Description

Introduction

The L2: Advanced Proficiency in KNIME Analytics Platform course is designed for professionals looking to deepen their expertise in KNIME and leverage its full capabilities for advanced data analytics, machine learning, and workflow automation. This training will empower you to handle complex data scenarios, integrate diverse data sources, build advanced models, and optimize workflows. KNIME, an open-source platform for data science, provides tools for data preparation, processing, and analysis, making it a go-to platform for professionals across industries. This course takes a hands-on approach to building complex analytical workflows, advanced modeling techniques, and operationalizing machine learning models.

Prerequisites

1. Basic knowledge of KNIME and its user interface.
2. Understanding of data analytics and machine learning principles.
3. Familiarity with basic KNIME nodes, workflows, and simple model building.
4. Experience working with structured and unstructured data.
5. Knowledge of Python or R for advanced integration with KNIME (recommended but not mandatory).

Table of Contents

1. Advanced Data Handling and Preprocessing
   1.1 Handling Missing and Inconsistent Data
   1.2 Advanced Data Transformation Techniques
   1.3 Data Blending and Integration from Multiple Sources
   1.4 Preprocessing for Time-Series and Text Data
2. Working with Unstructured Data
   2.1 Text Mining with KNIME: Introduction to NLP
   2.2 Sentiment Analysis and Topic Modeling
   2.3 Image and Video Processing in KNIME(Ref: KNIME Extensions: Enhancing Capabilities with Custom Nodes and Integrations)
   2.4 Audio Analysis and Signal Processing
3. Advanced Machine Learning in KNIME
   3.1 Building Complex Predictive Models
   3.2 Hyperparameter Optimization Techniques
   3.3 Handling Imbalanced Datasets with KNIME
   3.4 Advanced Ensemble Methods: Bagging, Boosting, and Stacking
4. Deep Learning with KNIME
   4.1 Introduction to Deep Learning Nodes in KNIME
   4.2 Building Neural Networks and Convolutional Networks
   4.3 Integrating KNIME with Keras and TensorFlow
   4.4 Fine-tuning and Optimizing Deep Learning Models
5. Automating and Scaling Workflows
   5.1 Workflow Automation and Scheduling in KNIME
   5.2 Using KNIME Server for Collaboration and Distribution
   5.3 Scaling Workflows with Parallel Processing
   5.4 Integrating KNIME with Cloud Platforms (AWS, Azure)
6. Advanced Model Evaluation and Validation
   6.1 Cross-Validation and Performance Metrics for Advanced Models
   6.2 Handling Model Overfitting and Underfitting
   6.3 Model Interpretability and Explainability (SHAP, LIME)
   6.4 Error Analysis and Refining Models
7. Data Visualization and Reporting in KNIME
   7.1 Advanced Visualization Tools and Techniques
   7.2 Creating Interactive Dashboards and Reports
   7.3 Integrating KNIME Reports with Business Intelligence Tools
   7.4 Visualization for Geospatial Data
8. Deployment and Operationalization of Models
   8.1 Deploying Models to Production Environments
   8.2 Operationalizing Machine Learning Models in KNIME
   8.3 Monitoring and Maintaining Deployed Models
   8.4 Exporting Models to REST APIs for Real-Time Use
9. Integrating KNIME with External Tools
   9.1 Integrating KNIME with Python and R Scripts
   9.2 Connecting KNIME to Databases, APIs, and Web Services
   9.3 Using KNIME for ETL and Data Pipeline Automation
   9.4 Building Custom Extensions and Plugins in KNIME
10. Advanced KNIME Case Studies and Use Cases
    10.1 Predictive Maintenance with Sensor Data
    10.2 Customer Segmentation and Personalization
    10.3 Financial Forecasting and Risk Management
    10.4 Fraud Detection and Anomaly Detection in Large Datasets
11. Capstone Project and Hands-On Application
    11.1 Developing a Complex Workflow from Scratch
    11.2 Addressing a Real-World Business Problem
    11.3 Final Presentation and Discussion

Conclusion

The L2: Advanced Proficiency in KNIME Analytics Platform course prepares professionals to use KNIME for advanced data science tasks, from predictive modeling and machine learning to deploying scalable data solutions. By mastering the advanced features and workflows in KNIME, participants will be able to tackle complex data challenges, create end-to-end solutions, and leverage KNIME’s full potential in real-world scenarios. Whether you’re working with structured data or complex unstructured data like text, images, or audio, this training equips you with the knowledge and skills to become a proficient KNIME user and data science expert.

Reference