How to explain to people all the cool applications you can build with data science and KNIME? We made a little video about that - and what it means to be open for innovation. To get from data to innovation faster, easier and more intuitively with KNIME. Our KNIME TV Channel will keep you up to speed with all new developments and features of the KNIME Analytics Platform and Server. Stay tuned and push your data science to the next level. Enjoy and share it freely with colleagues, customers, friends, family - and your boss! Credits Art Direction, Design, Animation, Production: The Magic Collective Script: Frank H Vial Narrator: Maya Tuttle
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This video is an introduction of KNIME. KNIME is an open source platform for data analysis, predictive analytics and modeling. It is not based on a script language rather it has a graphical interface. This video shows the basic functions KNIME in terms of the process of reading, manipulating, visualizing and analyzing data.
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KNIME Analytics Platform offers a number of Machine Learning algorithms. One of those is the Decision Tree. The Decision Tree Learner node is responsible for the training of a decision tree model. Here is a quick description of the basic settings available in its configuration window. The workflow shown in this video can be found on the EXAMPLES server under 04_Analytics/04_Classification_and_Predictive_Modelling /07_Decision_Tree. Other related videos: - Logistic Regression: Algorithm Settings https://youtu.be/AclQdjxpGA0
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In this video we build a basic model for churn prediction with KNIME. You think it is hard? Even with the whole talking and explanation, building the model takes less than half an hour in this video! Both workflows for training and deployment are shown. Both workflows are available on the KNIME EXAMPLES Server under 50_Applications/18_Churn_Prediction.
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How to paramterize a KNIME workflow? We have a workflow and we want it to work once for Germany, once for USA, once for China, etc ... That is, we need a parameter "country". Parameters in a KNIME workflow are called flow variables. Here we explain how to create one and how to use it to overwrite node settings. The workflow is available on KNIME EXAMPLES server under EXAMPLES://03_Control_Structures/06_Flow Variables/03_Create_and_Consume_FlowVars
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Another way to create flow variables in a KNIME workflow is to transform a data cell into a flow variable. To do that, you can use a node named "TableRow To Variable" and in this video you can see how. The workflow used in this video is available for download in the KNIME EXAMPLES Server under EXAMPLES://06_Control_Structures/03_Flow_Variables/04_Extract_Data_for Highest_Sale_Country
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A short video explaining the basic concept behind data aggregation, as implemented by the GroupBy and Pivoting node in the KNIME Analytics Platform. Aggregations in KNIME are implemented with the GroupBy node. Please check video "Basic Aggregations with the GroupBy node" to know more how to implement aggregations https://youtu.be/JQ-OWMt48ew
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The first step to building a micro-service based architecture is to be able to call a REST service. In this video we explain how to send a GET Request to a REST service from within a KNIME workflow using the GET Request node. Example workflows on how to call a REST service from a KNIME workflow can be found as usual on the KNIME EXAMPLES server in folder 01_Data_Access/05_REST_Web_Services. Other related videos: - Reading a text file https://youtu.be/flaHQw-Qhlg - Reading a table file https://youtu.be/tid1qi2HAOo - Reading an Excel file https://youtu.be/goo5ClHlfT8
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This video shows how to install the KNIME Analytics Platform core on Windows, by choosing one of the 4 installation options. - Installation of KNIME Analytics Platform on Linux available at https://youtu.be/wibggQYr4ZA - Installation of KNIME Analytics Platform on Mac available at https://youtu.be/1jvRWryJ220 Next: "How to install Extensions in KNIME Analytics Platform" https://youtu.be/8HMx3mjJXiw "Getting around the KNIME Welcome Page" https://youtu.be/Jib9t6hK6Bg
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This is a quick tutorial about the new R Interactive nodes in KNIME.
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This video shows how to install the KNIME Analytics Platform core on Linux, by choosing one of 2 installation options. - Installation of KNIME Analytics Platform on Windows at https://youtu.be/yeHblDxakLk. - Installation of KNIME Analytics Platform on Mac available at https://youtu.be/1jvRWryJ220 Next: "How to install Extensions in KNIME Analytics Platform" https://youtu.be/8HMx3mjJXiw "Getting around the KNIME Welcome Page" https://youtu.be/Jib9t6hK6Bg
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This video describes the GroupBy node of the KNIME Analytics Platform through some of the basic aggregations that can be implemented with it : count, unique count, missing value count, percent, many statistical measures, max and min, first and last, aggregation on datetime features, string concatenation. Refer to the video "What's data aggregation" to know more about data aggregation operations https://youtu.be/bDwF-TOMtWw Refer to the video "Advanced Aggregations with the GroupBy Node" to know more about more advanced aggregation methods and groups https://youtu.be/qym8hqYiTxE
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For supervised algorithms, a training and an evaluation phase are always required before applying the model to new data. Based on such requirements, supervised algorithms in KNIME Analytics Platform include a Learner node and a Predictor node: the Learner - Predictor construct.
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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
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How to select the best parameters when training a machine learning model? We could use the parameter optimization loop. Parameter Optimization loop is a special kind of loop in KNIME Analytics Platform that loops over a number of values and selects the value combination associated with the highest (lowest) accuracy (error) function. In this video we give 2 examples. - We optimize the minimum number of records per node in a Decision tree Learner node - We optimize sigma and step size in a Logistic regression Learner node Workflow is available in the KNIME EXAMPLES Server under 11_Optimization/06_Parameter_Optimization_two_examples Visit our website: https://www.knime.com
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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
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Quickform nodes are used to create flow variables and small GUIs. GUIs appear in configuration windows of wrapped metanodes when they contain one or more Quickform nodes. The same GUI is automatically generated when executing the workflow, and therefore the wrapped metanode, on the KNIME WebPortal. The workflow shown in this video can be found on the EXAMPLES server under 06_Control_Structures/02_Quickforms /01_Quickforms_and_Metanodes.
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Tableau is a popular reporting solution. It is not free nor open source. If you have a Tableau license, you can use the nodes from the KNIME Tableau integration to export data directly into a Tableau TDE file or into a Tableau Server. In this video, we present the Tableau nodes available in KNIME Analytics Platform, and show how they can be used to transfer data from KNIME Analytics Platform to both Tableau Desktop and Tableau Server. We discuss installation and setup of the Tableau extension, and present examples of a few simple Tableau visualizations.
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This video makes a list of some of the most commonly used advanced ETL functionalities for: - outlier detection, - dimensionality reduction, - feature generation, - feature selection, - imputing missing values, - automatic and manual, - simple and Machine Learning based, - involving coordinate transformations. 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
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I have data lying around. How do I start - and continue - if I want to build a predictive model based on these data? Well, it is not really like following a cooking recipes with precise steps. It is more like adjusting the steps here and there, going back and starting again with different parameters or maybe even more drastically anew with different algorithms. This video explains this general iterative process.
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This video shows how to use the Excel Reader node. The Excel Reader node reads Excel files (.xls and .xlsx). An example workflow is available on the KNIME EXAMPLES server under: 01_Data_Access/01_Common_Type_Files/07_Reading_Excel_Files Similar videos: - "Data Access in KNIME: File Reader" https://youtu.be/flaHQw-Qhlg - "Data Access in KNIME: Table Reader" https://youtu.be/tid1qi2HAOo
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This is the introduction part of the text Mining Webinar recorded on October 30 2013 (https://www.youtube.com/edit?o=U&video_id=tY7vpTLYlIg). It gives a broad overview about text mining applications, the text mining extension of KNIME, and a typical text mining workflow.
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This video shows how to read text files. Example workflows on how to use the Table Reader node can be found on the EXAMPLES server within the KNIME Analytics Platform (www.knime.org) under 01_Data_Access/01_Common_Type_Files Previous: - "Annotations and comments" https://youtu.be/AHURYB_O8sA Next: - How to read a .table formatted files https://youtu.be/tid1qi2HAOo
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When you download the KNIME Analytics Platform from www.knime.org you can choose whether to install the core KNIME Analytics Platform or the same package including also the KNIME extensions. If you have selected the first option or if you need to install additional extensions, this video is for you. It guides you step by step on installing additional extensions into your KNIME Analytics Platform. Previous videos: - How to install KNIME Analytics Platform on Windows https://youtu.be/yeHblDxakLk - How to install KNIME Analytics Platform on Linux https://youtu.be/wibggQYr4ZA - How to install KNIME Analytics Platform on Mac https://youtu.be/1jvRWryJ220 Next: "Getting around the KNIME Welcome Page" https://youtu.be/Jib9t6hK6Bg
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Operations to standardize data include, for example, converting string and date & times values to follow the same style, normalizing numbers, and generating new features. When applying data transformation operations, there are a few very versatile nodes which probably cover all the operations you need: the String Manipulation node, the Math Formula node, and the Rule Engine node. The workflow used in this video is available on the KNIME EXAMPLES server under 02_ETL_Data_Manipulation/04_Transformation/02_StringManipulation_MathFormula_RuleEngine To know more about Data Manipulation in KNIME Analytics Platform check Chapter 3 of the Introductiory Course to Data Science with KNIME https://www.knime.com/knime-introductory-course/chapter3
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We've developed a Guided Analytics application for machine learning automation. This application runs fully from a web browser and exposes only the necessary interaction points for the end analysts. Indeed, based on solely nodes and integrations in KNIME Analytics Platform, we have developed an application that lets the analyst: - Upload the data - Quickly select which column to predict - Easily filter out bad or not usable columns - Select the ML algorithms - Customize parameter optimization and feature engineering techniques - Select the platform for execution (local, cloud, Spark cluster, etc…) Machine Learning models are then trained in the background, using feature engineering and parameter optimization. A dashboard is produced displaying performance measures and execution times for each model. Finally, all models can be downloaded from the web browser onto your KNIME Analytics Platform. The workflow running behind this web based application is available on the KNIME Workflow Hub at https://www.knime.com/about/workflow-hub-guided-analytics-for-ML-automation Or on the EXAMPLES server under /50_Applications/36_Guided_Analytics_for_ML_Automation
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KNIME Analytics Platform offers a number of Machine Learning algorithms. One of those is the Logistic Regression. The Logistic Regression Learner node is responsible for the training of a logistic regression model. Here is a quick description of the basic settings available in its configuration window. The workflow shown in this video can be found on the EXAMPLES server under 04_Analytics/04_Classification_and_Predictive_Modelling /06_Logistic_Regression. Other related videos: - Logistic Regression Node: Output Values and Memory Handling https://youtu.be/ywPvpgFF2i4
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If you want to know the details of the algorithm behind the Logistic Regression Learner node in KNIME Analytics Platform, this is the video for you. It provides a high-level description of the goal, the structure, and the implementation for the training of a logistic regression model. It also provides a few additional details about regularization and coefficient interpretation. Visit our website for more details: https://www.knime.com/
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This video is the first part of a longer video on the Joiner node. The Joiner node in KNIME implements the join operation. In this first part only the inner join between two data sets is performed. The second part of this video is available at: https://youtu.be/gj66visISa8 The explanation of the join operation is available at: https://youtu.be/gj66visISa8https://youtu.be/6BigLM6vbhs
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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
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There are many ways of closing a loop in the KNIME Analytics Platform: outputting one data table or two, a flow variable, or appending columns. All those loop closing options are explained in this video. For more example workflows containing loops, you can refer to the EXAMPLES server under 06_Control_Structures/04_Loops. The EXAMPLES server is accessible from within the KNIME.Analytics Platform (www.knime.org).
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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
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Need help for the next node? The KNIME Workflow Coach can help you. Based on the wisdom of the KNIME crowd, it can provide useful suggestions for the next step/node in your workflow! Using data science to make data science smarter! Previous videos: - "A Tour of the KNIME Node Repository" https://youtu.be/XXZ_ny93Jl0 - "KNIME EXAMPLES Server" https://youtu.be/CRa_SbWgmVk Next video: - "Customize KNIME Analytics Platform" https://youtu.be/hLdeVNeH0-0
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This video explains what the KNIME Concatenate node does and how to use it to concatenate together two data sets in a KNIME workflow. Example workflow is available on the KNIME EXAMPLES server under 02_ETL_Data_Manipulation/03_Joining_and_Concatenating/01_Concatenate To know more about the concatenation operation, check https://youtu.be/VzH2lHbDAg0
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An exploration of the Workbench in KNIME Analytics Platform: KNIME Explorer, Node Repository, Workflow Coach, Views, Preferences, and more. This video helps you understanding what is where inside KNIME Analytics Platform. Previous: - 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 - "How to install Extensions in KNIME Analytics Platform" https://youtu.be/Yvm2pQbAK0E - "Getting around the KNIME Welcome Page" https://youtu.be/Jib9t6hK6Bg Next: "What is a node, what is a workflow" https://youtu.be/M4j5jQBTEsM "Set KNIME Preferences" https://youtu.be/hLdeVNeH0-0
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This video shows the new Feature Selection nodes of KNIME Analytics Platform 3.2. To see a full description of the new features of KNIME Analytics Platform 3.2 check the recording of the webinar held in July 2016 https://youtu.be/8Lm9zBUhM48.
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Metanodes are important to keep your workflow tidy and proper and to expose some paramters to the end users. KNIME Analytics Platform offers two types of metanodes: simple metanodes and wrapped metanodes. What is the difference? When shall we use one instead of the other? This video illustrates the general concept of metanodes, the differences, and best practices.
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KNIME Analytics Platform is an open platform and as such offers a number of integrations with external tools. A prominent place among integrations is of course occupied by R. This video shows how to run Microsoft R from within the KNIME Analytics Platform, as a pure R script or as a hybrid approach involving R script segments and KNIME nodes.
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In ETL / Data Manipulation sometimes you need to put together two or more data tables, via join or concatenation. This video explains what the join operation is, the different types of join, and the issues that come with it. If you want to also know what concatenation is, check this video https://youtu.be/VzH2lHbDAg0
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The knime:// protocol allows to define the location of a file relatively to the current workspace or currently executing workflow. In this video two examples are shown: for Table Reader and File Reader. Previous: - Table Reader node https://youtu.be/tid1qi2HAOo Next: - "What is Row Filtering?" https://youtu.be/NJwWwpIEBpg
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Row filtering in KNIME is implemented with the Row Filter node. In this video we explore how to implement a pattern matching based row filter with the Row Filter node. To know more: - "What is Row Filtering?" https://youtu.be/NJwWwpIEBpg - "Row Filter with pattern matching" https://youtu.be/j3YhdEgu0Z0 - "Row Filter on a numerical range" https://youtu.be/rBmGjMu9EG4 - "Row Filter with RowID" https://youtu.be/nomaYlGJwmA - "Advanced Row Filter" https://youtu.be/WcpEIzzZ-yc - "Advanced Row Filter for Special Data Types" https://youtu.be/miUZNhePBLg - "What is Column Filtering?" https://youtu.be/wQE_cXwDH-I
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This video is the introduction to the webinar "Creating and Modifying Predictive Models with PMML" (full recording available at http://youtu.be/_5pZm2PZ8Q8). Before explaining how to create and modify PMML models in KNIME, this short video gives a quick introduction to KNIME showing what it is and how it can be used.
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KNIME Analytics Platform allows data analysts/data scientists to build predictive models easily, quickly, and with arbitrary complexity. Now we can extend these great features of the tool to make it even more scalable and bring analytics to the data that resides in your Azure cloud. In this webinar we will show you how to use KNIME Cloud Analytics Platform to build a scalable churn prediction model; how to upload your data to the KNIME Cloud Analytics Platform; how to connect to existing databases; and put you in a position to start exploring the possibilities of Advanced Analytics in the Azure cloud.
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