Description

Introduction
This training focuses on equipping learners with the core AI/ML techniques used in data science, implemented through the KNIME Analytics Platform. Participants will learn how to prepare complex datasets, build predictive and clustering models, perform feature engineering, evaluate model performance, and automate analytics processes. The course emphasizes real-world, hands-on practice to enable participants to solve data science challenges using KNIME’s visual workflow environment.

Prerequisites
Basic data handling or spreadsheet knowledge
Understanding of simple analytical concepts (optional)
No coding or prior machine learning experience required

Table of Contents

1. Introduction to KNIME for Data Science
  1.1 Overview of KNIME Analytics Platform
  1.2 Understanding Nodes, Workflows, and Data Structures
  1.3 KNIME Interface Navigation: Node Repository, Console, Output Table
  1.4 Managing Workflows, Metadata, and Project Structure
  1.5 Data Import: CSV, Excel, Databases, APIs

2. Data Preprocessing & Feature Engineering
  2.1 Handling Missing, Duplicate, and Inconsistent Data
  2.2 Data Type Conversion & Parsing
  2.3 Feature Engineering: One-Hot Encoding, Binning, Scaling
  2.4 Outlier Detection & Treatment Techniques
  2.5 Text Cleaning & Basic NLP Preprocessing
  2.6 Rule-Based Filters and Column Expressions
  2.7 Data Sampling Techniques (Stratified, Random, Oversampling)

3. Core Machine Learning Techniques
  3.1 Introduction to Machine Learning in KNIME
  3.2 Classification Techniques: Decision Trees, Random Forest, Naive Bayes
  3.3 Regression Techniques: Linear Regression, Gradient Boosting, Tree Regression
  3.4 Clustering Techniques: k-Means, Hierarchical Clustering, DBSCAN
  3.5 Dimensionality Reduction: PCA & Feature Selection
  3.6 Training, Testing & Cross-Validation Workflows
  3.7 Hyperparameter Tuning using KNIME Nodes
  3.8 Performance Evaluation Metrics: Accuracy, F1, RMSE, Silhouette Score

4. Data Visualization & Reporting
  4.1 Visual Analytics with KNIME Charts & Views
  4.2 Correlation Matrices, Scatter Plots & Distribution Analysis
  4.3 Model Interpretation: Feature Importance & Decision Paths
  4.4 Creating Interactive Dashboards
  4.5 Automated Reporting Using KNIME Workflows

5. Advanced KNIME Integrations
  5.1 Using Python and R Scripts Inside KNIME
  5.2 Database Modeling & SQL Query Integration
  5.3 Text Mining, Sentiment Analysis & Keyword Extraction
  5.4 Big Data Extensions (Hadoop/Spark Overview)
  5.5 Deployment on KNIME Server (Overview)

6. Real-World AI/ML Project Workflows
  6.1 Customer Churn Prediction Workflow
  6.2 Market Basket Analysis using Association Rules
  6.3 Sales Forecasting & Time Series Modeling
  6.4 Fraud Detection using Classification Models
  6.5 Segmentation Model using Clustering Techniques

This training program provides participants with end-to-end data science capabilities using KNIME, covering data preparation, feature engineering, and hands-on machine learning techniques. By applying AI/ML models to real datasets, learners gain confidence in building complete analytics workflows that can be deployed and reused for business decision-making.