Course Outline
Introduction
Getting Started with Knime
- What is KNIME?
- KNIME Analytics
- KNIME Server
Machine Learning
- Computational learning theory
- Computer algorithms for computational experience
Preparing the Development Environment
- Installing and configuring KNIME
KNIME Nodes
- Adding nodes
- Accessing and reading data
- Merging, splitting, and filtering data
- Grouping and pivoting data
- Cleaning data
Modeling
- Creating workflows
- Importing data
- Preparing data
- Visualizing data
- Creating a decision tree model
- Working with regression models
- Predicting data
- Comparing and matching data
Learning Techniques
- Working with random forest techniques
- Using polynomial regression
- Assigning classes
- Evaluating models
Summary and Conclusion
Requirements
- Experience with Python
- R experience
Audience
- Data Scientists
Testimonials (5)
The fact of having more practical exercises using more similar data to what we use in our projects (satellite images in raster format)
Matthieu - CS Group
Course - Scaling Data Analysis with Python and Dask
Very good preparation and expertise of a trainer, perfect communication in English. The course was practical (exercises + sharing examples of use cases)
Monika - Procter & Gamble Polska Sp. z o.o.
Course - Developing APIs with Python and FastAPI
It was a though course as we had to cover a lot in a short time frame. Our trainer knew a lot about the subject and delivered the content to address our requirements. It was lots of content to learn but our trainer was helpful and encouraging. He answered all our questions with good detail and we feel that we learned a lot. Exercises were well prepared and tasks were tailored accordingly to our needs. I enjoyed this course
Bozena Stansfield - New College Durham
Course - Build REST APIs with Python and Flask
Trainer develops training based on participant's pace
Farris Chua
Course - Data Analysis in Python using Pandas and Numpy
The pace was just right and the relaxed atmosphere made candidates feel at ease to ask questions.