Description

Introduction
This training provides a beginner-friendly foundation in KNIME Analytics and core AI/ML concepts. Participants will learn how to build end-to-end workflows, prepare data, apply machine learning models, and visualize results using KNIME’s drag-and-drop platform. The program focuses on practical, hands-on learning to help learners quickly gain confidence in real-world AI/ML tasks.

Prerequisites
Basic computer skills
Understanding of simple data concepts (optional)
No prior programming or machine learning knowledge required

Table of Contents
1. KNIME Basics & Workflow Building
1.1 Introduction to KNIME Interface and Nodes
  1.2 Creating, Saving, and Managing Workflows
  1.3 Connecting and Configuring Nodes
  1.4 Data Import, Preview, and Exploration
  1.5 Hands-On: Building Your First Workflow

2. Data Preparation & Transformation
2.1 Data Cleaning, Filtering, and Sorting
  2.2 Missing Value Handling Techniques
  2.3 Feature Engineering Essentials
  2.4 Data Normalization and Encoding
  2.5 Joining, Grouping, and Aggregating Data

3. Machine Learning Concepts & Models
3.1 Basics of AI and Machine Learning
  3.2 Supervised vs Unsupervised Learning
  3.3 Classification Algorithms (Decision Tree, Logistic Regression)
  3.4 Clustering Algorithms (k-Means, Hierarchical Clustering)
  3.5 Model Training, Testing & Validation in KNIME
  3.6 Performance Evaluation Metrics

4. Visualization & Reporting
4.1 Data Visualization with KNIME Views
  4.2 Creating Interactive Dashboards
  4.3 Exporting Results & Workflow Sharing

5. Hands-On Mini Projects
5.1 Customer Segmentation Workflow
  5.2 Predictive Model for Sales/Churn
  5.3 End-to-End AI/ML Pipeline Automation

This beginner-focused training equips learners with practical skills to build KNIME workflows and apply essential AI/ML techniques. By the end, participants will confidently create automated analytics pipelines and implement machine learning models using KNIME.