Courses are organized by level: L1 basic, L2 advanced, L3 deployment, L4 specialized. Put what you’ve learnt into practice with the hands-on exercises. Text Mining Course: Importing text. At the course we will explore different supervised algorithms for classification and numerical problems such as decision trees, logistic regression, and ensemble models. [L1-DS] KNIME Analytics Platform for Data Scientists: Basics We’ll take you through everything you need to get started with KNIME Analytics Platform, so you can start creating well-documented, standardized, reusable workflows for your (often) repeated tasks. This course is about text mining, its theory, concepts, and applications. In this course, expert Keith McCormick shows how KNIME supports all the phases of the Cross Industry Standard Process for Data Mining (CRISP-DM) in one platform. After completing this course you'll have a set of fully functional workflows and will have learned how to build your own. We will explain a variety of approaches to compare data, find relationships, investigate development, and visualize multidimensional data. Knime Analytics Platform is an open-source software to create data science applications and services. [L2-LS] KNIME Analytics Platform for Data Scientists (Life Science): Advanced This course is designed for current and aspiring data scientists who would like to learn more about machine learning algorithms used commonly in data science projects. (Please note that this is an introductory data visualization course.) [L2-DW] KNIME Analytics Platform for Data Wranglers: Advanced The first preference is given mostly to the people who are certified in the knime training course. Currently, due to the Covid-19 situation, all courses are being run online. Learners will be guided to download, install and setup KNIME. Introduction to Knime Analytics Platform Course Overview. Learning LinkedIn Learning. This course lets you put everything you’ve learnt into practice in a hands-on session based on the use case: Eliminating missing values by predicting their values based on other attributes. The introduction of KNIME has brought the development of Machine Learning models in the purview of a common man. Learn all about flow variables, different workflow controls such as loops, switches, and error handling. And lastly learn how to visualize your data, export your results, format your Excel tables, and look beyond data wrangling towards data science, training your first classification model. NOTE: This course builds on the [L1-DS] KNIME Analytics Platform for Data Scientists: Basics course. We will also look at recommendation engines and neural networks and investigate the latest advances in deep learning. Knime Analytics Platform is an open-source software to create data science applications and services. KNIME Self-Paced Courses Start here to learn more about data science, data wrangling, text processing, big data, and collaboration and deployment at your own pace and in your own schedule! [L4-CH] Introduction to Working with Chemical Data Find out how to automatically find the best parameter settings for your machine learning model, see how Date&Time integrations work, and get a taste for ensemble models, parameter optimization, and cross validation. Get the training you need to stay ahead with expert-led courses on KNIME. With all of this, you’ll learn how to get your data into the right shape to generate insights quickly. The course then introduces you to KNIME Analytics Platform covering the whole data science cycle from data import, manipulation, aggregation, visualization, model training… This course is designed for Life Scientists who are just getting started on their data science journey with KNIME Analytics Platform. [L4-ML] Introduction to Machine Learning Algorithms Video created by University of California San Diego for the course "Code Free Data Science". Learn about the KNIME Spark Executor, preprocessing with Spark, machine learning with Spark, and how to export data back into KNIME/your big data cluster. Introduction to Knime Analytics Platform Course Overview. Get an introduction to the Apache Hadoop ecosystem and learn how to write/load data into your big data cluster running on premise or in the cloud on Amazon EMR, Azure HDInsight, Databricks Runtime or Google Dataproc. This course is designed for learners seeking to gain or expand their knowledge in the area of Data Science. This course introduces the main concepts behind Time Series Analysis, with an emphasis on forecasting applications: data cleaning, missing value imputation, time-based aggregation techniques, creation of a vector/tensor of past values, descriptive analysis, model training (from simple basic models to more complex statistics and machine learning based models), hyperparameter optimization, and model evaluation. This tutorial will teach you how to master the data analytics using several well-tested ML algorithms. It not only enables the communication of results, it also serves to explore and understand data better. Introduction to KNIME Analytics Platform This module will introduce the KNIME analytics platform. The course focuses on accessing, merging, transforming, fixing, standardizing, and inspecting data from different sources. This course is designed for those who are just getting started on their data science journey with KNIME Analytics Platform. Data visualization is one of the most important parts of data analysis and an integral piece of the whole data science process. [L1-DS] - KNIME Analytics Platform for Data Scientists: Basics, [L1-DW] - KNIME Analytics Platform for Data Wranglers: Basics, [L2-DS] - KNIME Analytics Platform for Data Scientists: Advanced, [L2-DW] - KNIME Analytics Platform for Data Wranglers: Advanced, Continental Nodes for KNIME — XLS Formatter Nodes, Splitting data and rejoining for manipulating only subpart, Generating data sets containing association rules, Generation of data set with more complex cluster structure, Parallel Generation of a Data Set containing Clusters, Advantages of Quasi Random Sequence Generation, Generating clusters with Gaussian distribution, Generating random missing values in an existing data set, Visualizing Git Statistics for Guided Analytics, Read all sheets from an XLS file in a loop, Recommendation Engine w Spark Collaborative Filtering, PMML to Spark Comprehensive Mode Learning Mass Prediction, Mass Learning Event Prediction MLlib to PMML, Learning Asociation Rule for Next Restaurant Prediction, Speedy SMILES ChEMBL Preprocessing Benchmarking, Using Jupyter from KNIME to embed documents, Clustering Networks based on Distance Matrix, Using Semantic Web to generate Simpsons TagCloud, SPARQL SELECT Query from different endpoints, Analyzing Twitter Posts with Custom Tagging, Sentiment Analysis Lexicon Based Approach, Interactive Webportal Visualisation of Neighbor Network, Bivariate Visual Exploration with Scatter Plot, Univariate Visual Exploration with Data Explorer, GeoIP Visualization using Open Street Map (OSM), Visualization of the World Cities using Open Street Map (OSM), Evaluating Classification Model Performance, Cross Validation with SVM and Parameter Optimization, Score Erosion for Multi Objective Optimization, Sentiment Analysis with Deep Learning KNIME nodes, Using DeepLearning4J to classify MNIST Digits, Sentiment Classification Using Word Vectors, Housing Value Prediction Using Regression, Calculate Document Distance Using Word Vectors, Network Example Of A Simple Convolutional Net, Basic Concepts Of Deeplearning4J Integration, Simple Anomaly Detection Using A Convolutional Net, Simple Document Classification Using Word Vectors, Performing a Linear Discriminant Analysis, Example for Using PMML for Transformation and Prediction, Combining Classifiers using Prediction Fusion, Customer Experience and Sentiment Analysis, Visualizing Twitter Network with a Chord Diagram, Applying Text and Network Analysis Techniques to Forums, Model Deployment file to database scheduling, Preprocessing Time Alignment and Visualization, Apply Association Rules for MarketBasketAnalysis, Build Association Rules for MarketBasketAnalysis, Filter TimeSeries Data Using FlowVariables, Working with Collection Creation and Conversion, Basic Examples for Using the GroupBy Node, StringManipulation MathFormula RuleEngine, Showing an autogenerated time series line plot, Extract System and Environment Variables (Linux only), Example for Recursive Replacement of Strings, Looping over all columns and manipulation of each, Writing a data table column wise to multiple csv files, Using Flow Variables to control Execution Order, Example for the external tool (Linux or Mac only), Save and Load Your Internal Representation. The course then introduces you to KNIME Analytics Platform covering the whole data science cycle from data import, manipulation, aggregation, visualization, model training, and deployment. It starts with a detailed introduction of KNIME Analytics Platform - from downloading it through to navigating the workbench. Video created by University of California San Diego for the course "Code Free Data Science". [L4-TS] Introduction to Time Series Analysis. We will also discuss various evaluation metrics for trained models and a number of classic data preparation techniques, such as normalization or dimensionality reduction. [L1-LS] KNIME Analytics Platform for Data Scientists (Life Science): Basics This course builds on the [L1-LS] KNIME Analytics Platform for Data Scientists (Life Science): Basics by introducing advanced data science concepts using Life Science examples. Course also covers popular text mining applications including social media analytics, topic detection and sentiment analysis. Intuitive, open, and continuously integrating new developments, KNIME makes understanding data and designing data science workflows and … Learn how to use KNIME Server to collaborate with colleagues, automate repetitive tasks, and deploy KNIME workflows as analytical applications and services. I believe I could directly apply the learnings to our department and optimize our data-related processes. You’ll also learn how to build and deploy an analytical application using KNIME Software and how to automate the deployment task using the KNIME Integrated Deployment Extension. Video created by University of California San Diego for the course "Code Free Data Science". The introduction of KNIME has brought the development of Machine Learning models in the purview of a common man. Put what you’ve learnt into practice with the hands-on exercises. 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