Home
Search results “Data mining tools python wrapped”
web scraping using python for beginners
 
11:26
Learn Python here: https://courses.learncodeonline.in/learn/Python3-course In this video, we will talk about basics of web scraping using python. This is a video for total beginners, please comment if you want more videos on web scraping fb: https://www.facebook.com/HiteshChoudharyPage homepage: http://www.hiteshChoudhary.com Download LearnCodeOnline.in app from Google play store and Apple App store
Views: 165618 Hitesh Choudhary
Machine Learning - Dimensionality Reduction - Feature Extraction & Selection
 
05:31
Enroll in the course for free at: https://bigdatauniversity.com/courses/machine-learning-with-python/ Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning. This #MachineLearning with #Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each. Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed! Explore many algorithms and models: Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction. Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests. Get ready to do more learning than your machine! Connect with Big Data University: https://www.facebook.com/bigdatauniversity https://twitter.com/bigdatau https://www.linkedin.com/groups/4060416/profile ABOUT THIS COURSE •This course is free. •It is self-paced. •It can be taken at any time. •It can be audited as many times as you wish. https://bigdatauniversity.com/courses/machine-learning-with-python/
Views: 22507 Cognitive Class
Weka Data Mining Tutorial for First Time & Beginner Users
 
23:09
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: 456842 Brandon Weinberg
Malware Analysis - Detecting if a File is Packed
 
14:45
How to spot the difference of packed vs unpacked? PortexAnalyzer: https://github.com/katjahahn/PortEx/tree/master/progs PEStudio: https://winitor.com/
Feature Selection
 
20:35
Subset selection Forward Stepwise Backward Stepwise Variable Selection Shrinkage Method Dimension Reduction Tuning Parameter Principle Component Analysis
Views: 4765 Sunil Bhatia
Weka Tutorial 09: Feature Selection with Wrapper (Data Dimensionality)
 
11:03
This tutorial shows you how you can use Weka Explorer to select the features from your feature vector for classification task (Wrapper method)
Views: 67588 Rushdi Shams
Feature Selection in Machine Learning
 
03:05
Explore feature selection on Quantopian: https://www.quantopian.com/lectures/multiple-linear-regression. Special thanks to Cheng Peng from the Quantopian Community for suggesting this video. Feel free to leave suggestions for any content that you would like to see at https://www.quantopian.com/posts/what-type-of-content-would-you-like-to-see. In this short video, Max Margenot gives an overview of selecting features for your model. He goes over the process of adding parameters to your model while avoiding overfitting. He also discusses general tools for evaluating the quality of different features, many contained in packages like scikit-learn, and covers methods of testing the various features in your algorithm. Learn more by subscribing to our Quantopian Channel to access all of our videos. As always, if there are any topics you would like us to focus on for future videos, please send us a quick note at [email protected] Disclaimer Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice. More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian. In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Views: 2943 Quantopian
Feature Selection - Forward, Backward, Stepwise & Genetic Algorithm
 
08:28
Links GitHub: https://github.com/jk6653284/python_feature_and_model MatLab: https://www.youtube.com/watch?v=1i8muvzZkPw
Views: 880 Louis Rampignon
Webinar: Overview of KNIME Software
 
47:06
Are you curious about KNIME Software? Do you know the difference between KNIME Analytics Platform and KNIME Server? Which data sources can KNIME connect to? Can you run an R script from within a KNIME workflow? A Python script? Which other integrations are available? How can KNIME help with ETL, data preparation, and general data manipulation? Which machine learning algorithms can KNIME offer? This webinar answers all of these questions! There’s also information about connecting to big data clusters and how you can run the whole or part of your analysis on a big data platform. It also covers everything you need to know about Microsoft Azure or Amazon AWS. A copy of the slides is available at: https://www.slideshare.net/KNIMESlides/knime-software-overview
Views: 4683 KNIMETV
Variable/feature Selection | Stepwise, Subset, Forward & Backward selection| Machine Learning
 
28:07
Variable selection or Feature selection is a technique using which we select the best set of features for a given machine learning model. The same can be used in deep learning models as well. In this video we have discussed about stepwise selection, subset selection, forward selection, backward selection & Shrinkage methords We have also discussed about how dimension reduction technique such as principal component analysis is different from feature selection techniques ANalytics Study Pack : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 2075 Analytics University
Drag & Drop Data Science
 
05:57
Build your own data science application step-by-step using the intuitive and visual user interface of KNIME Analytics Platform. KNIME Analytics Platform is a free, open source data analytics software which can be downloaded at https://www.knime.com/downloads
Views: 2454 KNIMETV
Serving Scikit-learn Models at Scale
 
04:13
Scikit-learn is a great tool for building your models. When it comes time to deploy them to prediction, scale up using Google Cloud ML Engine. In this episode of AI Adventures, Yufeng shows you how to set up your own deployment pipeline with scikit-learn so you can go back to focusing on tuning your model! Associated blog post → http://bit.ly/2Mg70Wi Kaggle kernel → http://bit.ly/2v31tMh Cloud ML Engine with scikit-learn quickstart → http://bit.ly/2Mdtszp Watch more AI Adventures → http://bit.ly/AIAdventures Subscribe to the Google Cloud Platform channel → http://bit.ly/GCloudPlatform
Views: 4815 Google Cloud Platform
Python Integration in KNIME
 
07:18
New Python Integration in KNIME Analytics Platform 2.11, based on CPython rather than JPython. This Pytrhon integration requires some Python modules: - PANDAS for data representation - Protobuf for the communication between CPython and KNIME - optionally Jedi for the auto-completion feature in the Python editor in the nodes configuration window This video is part of the recording of the "What is new in KNIME 2.11" webinar held on Dec 11 2014 and available on Youtube at: http://youtu.be/9RkRHI32Dy8 For more infos about updates in KNIME 2.11 check http://tech.knime.org/whats-new-in-knime-211
Views: 5385 KNIMETV
Feature Engineering in SAS Visual Data Mining & Machine Learning
 
13:10
http://support.sas.com/software/products/visual-data-mining-machine-learning/index.html Presenter: Radhikha Myneni Radhikha Myneni discusses some feature engineering techniques available in SAS Visual Data Mining and Machine Learning 8.3. SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 900 SAS Software
Regression forecasting and predicting - Practical Machine Learning Tutorial with Python p.5
 
14:28
In this video, make sure you define the X's like so. I flipped the last two lines by mistake: X = np.array(df.drop(['label'],1)) X = preprocessing.scale(X) X_lately = X[-forecast_out:] X = X[:-forecast_out:] To forecast out, we need some data. We decided that we're forecasting out 10% of the data, thus we will want to, or at least *can* generate forecasts for each of the final 10% of the dataset. So when can we do this? When would we identify that data? We could call it now, but consider the data we're trying to forecast is not scaled like the training data was. Okay, so then what? Do we just do preprocessing.scale() against the last 10%? The scale method scales based on all of the known data that is fed into it. Ideally, you would scale both the training, testing, AND forecast/predicting data all together. Is this always possible or reasonable? No. If you can do it, you should, however. In our case, right now, we can do it. Our data is small enough and the processing time is low enough, so we'll preprocess and scale the data all at once. In many cases, you wont be able to do this. Imagine if you were using gigabytes of data to train a classifier. It may take days to train your classifier, you wouldn't want to be doing this every...single...time you wanted to make a prediction. Thus, you may need to either NOT scale anything, or you may scale the data separately. As usual, you will want to test both options and see which is best in your specific case. With that in mind, let's handle all of the rows from the definition of X onward. https://pythonprogramming.net/forecasting-predicting-machine-learning-tutorial/ https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 210830 sentdex
Extract data with pagination: Click on "Next" button to scrape (Octoparse 7.X)
 
03:38
If the web page we want to extract data has a next-page button, it's very easy to create a pagination in Octoparse 7 by clicking on the "Next" button. This video will show you how exactly it is done. For graphic tutorial, please visit: https://www.octoparse.com/tutorial-7/extract-multiple-pages-through-pagination/ To deal with the websites with page number link only (no "Next" button), please check another video: https://youtu.be/57xR_TEuxys
Views: 2290 Octoparse
R tutorial: Cleaning and preprocessing text
 
03:14
Learn more about text mining with R: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words Now that you have a corpus, you have to take it from the unorganized raw state and start to clean it up. We will focus on some common preprocessing functions. But before we actually apply them to the corpus, let’s learn what each one does because you don’t always apply the same ones for all your analyses. Base R has a function tolower. It makes all the characters in a string lowercase. This is helpful for term aggregation but can be harmful if you are trying to identify proper nouns like cities. The removePunctuation function...well it removes punctuation. This can be especially helpful in social media but can be harmful if you are trying to find emoticons made of punctuation marks like a smiley face. Depending on your analysis you may want to remove numbers. Obviously don’t do this if you are trying to text mine quantities or currency amounts but removeNumbers may be useful sometimes. The stripWhitespace function is also very useful. Sometimes text has extra tabbed whitespace or extra lines. This simply removes it. A very important function from tm is removeWords. You can probably guess that a lot of words like "the" and "of" are not very interesting, so may need to be removed. All of these transformations are applied to the corpus using the tm_map function. This text mining function is an interface to transform your corpus through a mapping to the corpus content. You see here the tm_map takes a corpus, then one of the preprocessing functions like removeNumbers or removePunctuation to transform the corpus. If the transforming function is not from the tm library it has to be wrapped in the content_transformer function. Doing this tells tm_map to import the function and use it on the content of the corpus. The stemDocument function uses an algorithm to segment words to their base. In this example, you can see "complicatedly", "complicated" and "complication" all get stemmed to "complic". This definitely helps aggregate terms. The problem is that you are often left with tokens that are not words! So you have to take an additional step to complete the base tokens. The stemCompletion function takes as arguments the stemmed words and a dictionary of complete words. In this example, the dictionary is only "complicate", but you can see how all three words were unified to "complicate". You can even use a corpus as your completion dictionary as shown here. There is another whole group of preprocessing functions from the qdap package which can complement these nicely. In the exercises, you will have the opportunity to work with both tm and qdap preprocessing functions, then apply them to a corpus.
Views: 19612 DataCamp
Scrape Websites with Python + Beautiful Soup 4 + Requests -- Coding with Python
 
34:35
Coding with Python -- Scrape Websites with Python + Beautiful Soup + Python Requests Scraping websites for data is often a great way to do research on any given idea. This tutorial takes you through the steps of using the Python libraries Beautiful Soup 4 (http://www.crummy.com/software/BeautifulSoup/bs4/doc/#) and Python Requests (http://docs.python-requests.org/en/latest/). Reference code available under "Actions" here: https://codingforentrepreneurs.com/projects/coding-python/scrape-beautiful-soup/ Coding for Python is a series of videos designed to help you better understand how to use python. Assumes basic knowledge of python. View all my videos: http://bit.ly/1a4Ienh Join our Newsletter: http://eepurl.com/NmMcr A few ways to learn Django, Python, Jquery, and more: Coding For Entrepreneurs: https://codingforentrepreneurs.com (includes free projects and free setup guides. All premium content is just $25/mo). Includes implementing Twitter Bootstrap 3, Stripe.com, django, south, pip, django registration, virtual environments, deployment, basic jquery, ajax, and much more. On Udemy: Bestselling Udemy Coding for Entrepreneurs Course: https://www.udemy.com/coding-for-entrepreneurs/?couponCode=youtubecfe49 (reg $99, this link $49) MatchMaker and Geolocator Course: https://www.udemy.com/coding-for-entrepreneurs-matchmaker-geolocator/?couponCode=youtubecfe39 (advanced course, reg $75, this link: $39) Marketplace & Dail Deals Course: https://www.udemy.com/coding-for-entrepreneurs-marketplace-daily-deals/?couponCode=youtubecfe39 (advanced course, reg $75, this link: $39) Free Udemy Course (80k+ students): https://www.udemy.com/coding-for-entrepreneurs-basic/ Fun Fact! This Course was Funded on Kickstarter: http://www.kickstarter.com/projects/jmitchel3/coding-for-entrepreneurs
Views: 411941 CodingEntrepreneurs
Advanced Aggregations with the GroupBy Node
 
03:34
This video describes some advanced aggregations available with the GroupBy node of the KNIME Analytics Platform: grouping on multiple features, aggregating on multiple features with the pattern and type based aggregations, aggregating the whole data set with no grouping, generating the list of group names with no aggregations. Refer to the video "What's data aggregation" to know more about data aggregation operations https://youtu.be/bDwF-TOMtWw Refer to the video "Basic Aggregations with the GroupBy Node" to know more about more basic aggregation methods https://youtu.be/JQ-OWMt48ew
Views: 2187 KNIMETV
SAS Visual Data Mining and Machine Learning
 
08:40
http://www.sas.com/vdmml Boost analytical productivity and solve your most complex problems faster with a single, integrated in-memory environment that's both open and scalable. SAS VISUAL DATA MINING AND MACHINE LEARNING SAS Visual Data Mining and Machine Learning supports the end-to-end data mining and machine-learning process with a comprehensive, visual (and programming) interface that handles all tasks in the analytical life cycle. It suits a variety of users and there is no application switching. From data management to model development and deployment, everyone works in the same, integrated environment. http://www.sas.com/vdmml SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 5163 SAS Software
StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data
 
05:16
This is just a short follow up to last week's StatQuest where we introduced decision trees. Here we show how decision trees deal with variables that don't improve the tree (feature selection) and how they deal with missing data. For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/
Developer Data Scientist – New Analytics Driven Apps Using Azure Databricks & Apache Spark | B116
 
43:49
This session gives an introduction to machine learning for developers who are new to data science, and it shows how to build end-to-end MLlib Pipelines in Apache Spark. It provides example code to personalize recommendations, score inbound leads, or do natural language processing in Scala and Python. See how to productionize machine learning pipelines to create richer, more useful applications.
Weka Tutorial 10: Feature Selection with Filter (Data Dimensionality)
 
11:09
This tutorial shows how to select features from a set of features that performs best with a classification algorithm using filter method.
Views: 67627 Rushdi Shams
ETL with KNIME. Advanced Row Filtering
 
06:41
As powerful as the Row Filter node is, sometimes it is not enough. For all those special cases, where an advanced row filtering strategy is required, other more powerful nodes can be used for row filtering in KNIME. A review of such nodes is proposed in this video. If you want to know more: - What is Row Filtering?https://youtu.be/NJwWwpIEBpg - The Row Filter node (4 parts): 1. What is Row Filtering? https://youtu.be/NJwWwpIEBpg 2. (Row Filter based on Pattern Matching https://youtu.be/j3YhdEgu0Z0 3. Row Filter based on a numerical interval or Missing Values https://youtu.be/rBmGjMu9EG4 4. Row Filter based on Row ID https://youtu.be/nomaYlGJwmA 5. "Advanced Row Filter" https://youtu.be/WcpEIzzZ-yc 6. Advanced Row Filter for Special Data Types. https://youtu.be/miUZNhePBLg - What is Column Filtering? https://youtu.be/wQE_cXwDH-I
Views: 5945 KNIMETV
AWS Windows Instance Set Up Step 4: Installing Open Source Software for Data Science
 
29:51
This video (revised 8-13-2015) is part 4 in a series of 5 videos that show how to set up a Windows virtual machine (instance) using Amazon Web Services and then provision it with Python and R (and some additional software) so that it can serve as a platform for doing some data science. In this video I show how to get an obtain open source software and install it on the Windows instance. The primary software I am installing is Python 2.7 and the statistics software R with the intention of using this platform to do some data science. The software installed in this video is Firefox, ClamWin anti virus software, Clam Sentinel real-time virus scanning software based on ClamWin, Anaconda Python, the R statistics package, and the cygwin unix-like tools for Windows. NOTE: It is very important to turn off Clam Sentinel while installing the rest of the software (especially Anaconda and cygwin). A web page with the links for provisioning the Windows instance can be found at: http://datasciencesource.com/WindowsInstanceProvisioning
Views: 5685 ProfessorEaston
PyCon.DE 2017 Ami Tavory - Getting Scikit-Learn To Run On Top Of Pandas
 
28:59
Ami Tavory Ami is a data scientist at Facebook Research's Core Data Science group. He previously worked as a machine learning researcher in the fields of bioinformatics and algorithmic trading. In 2010 he received a Ph.D in Electrical Engineering from Tel Aviv University, in the field of financial information theory. His bachelor's and master's are from Tel Aviv University too. Ami uses Python and C++ for data analysis. He contributed to various open source projects, and is the author of a libstd C++ extension shipped with g++ (pb_ds: policy-based data structures). Abstract Tags: code-introspection scikit-learn pandas data-science python machine learning Scikit-Learn is built directly over numpy, Python's numerical array library. Pandas adds to numpy metadata and higher-level munging capabilities. This talk describes how to intelligently auto-wrap Scikit-Learn for creating a version that can leverage pandas's added features. Description Scikit-Learn is the de-facto standard Python library for general-purpose machine learning. It operates over NumPy, an efficient, but low-level, homogeneic array library. Pandas adds to NumPy metadata, heterogeneity, and higher-leve munging capabilities. In the field of visualization, newer generation libraries, e.g., Seaborn and Bokeh, are providing safer, more readable, and higher-level functionality, by operating over Pandas data structures. Some of these are implemented using Matplotlib, a lower-level NumPy-based plotting library. This talk describes a library for a Pandas-based version of sickit-learn. Here, too, giving a Pandas interface to a machine-learning library, provides code which is safer to use, more readable, and allows direct integration with Pandas's higher-level munging capabilities. Due to the large-scale, and evolving nature, of sicikit-learn's codebase, it is infeasible to manually wrap it. Except for a small number of intentional deviations from sickit-learn, the library wraps Scikit-Learn modules lazily through module and class introspection, and dynamic module loading. Following a short review of the relevant points of Pandas and Scikit-Learn, the talk is roughly divided into two aspects: Scikit-Learn And Pandas User Perspective Safety Advantages Of Pandas-Based Estimators Using Metadata For Inter-Instance Aggregated Features And Cross-Validation Using Metadata For Advanced Meta-Algorithms: Stacking, Nested Labeled And Stratified Cross-Valdiation Python Develop Perspective Unique Challenges Of Scikit-Learn Introspection And Decoration Two Approaches For Wrapping Scikit-Learn Estimators Lazy Dynamic Module Loading Recorded at PyCon.DE 2017 Karlsruhe: pycon.de Video editing: Sebastian Neubauer & Andrei Dan Tools: Blender, Avidemux & Sonic Pi
Views: 128 PyConDE
100 COOL THINGS IN PYTHON (PART 1) - CS50 on Twitch, EP. 14
 
03:14:03
Join CS50's head course assistant, Veronica Nutting, for a tour of some of Python's cool features (with an eventual goal of reaching 100 over several parts!), from data structures to analyzing presidential data. Co-hosted by Colton Ogden. Join us live at twitch.tv/cs50tv and be a part of the live chat every week. This is CS50 on Twitch.
Views: 4334 CS50
Building a Real-time, Big Data Analytics Platform with Solr
 
35:45
Presentation slides available here: http://www.lucenerevolution.org/?q=2013/Lucene-Solr-Revolution-2013-Presentations Presented by Trey Grainger, Search Technology Development Manager, CareerBuilder Having "big data" is great, but turning that data into actionable intelligence is where the real value lies. This talk will demonstrate how you can use Solr to build a highly scalable data analytics engine to enable customers to engage in lightning fast, real-time knowledge discovery. At CareerBuilder, we utilize these techniques to report the supply and demand of the labor force, compensation trends, customer performance metrics, and many live internal platform analytics. You will walk away from this talk with an advanced understanding of faceting, including pivot-faceting, geo/radius faceting, time-series faceting, function faceting, and multi-select faceting. You'll also get a sneak peak at some new faceting capabilities just wrapping up development including distributed pivot facets and percentile/stats faceting, which will be open-sourced. The presentation will be a technical tutorial, along with real-world use-cases and data visualizations. After this talk, you'll never see Solr as just a text search engine again.
Views: 11874 LuceneSolrRevolution
What’s new in KNIME Analytics Platform 3.6 and KNIME Server 4.7
 
18:08
Here are the release highlights for KNIME Analytics Platform 3.6 and KNIME Server 4.7. The new features described in the video include the following topics. For KNIME Analytics Platform 3.6: - GUI - connecting and disconnecting nodes, replacing nodes in a workflow, zoom features, and easy server login - New Integrations with Git and Apache Kafka - New data manipulation nodes for outlier detection, column expressions, constant value filtering, and JavaScript based Scorer view - More deep learning nodes - The preview of the new database integration - More nodes and features for the Big Data Extensions For KNIME Server 4.7: - Centralized Management of Client Preferences - Preview features: job view, distributed executors, KNIME Workflow Hub For more details, check out the page “What's New in KNIME Analytics Platform 3.6 and KNIME Server 4.7” at https://www.knime.com/whats-new-in-knime-36
Views: 2283 KNIMETV
The new KNIME is  out!
 
22:18
Check all what is new in : - KNIME Analytics Platform 3.4 - KNIME Server 4.5 - KNIME Big Data Extension 2.0 Download KNIME Analytics Platform from: New datetime integration; H2O integration; KNIME Personal Productivity now part of KNIME Analytics Platform; Composite View for wrapped metanodes; Support for Python 2.0 and 3.0; New logisitc regression nodes; new nodes for audio and speech recognition; many new javascript based nodes for data visualization; support for Spark 2.0; ... and much more! https://tech.knime.org/whats-new-in-knime-34
Views: 2384 KNIMETV
Webinar: Sharing and Deploying Data Science with KNIME Server
 
36:59
Join us on Feb 13, 2019 at 5pm (CET) for the next live webinar: https://www.youtube.com/watch?v=qFV4P9-enZk You’re currently using the open source KNIME Analytics Platform, but looking for more functionality - especially for working across teams and business units? KNIME Server is the enterprise software for team based collaboration, automation, management, and deployment of data science workflows, data, and guided analytics. Non experts are given access to data science via KNIME Server WebPortal or can use REST APIs to integrate workflows as analytic services to applications, IoT, and systems. In this demonstration webinar, we’ll introduce you to all KNIME Server features. We’ll cover everything you need to manage your analytics at scale - deploying your workflows for sharing and collaboration, scheduling and automating tasks, templating and version control, as well as enterprise integration. We’ll show you the power of the REST API of KNIME Server, and you’ll get to know KNIME WebPortal - the ideal way for bringing data analytics to your non experts. Join us on September 10, 2018, at 5:00 pm CEST for a demonstration of KNIME Server and how it can extend the flexibility of KNIME Analytics Platform beyond the individual data scientist. Register via our event page: https://www.knime.com/about/events/webinar-features-and-functionalities-of-knime-server
Views: 1368 KNIMETV
ShinyCNV: a Shiny/R application to view and annotate CNV
 
12:23
ShinyCNV is developed by wrapping up the graphics and data-table processing functions in R packages, and the interactive features are provided from the Shiny package. Users can visually check normalized SNP data (either from Illumina or Affymetrix platform) together with reported CNVs from any CNV detection tools, and semi-atomically edit and update the CNVs. An updated tutorial with extra functions added is available from https://github.com/gzhmat/ShinyCNV. This video was recorded on my laptop, thus the operation was slower than it's used to be on my desktop. To make the ShinyCNV layout nicely, make sure you have a relatively big screen. Any comments and suggestions are very welcome!
Views: 172 Zhaohui Gu
What can Twitter Tell us about Quitting Smoking?
 
49:26
For the past decade, the Health Media Collaboratory has studied whether and how televised anti-smoking ads influence individuals' ideas about smoking, and whether they will quit smoking. Recently the Collaboratory recognized the growing significance of social networks and developing a new conceptual framework for understanding the relationship between media and health behavior. The analytics teams at the Health Media Collaboratory struggled with their usual data analytics tools. They could not find a happy medium between Excel and statistical software like SPSS to help analyze the tweets. They required a tool that could easily explore a million tweets without restrictions on data size, but also visualize the data in new ways to help us make insightful connections with our data. Tableau allows for quick discovery of the connections between the text of a tweet and its valuable metadata. This presentation will explore how Twitter and other social media platforms can be utilized to effectively understand an audience's behavior. What is a firehose of social data? Is there an advantage to using a purchased API firehose of social data versus Twitter's free pubic datastream? What social media data platforms are available and what are the demographic of those platforms? How can I effectively use keyword queries to capture social media data and how do I know my data is accurate? And finally, what metadata is available for social media platforms and how can Tableau be useful in the analysis and visualization of metadata.
Views: 299 Tableau Software
KNIME Analytics: a Review
 
09:42
This video shows a general review of the analytics capabilities of the KNIME Analytics Platform. Here we only mention: Random Forest, Deep Learning, Gradient Boosted Trees, Bagging and Boosting for ensemble methods, Decision Trees, Neural Networks, Logistic Regression, how to build your own ensemble model, and external integrations as Weka, H2O, R, and Python. This is what we show here, which for time reasons, is of course incomplete. Download and install KNIME Analytics Platform (https://www.knime.com/downloads) to explore the constantly growing set of machine learning and statistics algorithms available to analyze your data. Workflow is available on the KNIME EXAMPLES Server under 50_Applications/28_Predicting_Departure_Delays/01_Analytics This same workflow can be reproduced to run on a Spark and/or Hadoop platform still from within KNIME, as described in video "Scaling Analytics with Big data" https://youtu.be/b_ijiZdQB7g
Views: 3685 KNIMETV
SimPEG meeting Nov 14
 
44:03
Bane Sullivan (Colorado School of Mines) shares his work on PVGeo, an open-source framework for visualizing geoscientific data and models in VTK powered platforms like ParaView. The PVGeo Python package contains VTK powered tools for data visualization in geophysics which are wrapped for direct use within the application ParaView by Kitware. These tools are tailored to data visualization in the geosciences with a heavy focus on structured data sets like 2D or 3D time-varying grids. As an effort to bring interoperability between SimPEG projects and PVGeo, Bane will be demoing his work in SimPEG/discretize MR#114 to create an interface between discretize meshes and VTK data objects.
Views: 41 GeoSci.xyz
Evolution of pattern (Gource Visualization)
 
01:17
Gource visualization of pattern (https://github.com/clips/pattern). Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.
Views: 28 Landon Wilkins
Boomerang Trick Shots | Dude Perfect
 
06:11
Time to take boomerangs to the next level! ► Click HERE to subscribe to Dude Perfect! http://bit.ly/SubDudePerfect ► Click HERE to watch our most recent videos! http://bit.ly/NewestDudePerfectVideos http://bit.ly/NewestDPVideos ►Click HERE to follow Logan on Instagram! Follow @logan.broadbent: https://www.instagram.com/logan.broadbent ► SHOP our NEW Merchandise! - http://bit.ly/DPStore ►Click HERE to join the exclusive Dude Perfect T-Shirt Club! http://bit.ly/DPTShirtClub Music: Army by Zayde Wolf ►Click HERE to download : http://smarturl.it/ZWGoldenAge Play our NEW iPhone game! ► PLAY Endless Ducker on iPhone -- http://smarturl.it/EndlessDucker ► PLAY Endless Ducker on Android -- http://smarturl.it/EndlessDucker ► VISIT our NEW STORE - http://bit.ly/DPStore ► JOIN our NEWSLETTER - http://bit.ly/DPNewsletterEndCard ► WATCH our STEREOTYPES - http://bit.ly/StereotypesPlaylist In between videos we hang out with you guys on Instagram, Snapchat, Twitter, and Facebook so pick your favorite one and hang with us there too! http://Instagram.com/DudePerfect http://bit.ly/DudePerfectSnapchat http://Twitter.com/DudePerfect http://Facebook.com/DudePerfect Do you have a GO BIG mindset? See for yourself in our book "Go Big." ►http://amzn.to/OYdZ2s A special thanks to those of you who play our iPhone Games and read our book. You guys are amazing and all the great things you tell us about the game and the book make those projects so worthwhile for us! Dude Perfect GAME - http://smarturl.it/DPGameiPhone Dude Perfect BOOK - "Go Big" - http://amzn.to/OYdZ2s Click here if you want to learn more about Dude Perfect: http://www.dudeperfect.com/blog-2/ Bonus points if you're still reading this! Comment As always...Go Big and God Bless! - Your friends at Dude Perfect Business or Media, please contact us at: [email protected] ------------ 5 Best Friends and a Panda. If you like Sports + Comedy, come join the Dude Perfect team! Best known for trick shots, stereotypes, battles, bottle flips, ping pong shots and all around competitive fun, Dude Perfect prides ourselves in making the absolute best family-friendly entertainment possible! Welcome to the crew! Pound it. Noggin. - Dude Perfect
Views: 53309201 Dude Perfect
Modular PMML in KNIME
 
03:01
In KNIME 2.11, there are three new nodes to implement modular PMML, that is to assemble transformations and models into a PMML structure piece by piece avoiding repetitions. The nodes are Empty PMML Creator, PMML Transformation Appender, PMML Model Appender. This video is part of the recording of the "What is new in KNIME 2.11" webinar held on Dec 11 2014 and available on Youtube at: http://youtu.be/9RkRHI32Dy8 For more infos about updates in KNIME 2.11 check http://tech.knime.org/whats-new-in-knime-211
Views: 1727 KNIMETV
A Tour of the KNIME Node Repository
 
08:13
This video explores the KNIME Node Repository to show the features and modules available in the KNIME Analytics Platform. We start with IO, moving to Mining and Statistics, through ETL and Data Manipulation, Data Views, Tool & Script Integration, and many more. - Installation of KNIME Analytics Platform on Linux available at https://youtu.be/wibggQYr4ZA - Installation of KNIME Analytics Platform on Windows available at https://youtu.be/yeHblDxakLk - Installation of KNIME Analytics Platform on Mac available at https://youtu.be/1jvRWryJ220 - "What is a node, what is a workflow" https://youtu.be/M4j5jQBTEsM Next: - "The EXAMPLES Server" https://youtu.be/CRa_SbWgmVk - "Workflow Coach: The Wisdom of the KNIME Crowd" https://youtu.be/RusMXn-shsQ
Views: 5093 KNIMETV
Forever Stranded #16 - Advanced Rocketry Asteroid Mission
 
57:13
In this episode we send a rocket off on a mining mission to an asteroid. We build an Observatory, a Satellite, a Rocket. Advanced tooltips enabled with F3+H show the NBT data so you can see the specifics about blocks. This video is the my sixteenth episode of Forever Stranded, a series of the Mod Pack by GWSheridan. Surviving in this pack is quite a challenge. zmasterfun is the mod author and a tutorial video is https://youtu.be/qzfAff5N-34 Whisper Fire Satellite Rockets episode is https://youtu.be/edgjpsFwMIE?list=PLGdXrnO3rmEwO-wGbpnURN_SEu6VNm2u7 Philippe "xhedra" Asselin https://www.youtube.com/playlist?list=PL74I2IFhws3g4KOf7XljKq6VawINGBADS Music by Daria Shakhova is available at http://freemusicarchive.org/music/The_Owl/Fairy_Forest/the_owl_-_owls_secret I'm using Avisynth and MeGUI to prepare the video for youtube and recording with Dxtory and Lagarith video codec for recording. All the clips are wrapped with an avs script that has this (Python) template: source = "${source}" LoadPlugin("C:\\Program Files (x86)\\MeGUI\\tools\\ffms\\ffms2.dll") V = FFVideoSource(source, fpsnum=30, fpsden=1, threads=1).Lanczos4Resize(1280, 720) A1 = FFAudioSource(source, track=1).Normalize(volume=1.0, show=false) A2 = FFAudioSource(source, track=2).Normalize(volume=1.0, show=false) commentary = MonoToStereo(A1, A1).AmplifyDB(1.5) audio = MixAudio(commentary, A2, 0.9, 0.1) AudioDub(V, audio) return last An avs script that is common to all episodes is: LoadPlugin("C:\Program Files (x86)\MeGUI\tools\avisynth_plugin\NicAudio.dll") global s2 = "Until next time..." global pngfile = "D:\dxtory\foreverstranded\ForeverStranded.png" global endpngfile = "D:\dxtory\foreverstranded\ForeverStrandedShip.png" global musicfile = "D:\dxtory\The_Owl_-_02_-_owls_secret.mp3" function MakeVideo(clip c, string title, int "trim_in", int "trim_out") { sx = 460 sx = 70 sy = 574 sy = 460 font="Copasetic NF" font="Gin Rai Italic" font_size=45 text_color = $d6d6c8 #light gray halo_color = $ecb03c #light orange halo_color = $df8519 #dark orange fadein_frames = 0 fadeout_frames = 90 align = 5 title_frames = 150 pngclip = ImageSource(pngfile, pixel_type="RGB32", 0, 239).Lanczos4Resize(1280, 720) intro = Subtitle(pngclip, title, sx, sy, font=font, size=font_size, text_color=text_color, halo_color=halo_color, lsp=60) endpngclip = ImageSource(endpngfile, pixel_type="RGB32", fps=30, 0, 599).Lanczos4Resize(1280, 720).FadeIn(fadein_frames) music = NicMPG123Source(musicfile).AudioTrim(0, 20).FadeOut(150) ending = AudioDub(endpngclip, music) trim_in = Default(trim_in, 0) trim_out = Default(trim_out, c.FrameCount()) base = c.Trim(trim_in, trim_out) Overlay(base, pngclip, mask=pngclip.ShowAlpha().FadeOut(fadein_frames), mode="blend", opacity=0.9) Overlay(last, intro, mask=intro.ShowAlpha().FadeOut(fadein_frames), 0, 0) FadeOut(fadeout_frames) Subtitle(s2, first_frame=FrameCount-title_frames-1, last_frame=FrameCount-1, size=font_size, align=align, text_color=text_color, halo_color=halo_color, lsp=60) last++ending ConvertToYV12(interlaced=false) tweak(bright=10) return last } BlankClip(width=1280, height=720) The clips are joined with this script: joinduration=60 marbleradius=12 Import("D:\dxtory\foreverstranded\common.avs") video1=Import("D:\dxtory\foreverstranded\foreverstranded-0051.avi.avs") video2=Import("D:\dxtory\foreverstranded\foreverstranded-0052.avi.avs") video3=Import("D:\dxtory\foreverstranded\foreverstranded-0053.avi.avs") video4=Import("D:\dxtory\foreverstranded\foreverstranded-0054.avi.avs") video5=Import("D:\dxtory\foreverstranded\foreverstranded-0055.avi.avs").Trim(0,15489) video6=Import("D:\dxtory\foreverstranded\foreverstranded-0056.avi.avs") video = TransMarbles(video1, video2, joinduration, marbleradius, drop=false) video = Dissolve(video, video3, joinduration) video = Dissolve(video, video4, joinduration) video = Dissolve(video, video5, joinduration) video = Dissolve(video, video6, joinduration) MakeVideo(video, "Episode 16: Asteroid Mission") return last log = "D:\dxtory\foreverstranded\chapters.txt" Exist(log) ? NOP : Eval(""" len=video1.FrameCount()-joinduration WriteFile(log, String(len), append=false) len=len+video2.FrameCount()-joinduration WriteFile(log, String(len), append=true) len=len+video3.FrameCount()-joinduration WriteFile(log, String(len), append=true) len=len+video4.FrameCount()-joinduration WriteFile(log, String(len), append=true) len=len+video5.FrameCount()-joinduration WriteFile(log, String(len), append=true) len=len+video6.FrameCount()-joinduration WriteFile(log, String(len), append=true) """) return last
Views: 496 Duncan Webb
George Hotz | Livecoding twitchchess | a simple neural chess AI | part1
 
05:02:09
Date of stream 25 Jun 2018. Live-stream chat added as Subtitles/CC - English (Twitch Chat). Stream title: twitchchess: a simple neural chess AI. how long will it take to beat me? Video archive: - https://youtube.com/commaaiarchive/playlists Source files: - https://github.com/geohot/twitchchess Follow for notifications: - https://twitch.tv/georgehotz Subscribe to support: - https://twitch.tv/products/tomcr00s3_3000 - https://twitch.tv/products/georgehotz_3000 We archive George Hotz videos for fun. Follow for notifications: - https://twitter.com/commaaiarchive We are not affiliated with comma.ai. Official communication channels: - https://comma.ai - https://twitter.com/comma_ai - https://pscp.tv/comma_ai - https://github.com/commaai - https://discord.comma.ai - https://community.comma.ai - https://comma.ai/jobs - https://comma.ai/shop
Views: 181459 commaai archive
Forever Stranded #32 - Advanced Rocketry Terraforming the Moon
 
40:20
In this episode we: - have a look at AE2 priorities to make sure the use the storage - see Rocket Unloader now working in version 1.2.2 - go to Sol-3 to collect some gasses - see if it possible to have two rockets collecting gas simultaneously from the same launch platform. - make and colour six ender tanks - try to send up a biome changer satellite - would have help if it was in a satellite container - send up a biome changer satellite - bingo it works! You can check out athribiristan on his channel https://www.youtube.com/channel/UCLSGRZwWyFwDjAHYPDPRW2g This video is the my thirty second episode of Forever Stranded, a series of the Mod Pack by GWSheridan. Surviving in this pack is quite a challenge. Music by Daria Shakhova is available at http://freemusicarchive.org/music/The_Owl/Fairy_Forest/the_owl_-_owls_secret I'm using Avisynth and MeGUI to prepare the video for youtube and recording with Dxtory and Lagarith video codec for recording. All the clips are wrapped with an avs script that has this (Python) template: source = "${source}" LoadPlugin("C:\\Program Files (x86)\\MeGUI\\tools\\ffms\\ffms2.dll") V = FFVideoSource(source, fpsnum=30, fpsden=1, threads=1).Lanczos4Resize(1280, 720) A1 = FFAudioSource(source, track=1).Normalize(volume=1.0, show=false) A2 = FFAudioSource(source, track=2).Normalize(volume=1.0, show=false) commentary = MonoToStereo(A1, A1).AmplifyDB(1.5) audio = MixAudio(commentary, A2, 0.9, 0.1) AudioDub(V, audio) return last An avs script that is common to all episodes is: LoadPlugin("C:\Program Files (x86)\MeGUI\tools\avisynth_plugin\NicAudio.dll") global s2 = "Until next time..." global pngfile = "D:\dxtory\foreverstranded\ForeverStranded.png" global endpngfile = "D:\dxtory\foreverstranded\ForeverStrandedShip.png" global musicfile = "D:\dxtory\The_Owl_-_02_-_owls_secret.mp3" function MakeVideo(clip c, string title, int "trim_in", int "trim_out") { sx = 460 sx = 70 sy = 574 sy = 460 font="Copasetic NF" font="Gin Rai Italic" font_size=45 text_color = $d6d6c8 #light gray halo_color = $ecb03c #light orange halo_color = $df8519 #dark orange fadein_frames = 0 fadeout_frames = 90 align = 5 title_frames = 150 pngclip = ImageSource(pngfile, pixel_type="RGB32", 0, 239).Lanczos4Resize(1280, 720) intro = Subtitle(pngclip, title, sx, sy, font=font, size=font_size, text_color=text_color, halo_color=halo_color, lsp=60) endpngclip = ImageSource(endpngfile, pixel_type="RGB32", fps=30, 0, 599).Lanczos4Resize(1280, 720).FadeIn(fadein_frames) music = NicMPG123Source(musicfile).AudioTrim(0, 20).FadeOut(150) ending = AudioDub(endpngclip, music) trim_in = Default(trim_in, 0) trim_out = Default(trim_out, c.FrameCount()) base = c.Trim(trim_in, trim_out) Overlay(base, pngclip, mask=pngclip.ShowAlpha().FadeOut(fadein_frames), mode="blend", opacity=0.9) Overlay(last, intro, mask=intro.ShowAlpha().FadeOut(fadein_frames), 0, 0) FadeOut(fadeout_frames) Subtitle(s2, first_frame=FrameCount-title_frames-1, last_frame=FrameCount-1, size=font_size, align=align, text_color=text_color, halo_color=halo_color, lsp=60) last++ending ConvertToYV12(interlaced=false) tweak(bright=10) return last } BlankClip(width=1280, height=720) The clips are joined with this script: Import("D:\dxtory\foreverstranded\common.avs") video1=Import("D:\dxtory\foreverstranded\foreverstranded-0106.avi.avs") video2=Import("D:\dxtory\foreverstranded\foreverstranded-0107.avi.avs") video = TransMarbles(video1, video2, joinduration, marbleradius, drop=false) MakeVideo(video, "Episode 32: Terraforming")
Views: 1749 Duncan Webb
The KNIME Workflow Cycle
 
07:25
This is the Introduction part in the KNIME Server Webinar video explaining where the KNIME Desktop fits and where the KNIME Server fits in a KNIME Workflow development cycle.
Views: 545 KNIMETV
A Free LinkedIn Jobs Scraper! You Cannot MISS IT!!
 
06:27
A really easy to use LinkedIn scraper!!! I used only 2 minutes to make a crawler in Octoparse!! Though the results has some missing records.... From all the data scraped I think the amount of data may depend on the my internet network? I will figure out and upload a new one! Octoparse is AMAZING!!! http://www.octoparse.com/
Views: 1325 Nana Choi
Prof Aggelos Kiayias | Proving the security of blockchain protocols.
 
02:59:18
Prof Aggelos Kiayias at Shanghai Jiao Tong University | Proving the security of blockchain protocols. A video presentation with Prof Aggelos Kiayias at Shanghai Jiao Tong University & Winter School on Cryptocurrency and Blockchain Technologies, Filmed on location in Shanghai January 15th - 17th 2017. Prof Aggelos Kiayias is the Chair in Cyber Security and Privacy at the University of Edinburgh. His research interests are in computer security, information security, applied cryptography and foundations of cryptography with a particular emphasis in blockchain technologies and distributed systems, e-voting and secure multiparty protocols as well as privacy and identity management. He joins IOHK as chief scientist through a long-term consulting agreement between IOHK and the University of Edinburgh, UK, where he is based and continues to do research and teach courses in cyber security and cryptography. Prof Kiayias is also Professor in Residence (gratis) at the University of Connecticut, USA, and Associate Professor of Cryptography and Security (on leave) at the National and Kapodistrian University of Athens, Greece. Prof Kiayias’s cyber security research over the years has been funded by the Horizon 2020 programme (EU), the European Research Council (EU), the General Secretariat for Research and Technology (Greece), the National Science Foundation (USA), the Department of Homeland Security (USA), and the National Institute of Standards and Technology (USA). He has received an ERC Starting Grant, a Marie Curie fellowship, an NSF Career Award, and a Fulbright Fellowship. He holds a Ph.D. from the City University of New York and he is a graduate of the Department of Mathematics at the University of Athens. He has more than 100 publications in journals and conference proceedings in the area. He currently serves as the program chair of the Financial Cryptography and Data Security 2017 conference. https://iohk.io/team/aggelos-kiayias/ Cryptocurrencies like Bitcoin have proven to be a phenomenal success. The underlying blockchain techniques hold a huge promise to change the future of financial transactions, and even our way of computation and collaboration. Both development community and research community have recently made significant progresses. But at the same time, we are facing many challenges. This winter school aims to bring together the communities working on cryptocurrency and blockchain technologies. The target audience is anyone (students, researchers, developers, professionals) with an interest in cryptography and security. The lectures in the school will be given by world leading researchers in this area (such as Professors Jonathan Katz and Aggelos Kiayias - IOHK). All lectures will be self-contained, and we don’t assume the participants to have cryptography background. In this winter school, we will study a comprehensive set of topics about blockchain technologies, including: Bitcoin basics; Analysis of Nakamoto consensus in cryptographic setting and in game-theoretical setting; Ethereum and smart contracts; Alternative approaches to mining and consensus; Scalability; Anonymity. Input Output Founded in 2015 by Charles Hoskinson and Jeremy Wood, IOHK is a technology company committed to using peer-to-peer innovations to provide financial services to the three billion people who don’t have them. Cascading disruption It is the founding principle of IOHK. Cascading disruption is the idea that most of the structures that form the world’s financial, governance and social systems are inherently unstable and thus minor perturbations can cause a ripple effect that fundamentally reconfigures the entire system. Our company is committed to identifying and developing technology to force these perturbations in order to push towards a more fair and transparent order. Projects we work on Currently IOHK is studying new tools and paradigms for cryptographic research and the architecture of cryptocurrencies. More specifically, we are collaboratively developing an open-source library for universal composability and the Scorex project. We also do for-profit work aligned with our mission, vision and goals. The mission of IOHK We view the world as a series of giant and mostly interconnected social graphs with hundreds of complex systems embedded. Our mission is to perturb the graphs to a more connected, transparent and fair configuration for both the flow of ideas and value. Get our latest news updates: https://iohk.io/blog/ Meet the team: https://iohk.io/team/ Learn about our projects: https://iohk.io/projects/cardano/ Read our papers: http://iohk.link/paper-ouroboros Visit our library: https://iohk.io/research/library/ In the press: https://iohk.io/press/ Work with us: https://iohk.io/careers/
Views: 1309 IOHK
Nathalie Henry Riche: Researchers developing new ways to visualize complex data
 
19:11
Data-driven storytelling is becoming more pervasive with the help of sophisticated visualization tools. Tools that allow us to visualize complex data add more than decoration to our work, says Microsoft researcher Nathalie Riche. “Visualization helps you answer questions you did not even know you had. So it’s about generating hypotheses,” Riche said during her presentation at this year’s Women in Data Science conference at Stanford University. “Of course, you still need statistics and all those complex algorithms to actually answer those questions and really know if this is significant or not. The pattern is in the data,” she says. As an example, Riche showed a table containing four series of numbers. When computing basic statistics about the numbers using measures like standard deviation and regression, they appear to be equivalent and it would appear that the x and y coordinates are essentially the same. Yet when plugged into a basic visualization tool, it’s apparent that they are not. The tool Riche used in her demonstration is decades old. But her research is aimed at laying the groundwork for the development of advanced visualization applications that are simple enough to include in programs like Excel or the more sophisticated Power BI, she says. A tool in Excel called Power Map allows users to plot geographic and temporal data on a representation of a 3D globe or a custom map. A new custom visualization tool in Power BI lets a user animate each data point in a set of data. And Microsoft researchers are currently exploring ways to use virtual reality to visualize data in 3D, Riche says. Data visualization is a powerful way to tell stories, Riche says. “You can actually communicate a message very effectively with visualization. And in fact, those stories with data are everywhere.” As part of their research, Riche and her colleagues look for the most effective way to tell a story with visualization.
Forever Stranded #28 - Advanced Rocketry Asteroid Mining Rare Ores
 
16:14
In this episode I did every thing reverse order as it takes quite a while to fill the chip with data. We did: - try and fail to unload the rocket automatically - bug fixed in 1.2.2 - fill an asteroid chip with data - prepare a asteroid chip for the mission - initialise an asteroid chip - we have a little trouble with the data processing. Youtube cannot handle XML entities in the description so I've replaced the angle brackets with square brackets. [Asteroids] [asteroid name="Small Asteroid" distance="10" mass="100" massVariability="0.5" minLevel="0" probability="1" richness="0.2" richnessVariability="0.5"] [ore itemStack="minecraft:iron_ore" chance="15" /] [ore itemStack="minecraft:gold_ore" chance="10" /] [ore itemStack="minecraft:redstone_ore" chance="10" /] [/asteroid] [asteroid name="Med Asteroid" distance="10" mass="500" massVariability="0.5" minLevel="0" probability="0.5" richness="0.2" richnessVariability="0.5"] [!-- ore0 is Dilithium, 8 is Rutile, 9 is Aluminium and 10 is Iridium --] [ore itemStack="libvulpes:ore0" chance="20" /] [ore itemStack="libvulpes:ore0 8" chance="20" /] [ore itemStack="libvulpes:ore0 9" chance="20" /] [ore itemStack="libvulpes:ore0 10" chance="20" /] [/asteroid] [/Asteroids] This video is the my twenty eighth episode of Forever Stranded, a series of the Mod Pack by GWSheridan. Surviving in this pack is quite a challenge. Music by Daria Shakhova is available at http://freemusicarchive.org/music/The_Owl/Fairy_Forest/the_owl_-_owls_secret I'm using Avisynth and MeGUI to prepare the video for youtube and recording with Dxtory and Lagarith video codec for recording. All the clips are wrapped with an avs script that has this (Python) template: source = "${source}" LoadPlugin("C:\\Program Files (x86)\\MeGUI\\tools\\ffms\\ffms2.dll") V = FFVideoSource(source, fpsnum=30, fpsden=1, threads=1).Lanczos4Resize(1280, 720) A1 = FFAudioSource(source, track=1).Normalize(volume=1.0, show=false) A2 = FFAudioSource(source, track=2).Normalize(volume=1.0, show=false) commentary = MonoToStereo(A1, A1).AmplifyDB(1.5) audio = MixAudio(commentary, A2, 0.9, 0.1) AudioDub(V, audio) return last An avs script that is common to all episodes is: LoadPlugin("C:\Program Files (x86)\MeGUI\tools\avisynth_plugin\NicAudio.dll") global s2 = "Until next time..." global pngfile = "D:\dxtory\foreverstranded\ForeverStranded.png" global endpngfile = "D:\dxtory\foreverstranded\ForeverStrandedShip.png" global musicfile = "D:\dxtory\The_Owl_-_02_-_owls_secret.mp3" function MakeVideo(clip c, string title, int "trim_in", int "trim_out") { sx = 460 sx = 70 sy = 574 sy = 460 font="Copasetic NF" font="Gin Rai Italic" font_size=45 text_color = $d6d6c8 #light gray halo_color = $ecb03c #light orange halo_color = $df8519 #dark orange fadein_frames = 0 fadeout_frames = 90 align = 5 title_frames = 150 pngclip = ImageSource(pngfile, pixel_type="RGB32", 0, 239).Lanczos4Resize(1280, 720) intro = Subtitle(pngclip, title, sx, sy, font=font, size=font_size, text_color=text_color, halo_color=halo_color, lsp=60) endpngclip = ImageSource(endpngfile, pixel_type="RGB32", fps=30, 0, 599).Lanczos4Resize(1280, 720).FadeIn(fadein_frames) music = NicMPG123Source(musicfile).AudioTrim(0, 20).FadeOut(150) ending = AudioDub(endpngclip, music) trim_in = Default(trim_in, 0) trim_out = Default(trim_out, c.FrameCount()) base = c.Trim(trim_in, trim_out) Overlay(base, pngclip, mask=pngclip.ShowAlpha().FadeOut(fadein_frames), mode="blend", opacity=0.9) Overlay(last, intro, mask=intro.ShowAlpha().FadeOut(fadein_frames), 0, 0) FadeOut(fadeout_frames) Subtitle(s2, first_frame=FrameCount-title_frames-1, last_frame=FrameCount-1, size=font_size, align=align, text_color=text_color, halo_color=halo_color, lsp=60) last++ending ConvertToYV12(interlaced=false) tweak(bright=10) return last } BlankClip(width=1280, height=720) The clips are joined with this script: Import("D:\dxtory\foreverstranded\common.avs") video1=Import("D:\dxtory\foreverstranded\foreverstranded-0091.avi.avs") video2=Import("D:\dxtory\foreverstranded\foreverstranded-0092.avi.avs") video3=Import("D:\dxtory\foreverstranded\foreverstranded-0093.avi.avs") video = Dissolve(video1, video2, joinduration) video = Dissolve(video, video3, joinduration) MakeVideo(video, "Episode 28: Asteroid Mining Special Ores")
Views: 613 Duncan Webb
Get web page data into Excel using VBA
 
14:47
Our Excel training videos on YouTube cover formulas, functions and VBA. Useful for beginners as well as advanced learners. New upload every Thursday. For details you can visit our website: http://www.exceltrainingvideos.com/complete-automation-of-getting-web-page-data-into-excel-worksheet-using-vba/ In this video we show the complete automation of how to get data into an Excel worksheet using VBA. 1. We first study the website and find out the elements we'll need to access a form and the subsequent results. When you study the web page's HTML source code you'll note that the actual results are wrapped up in DIV containers. 2. Next we write the VBA code We use the getElementById method to get a reference to a single object and the getElementsByTagName method to get a collection of all the elements. Next we loop through all the elements and get the text properties or data ('innertext') of all the elements we wish to have. Our code instantiates our web browser (Internet Explorer) and navigates to the URL of our choice and then helps to get or extract the data using events. We also ensure that the code is placed in appropriate columns and rows so that any further analysis is made easy. Finally we use a recorded macro to format the data to make it more presentable to the human eye. You can view the complete code at: http://www.exceltrainingvideos.com/complete-automation-of-getting-web-page-data-into-excel-worksheet-using-vba/ Interesting Links: http://www.tushar-mehta.com/publish_train/xl_vba_cases/vba_web_pages_services/index.htm http://officevbavsto-en.blogspot.com.br/2012/06/vba-internet-acessing-web-pages-through_15.html?m=1 Get the book Excel 2016 Power Programming with VBA: http://amzn.to/2kDP35V If you are from India you can get this book here: http://amzn.to/2jzJGqU
Views: 165887 Dinesh Kumar Takyar
Forever Stranded #29 - Advanced Rocketry Orbital Laser Drill and Biome Scanner
 
33:33
In this episode we: - see that the Biome Scanner has been fixed - warp around to a couple of planets checking the Biomes - assemble an Orbital Laser Drill - we make some AE2 recipes for lens - turn it on and test it. This video is the my twenty ninth episode of Forever Stranded, a series of the Mod Pack by GWSheridan. Surviving in this pack is quite a challenge. Music by Daria Shakhova is available at http://freemusicarchive.org/music/The_Owl/Fairy_Forest/the_owl_-_owls_secret I'm using Avisynth and MeGUI to prepare the video for youtube and recording with Dxtory and Lagarith video codec for recording. All the clips are wrapped with an avs script that has this (Python) template: source = "${source}" LoadPlugin("C:\\Program Files (x86)\\MeGUI\\tools\\ffms\\ffms2.dll") V = FFVideoSource(source, fpsnum=30, fpsden=1, threads=1).Lanczos4Resize(1280, 720) A1 = FFAudioSource(source, track=1).Normalize(volume=1.0, show=false) A2 = FFAudioSource(source, track=2).Normalize(volume=1.0, show=false) commentary = MonoToStereo(A1, A1).AmplifyDB(1.5) audio = MixAudio(commentary, A2, 0.9, 0.1) AudioDub(V, audio) return last An avs script that is common to all episodes is: LoadPlugin("C:\Program Files (x86)\MeGUI\tools\avisynth_plugin\NicAudio.dll") global s2 = "Until next time..." global pngfile = "D:\dxtory\foreverstranded\ForeverStranded.png" global endpngfile = "D:\dxtory\foreverstranded\ForeverStrandedShip.png" global musicfile = "D:\dxtory\The_Owl_-_02_-_owls_secret.mp3" function MakeVideo(clip c, string title, int "trim_in", int "trim_out") { sx = 460 sx = 70 sy = 574 sy = 460 font="Copasetic NF" font="Gin Rai Italic" font_size=45 text_color = $d6d6c8 #light gray halo_color = $ecb03c #light orange halo_color = $df8519 #dark orange fadein_frames = 0 fadeout_frames = 90 align = 5 title_frames = 150 pngclip = ImageSource(pngfile, pixel_type="RGB32", 0, 239).Lanczos4Resize(1280, 720) intro = Subtitle(pngclip, title, sx, sy, font=font, size=font_size, text_color=text_color, halo_color=halo_color, lsp=60) endpngclip = ImageSource(endpngfile, pixel_type="RGB32", fps=30, 0, 599).Lanczos4Resize(1280, 720).FadeIn(fadein_frames) music = NicMPG123Source(musicfile).AudioTrim(0, 20).FadeOut(150) ending = AudioDub(endpngclip, music) trim_in = Default(trim_in, 0) trim_out = Default(trim_out, c.FrameCount()) base = c.Trim(trim_in, trim_out) Overlay(base, pngclip, mask=pngclip.ShowAlpha().FadeOut(fadein_frames), mode="blend", opacity=0.9) Overlay(last, intro, mask=intro.ShowAlpha().FadeOut(fadein_frames), 0, 0) FadeOut(fadeout_frames) Subtitle(s2, first_frame=FrameCount-title_frames-1, last_frame=FrameCount-1, size=font_size, align=align, text_color=text_color, halo_color=halo_color, lsp=60) last++ending ConvertToYV12(interlaced=false) tweak(bright=10) return last } BlankClip(width=1280, height=720) The clips are joined with this script: Import("D:\dxtory\foreverstranded\common.avs") video1=Import("D:\dxtory\foreverstranded\foreverstranded-0094.avi.avs") video2=Import("D:\dxtory\foreverstranded\foreverstranded-0095.avi.avs") video3=Import("D:\dxtory\foreverstranded\foreverstranded-0096.avi.avs") video4=Import("D:\dxtory\foreverstranded\foreverstranded-0097.avi.avs") video = Dissolve(video1, video2, joinduration) video = Dissolve(video, video3, joinduration) video = Dissolve(video, video4, joinduration) MakeVideo(video, "Episode 29: Orbital Laser Drill")
Views: 827 Duncan Webb
4 Kemampuan dasar untuk menjadi data scientist
 
00:44
Pertanyaan tersering ditanyakan ke kami adalah “Kalau mau jadi data scientist, skill apa saja yang harus dikuasai?” Salah satu mentor #IYKRA, Adi Wijaya menjawab pertanyaan tersebut lewat buku yang ia baca dan pengalamannya. Dalam video ini, Adi berbagi 4 kemampuan yang harus dimiliki untuk menjadi seorang data scientist. Tech Think Tank adalah vlog yang kontennya bertemakan data, bisnis dan teknologi.
Views: 63 IYKRA Indonesia