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No-Code Data Science with KNIME

Edureka via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

3 weeks, 1 hour a week
Sessions

Self-Paced
Level

Beginner
Subtitles

English

Found in

Overview

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This course gives you hands-on access to KNIME, a free, open-source, no-code data science platform used to build real machine learning and generative AI solutions without writing a single line of code. You will learn to install KNIME, navigate its visual Workflow Editor, and connect nodes to read, clean, and transform data, building the same core data preparation skills used in professional analytics teams. From there, you will build and evaluate classification models using decision trees, confusion matrices, and ROC curves, all through drag-and-drop workflows. You will compare multiple models with KNIME's AutoML tools and deploy the model that performs best on your data. The course also introduces generative AI and retrieval-augmented generation (RAG) inside KNIME using only free resources, including a local GPT4All model and the Hugging Face free-tier API, so you can connect a language model to your own data and build a working AI pipeline. Whether you are new to data science or adding no-code machine learning tools to your existing skill set, you will finish this course able to build, evaluate, and explain a complete, AI-powered analytics solution, entirely with free software and no paid subscriptions.

Syllabus

Getting Started with No-Code Data Science and Machine Learning
- This module introduces no-code data science and the KNIME Analytics Platform as a single, free, end-to-end tool for building AI-powered analytics solutions. Learners explore the core vocabulary of nodes and workflows, install and navigate the KNIME Workbench, and connect foundational machine learning concepts to a visual, drag-and-drop environment.
Preparing Data and Building Predictive Models
- Develop practical data preparation and predictive modeling skills by progressing from data quality fundamentals to building and deploying classification models in KNIME. Learn how to clean, transform, and visualize data, then build and evaluate models such as Decision Trees using industry-standard metrics like accuracy, precision, recall, F1-score, and ROC-AUC. Strengthen your ability to compare and optimize models through AutoML and Integrated Deployment to produce accurate, production-ready predictions.
Extending KNIME Workflows with Generative AI
- This module focuses on integrating generative AI into KNIME to extend traditional data workflows with intelligent capabilities. Learners connect free LLM and embedding tools to ground workflows in real, retrievable data, then build, test, and refine a retrieval-augmented generation (RAG) pipeline. Learners validate AI-powered outputs for accuracy and responsible use, then optimize the complete solution for reliability, compliance, and business relevance.