KNIME Self-Paced Courses

This course is designed for those who are just getting started on their data science journey with KNIME Analytics Platform. It starts with a detailed introduction of KNIME Analytics Platform - from downloading it through to navigating the workbench.
 
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.
 
Course content:

• Overview of KNIME Analytics Platform & Data Access
• Data Cleaning & Visualization
• Bringing Things Together
• Machine Learning & Data Export

NOTE: If you don't have a KNIME account, please create one and return to this page to continue.

This course is designed for those who are just getting started on their data wrangler journey with KNIME Analytics Platform. It starts with a detailed introduction of KNIME Analytics Platform - from downloading it through to navigating the workbench.

The course focuses on accessing, merging, transforming, fixing, standardizing, and inspecting data from different sources. It dives into data cleaning and aggregation, using methods such as advanced filtering, concatenating, joining, pivoting, and grouping. With all of this, you’ll learn how to get your data into the right shape to generate insights quickly.

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.

Course content:

• Overview of KNIME Analytics Platform
• Data Access
• Data Cleaning
• Bringing Things Together
• Tips & Tricks

NOTE: If you don't have a KNIME account, please create one and return to this page to continue.

This course builds on the [L1-DS] KNIME Analytics Platform for Data Scientist: Basics course by introducing advanced data science concepts.

Learn all about flow variables, different workflow controls such as loops, switches, and error handling. 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.

Course content:

• Date&Time & Databases
• Flow Variables & Components
• Workflow Control
• Advanced Machine Learning

NOTE: If you don't have a KNIME account, please create one and return to this page to continue.

This course builds on the [L1-DW] KNIME Analytics Platform Course for Data Wranglers: Basics course by introducing advanced concepts for building and automating workflows.

Learn all about flow variables, different workflow controls such as loops, switches, and how to catch errors. And lastly learn how to visualize your data, export your results, and look beyond data wrangling towards data science, training your first classification model.

Course content:

• Visualization
• Data Export
• Workflow Abstraction
• Loops
• Introduction to Data Science

NOTE: If you don't have a KNIME account, please create one and return to this page to continue.

This course introduces you to the procedures and the best practices to test, deploy, monitor, and maintain machine learning models on KNIME Business Hub.

You will also learn how to use the Continuous Deployment for Data Science (CDDS) extension to enable continuous deployment on KNIME Business Hub.

Course content:

• Requirements to deployment of data science applications
• Deployment process, types, and practices
• General KNIME Business Hub principles and features
• Workflow invocation
• Integrated deployment
• Testing
• Workflow execution and deployment
• Versioning
• Performance optimization and orchestration
• Machine learning experiment logging
• Continuous Deployment for Data Science (CDDS)

NOTE: If you don't have a KNIME account, please create one and return to this page to continue.

This course dives into the details of KNIME Server and KNIME WebPortal. Learn how to use KNIME Server to collaborate with colleagues, automate repetitive tasks, and deploy KNIME workflows as analytical applications and services.

Specifically, learn how to share workflows, data, and components with colleagues and among different functions within the company. Learn how to set access rights on your workflows, data, and components, execute workflows remotely on KNIME Server and from the KNIME WebPortal, and schedule report and workflow executions.

Course content:

• Collaboration
• Automation and Deployment
• Management

NOTE: If you don't have a KNIME account, please create one and return to this page to continue.

This course introduces you to how different Machine Learning Algorithms work and how to implement them in KNIME workflows. Explore different supervised algorithms for classification and numerical problems such as decision trees, logistic regression, and ensemble models. The course further introduces you to recommendation engines and examines unsupervised learning techniques, such as clustering with k-Means, hierarchical clustering, and DBSCAN. You will also learn about various evaluation metrics for trained models and several classic data preparation techniques, such as normalization and dimensionality reduction.

Course content:

• The Machine Learning Process
• Classification
• Numeric Prediction
• Logistic Regression
• Ensemble Models
• Recommendation Engines
• Clustering
• Data Preparation

NOTE: If you don't have a KNIME account, please create one and return to this page to continue.

This course is about text mining, its theory, concepts, and applications. Specifically, the course focuses on the acquisition and processing of textual data with KNIME Analytics Platform. You will learn how to use the Text Processing Extension to read textual data into KNIME, enrich it semantically, preprocess it, transform it into numerical data, and extract information and knowledge from it through descriptive analytics methods. The course also covers popular text mining applications including topic detection and sentiment analysis.

Course content:

• Intro to Text Processing and Importing Text
• Reading Documents and Adding Tags
• Cleaning and Transformation
• Different Applications

NOTE: If you don't have a KNIME account, please create one and return to this page to continue.

This course will provide you with the basic orientation in the world of deep learning and the skills to assemble, train, and apply different deep learning modules.

Deep learning is used successfully in many data science applications, such as image processing, text processing, and fraud detection. KNIME offers an integration to the Keras libraries for deep learning, combining the codeless ease of use of KNIME Analytics Platform with the extensive coverage of deep learning paradigms by the Keras libraries.

Course content:

• Define and execute feed-forward neural networks
• Compare loss and activation functions
• Define techniques to avoid overfitting
• Build an autoencoder
• Preprocess data for different deep learning applications
• Execute recurrent neural networks to perform prediction on sequential data
• Operate convolutional neural networks to perform prediction on image data

NOTE: If you don't have a KNIME account, please create one and return to this page to continue.