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Machine learning is a data-driven approach, and nowadays, we are producing a lot of data from our smartphones, cameras, internet.
So using machine learning techniques/algorithms, we can find the hidden information from the data or even you can make a prediction. It depends on the data and the nature of data, so we are not going to cover the whole machine learning fields here, I am just going to teach you how can you create the models in KNIME.
We have several types of tools available for the datasets, one of the most popular is the Wika, I have personally used it, but it was too hard to understand in minimum time, KNIME help you to create/train you, models, with few built-in nodes.
For each node in the top left corner, KNIME also provides us with documentation. It also informs us of the choices so that we can conveniently use the node without visiting the official website.
KNIME implies that nodes are accessible in the upper left corner according to our function. KNIME gives us the search bar to find new nodes, and we can quickly browse. The search bar is on the left side.
It also provides us the additional node where we can import and export the KNIME workflow as if we were to run the java code.
This is KNIME’s elegance, KNIME calculation is easy as it includes the math problem nodes and much more; we have plenty of nodes to represent the data in the view. We have a great many plugins in our KNIME, which allow us to access the plugin from the package without going anywhere. It also provides us with the unique features related to the domain.
- Read and Write Node in KNIME
- Linear Regression in KNIME
- Unsupervised learning
- Multilayer Perceptron in KNIME
- Decision tree in KNIME
- Probabilistic neural network
- Support vector machine in KNIME
- K-fold cross-validation in KNIME
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- Language: English
- Certificate of Completion