Free KNIME Courses

KNIME is an analytics platform that helps you create and understand data science and machine learning methodologies. You can explore the algorithms, various concepts, processes, and functions. You can visualize the workflow and understand the model and the pipeline better. 

KNIME provides you with a graphical interface for development. KNIME is known as software that is used for developing Data Science. Data Science and Machine Learning models are always challenging to understand because of their complex and cryptic nature. To work with Machine learning and Data Science, you must be a good developer who understands Data Science and Machine Learning concepts.

Through KNIME, even a person with a vague understanding of Machine Learning and Data Science can understand their models better. You can understand the different algorithms and functions with a better approach. You can create and explore more of its concepts and algorithms with the features of KNIME software.

KNIME provides you with a user-friendly GUI, basically a graphical interface for the development. KNIME has various predefined components called nodes, which help read the data, understand many Machine Learning algorithms, and allows you to visualize the data in multiple formats. It has repositories that include pre-defined nodes. Using these pre-defined nodes, you can specify the workflow between them.

KNIME is available for Linux, Windows, and Mac OS. To download KNIME on your Windows or Linux system, you can follow the instructions given on the download page of KNIME. When you run KNIME on your system, you can see that the workbench has several views. You can utilize these views to create and understand Data Science and Machine Learning better.

In the workbench, you see the below-mentioned views:

• Workspace
• Node Repository
• Outline
• Console
• KNIME Explorer
• Description

Workspace View

It is the most crucial view that helps you in creating the models regarding Machine Learning. Each workspace consists of several nodes. These nodes are connected using arrows. 

Generally, these nodes are defined from left to right, but it is not necessary as you can also freely move these nodes anywhere on the workspace as per the requirement. The connections will move accordingly between the nodes as you move them to maintain their connectivity. You can also add or remove the relationships between these nodes at any point in time.  

Node Repository

It is the next critical view. Node repository provides you with a list of nodes that can be used for your analytics. It is easy to work with as it systematically categorizes the nodes based on their functions. The categories are likely to be:

• IO
• Views
• Analytics

Nodes define the functionality that can be visually added to your workflow. When you expand these categories, you will find several options. For example, in the IO category, you can find various nodes to read data in multiple formats like CSV, XLS, ARFF, etc. 

You can define and understand various Machine Learning Algorithms through the Analytics node like Clustering, Bayes, Decision Tree, Ensemble Learning, and many more. You can pick the appropriate node from the repository, a Machine Learning algorithm, and apply it to your workspace for your analytics. Connect the input of this node to the output of your data reader node resulting in the creation of your workflow.

To explore the other views of KNIME and learn KNIME in-depth, enrol in the KNIME Free Courses offered by Great Learning Academy. Understand and utilize the KNIME analytical platform better by learning its concepts through these courses. You can also secure the course completion certificates on the successful completion of the enrolled courses.