Deep Dive into Data Science with KNIME Analytics Platform
In this course we will cover the major steps in a data science project. From data access, data pre-processing, and data visualization, to machine learning, model optimization, and deployment using KNIME Analytics Platform.
Outline/Structure of the Workshop
- Introduction to the data science cycle (CRISP-DM)
- Introduction to KNIME
- Reading Data
- Data Manipulation
- Data Visualization
- Machine Learning
- Model Optimization
- Exporting & Deployment
Different steps involved in a data science project and how they can be managed using the open source KNIME Analytics Platform.
Data Scientists, Data Engineers, Data Specialists, Machine Learning Engineers, Data Science Enthusiasts
Prerequisites for Attendees
This workshop has several hands-on sessions to practice what you'll learn. Please bring your laptop, ideally with KNIME Analytics Platform!
No previous KNIME experience is required.
schedule Submitted 2 months ago
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schedule 2 months agoSold Out!
In recent years, a wealth of tools has appeared that automate the machine learning cycle inside a black box. We take a different stance. Automation should not result in black boxes, hiding the interesting pieces from everyone. Modern data science should allow automation and interaction to be combined flexibly into a more transparent solution.
In some specific cases, if the analysis scenario is well defined, then full automation might make sense. However, more often than not, these scenarios are not that well defined and not that easy to control. In these cases, a certain amount of interaction with the user is highly desirable.
By mixing and matching interaction with automation, we can use Guided Analytics to develop predictive models on the fly. More interestingly, by leveraging automated machine learning and interactive dashboard components, custom Guided Analytics Applications, tailored to your business needs, can be created in a few minutes.
We'll build an application for automated machine learning using KNIME Software. It will have an input user interface to control the settings for data preparation, model training (e.g. using deep learning, random forest, etc.), hyperparameter optimization, and feature engineering. We'll also create an interactive dashboard to visualize the results with model interpretability techniques. At the conclusion of the workshop, the application will be deployed and run from a web browser.