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KNIME provides a graphical interface for development. The introduction of KNIME has brought the development of Machine Learning models in the purview of a common man. This chapter will teach you how to master the data analytics using several well-tested ML algorithms.

Audience

This chapter has been prepared for the beginners to help them understand the basic to advanced concepts related to KNIME.

Prerequisites

Before you start practicing various types of examples given in this reference, we assume that you are already aware about the concepts of machine learning. If you are new to machine learning, please pick up its related chapter and get comfortable with its concepts before you actually start digging into it.

Introduction

Developing Machine Learning models is always considered very challenging due to its cryptic nature. Generally, to develop machine learning applications, you must be a good developer with an expertise in command-driven development. The introduction of KNIME has brought the development of Machine Learning models in the purview of a common man.

It provides a graphical interface (a user friendly GUI) for the entire development. In KNIME, you simply have to define the workflow between the various predefined nodes provided in its repository. KNIME provides several predefined components called nodes for various tasks such as reading data, applying various ML algorithms, and visualizing data in various formats. Thus, for working with KNIME, no programming knowledge is required. Isn’t this exciting?

The upcoming chapters of this chapter will teach you how to master the data analytics using several well-tested ML algorithms.

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