Introduction

This training focuses on leveraging KNIME Analytics to build and deploy Artificial Intelligence (AI) and Machine Learning (ML) models effectively. KNIME provides a powerful platform for data analytics, integrating advanced AI and ML capabilities with intuitive graphical workflows. In this course, you will learn to use KNIME’s tools and nodes to process data, train models, and apply AI techniques to a range of use cases such as classification, regression, clustering, and prediction. The course also covers model evaluation, tuning, and deployment, preparing you to handle complex data analysis tasks using KNIME.

Prerequisites

• Basic understanding of data analytics and machine learning concepts
• Familiarity with KNIME Analytics Platform
• Experience in working with datasets and performing data preprocessing
• Knowledge of basic Python or R for integration with KNIME (optional)
• Understanding of statistical analysis and model evaluation

Table of Contents

1. Introduction to KNIME Analytics Platform
   1.1 Overview of KNIME
   1.2 Installing and Setting Up KNIME
   1.3 KNIME Interface and Workflow Basics
   1.4 KNIME Nodes for AI and Machine Learning
2. Data Preprocessing and Cleaning with KNIME
   2.1 Importing and Exporting Data in KNIME
   2.2 Data Cleaning Techniques: Missing Values, Outliers, and Duplicates
   2.3 Data Transformation and Normalization
   2.4 Feature Engineering and Selection
3. Supervised Learning in KNIME
   3.1 Introduction to Supervised Learning Algorithms
   3.2 Building a Classification Model with KNIME
   3.3 Regression Models in KNIME
   3.4 Model Evaluation: Accuracy, Precision, Recall, F1 Score
4. Unsupervised Learning in KNIME
   4.1 Introduction to Clustering Algorithms
   4.2 K-Means and DBSCAN Clustering in KNIME
   4.3 Principal Component Analysis (PCA) and Dimensionality Reduction
   4.4 Association Rule Learning and Market Basket Analysis
5. Advanced Machine Learning Techniques in KNIME
   5.1 Ensemble Methods: Random Forest, AdaBoost, and Gradient Boosting
   5.2 Support Vector Machines (SVM) in KNIME
   5.3 Hyperparameter Tuning with Grid Search and Random Search
   5.4 Cross-Validation and Model Validation Techniques
6. Deep Learning in KNIME
   6.1 Introduction to Deep Learning Concepts
   6.2 Using KNIME Deep Learning Nodes(Ref: AI & Data Analytics from Basics to Advanced)
   6.3 Building a Neural Network Model in KNIME
   6.4 Image Recognition with Convolutional Neural Networks (CNNs)
7. Natural Language Processing (NLP) with KNIME
   7.1 Text Mining and Tokenization in KNIME
   7.2 Sentiment Analysis with KNIME
   7.3 Topic Modeling with Latent Dirichlet Allocation (LDA)
   7.4 Named Entity Recognition (NER) and Text Classification
8. AI and ML Model Deployment in KNIME
   8.1 Exporting and Deploying Models in KNIME
   8.2 Integrating KNIME with Cloud Services (AWS, Azure)
   8.3 Model Deployment in Production Environments
   8.4 Creating Reusable Model Pipelines in KNIME
9. Performance Evaluation and Model Tuning
   9.1 Evaluating Model Performance: ROC Curve, Confusion Matrix
   9.2 Model Tuning and Optimization
   9.3 Using KNIME’s AutoML Feature for Model Tuning
   9.4 Monitoring and Improving Model Accuracy
10. Real-World Applications of AI and ML with KNIME
    10.1 Case Study: Predictive Maintenance in Manufacturing
    10.2 Case Study: Fraud Detection in Financial Services
    10.3 Case Study: Customer Segmentation and Personalization in Marketing
    10.4 Future Trends and Emerging Applications of AI and ML with KNIME

Conclusion

By completing this course, you will have gained a comprehensive understanding of how to use KNIME Analytics for AI and machine learning tasks. You will be able to preprocess data, implement and evaluate ML models, and deploy AI solutions in real-world scenarios. With this knowledge, you’ll be prepared to leverage KNIME to solve complex business problems using advanced data analytics and machine learning techniques.

Reference