do we know whether a country is having a major disaster, such as war, genocide, or natural disaster? This tutorial will guide us to use iPython to do a basic analysis in finding those relevant trends. iPyhon Notebook Source: http://goo.gl/1G8ZRZ
Views: 939 erwin huang
Opening Up Astronomy with Python and AstroML; SciPy 2013 Presentation
Authors: Vanderplas, Jake, University of Washington; Ivezic, Zeljko, University of Washington; Connolly, Andrew, University of Washington Track: General As astronomical data sets grow in size and complexity, automated machine learning and data mining methods are becoming an increasingly fundamental component of research in the field. The astroML project (http://astroML.github.com), first released in fall 2012, provides a common repository for practical examples of the data mining and machine learning tools used and developed by astronomical researchers, written in python. The astroML module offers a host of general data analysis and machine learning routines, loaders for openly-available astronomical datasets, and fast implementations of specific computational methods often used in astronomy and astrophysics. The associated website features hundreds of examples of these routines in action, using real datasets. In this talk I'll go over some of the highlights of the astroML code and examples, and discuss how we've used astroML as an aid for student research, hands-on graduate astronomy curriculum, and the sharing of research tools and results.
Views: 3031 Enthought
The World Factbook - Explore the World with Free Open Public Domain Datasets
Free Open Public Domain Data :: 250+ Country Profiles (Incl. Flags 'n' Maps) Vortragender: Gerald Bauer Learn about the World Factbook. Shows how to make your own world almanac using the factbook.json datasets in twenty line of scripts. How to query the datasets in SQL using the single-file factbook.db SQLite Database or using big data gold mining with NoSQL queries in document collections with MongoDB (e.g. db.factbook.find( { "Geography.Natural resources.text": /gold/} )) and more. Yes, we will find gold and diamonds. Trivia Question: No. #1 country in the world with the largest proven crude oil reserves? Anyone?
Views: 28 mi eb
Sharing Your Scraping Results in the Datasets Catalog
Scrapinghub's Datasets Catalog (https://app.scrapinghub.com/datasets) allows you to share the results of your Scrapinghub projects as publicly searchable datasets. In this video, we show how to create and share a public dataset from the results of your Scrapy spiders running on Scrapinghub.
Views: 1134 Scrapinghub
From Data to Knowledge - 102 - Eamonn Keogh
Eamonn Keogh: "A Trillion here, a Trillion there: Scaling Time Series Data Mining to a Trillion Time Series" A video from the UC Berkeley Conference: From Data to Knowledge: Machine-Learning with Real-time and Streaming Applications (May 7-11, 2012). Abstract Eamonn Keogh (Computer Science and Engineering Dept., University of California, Riverside) In this talk I will argue the following claims. 1) Similarity search is the fundamental operation for mining time series data, and virtually any task, classification, clustering, rule finding, anomaly detection etc., can be efficiently and effectively solved once the similarity search problem is solved. 2) While there are dozens of alternative distance measures for similarity search, a 50-year old idea, Dynamic Time Warping (DTW) is exceptionally hard to beat. 3) DTWs often touted lethargy is no more. With four simple new ideas, we can exactly search billions of time series in a minute under DTW, using off-the-shelf computers. I will illustrate this talk with some experiments on datasets that are larger than the combined size of all of the time series datasets considered in all data mining papers ever published.
Views: 2874 ckleinastro
Market Basket Association and Association Rules
This video briefly presents the concepts of Association Rules and Market Basket Analysis. Important issues are illustrated with suitable examples. Discussions have also been made on how to use R commands to mine association rules from a large transaction data set.
Views: 154 Jaydip Sen

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