Description

Introduction of AI and Machine Learning in Qlik:

Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized the way businesses approach data analysis and decision-making. “AI and Machine Learning in Qlik: Unlocking the Power of Predictive Analytics” is a specialized course designed for professionals who want to integrate AI and ML capabilities into Qlik’s business intelligence platform. This course will provide you with the skills to harness advanced AI and ML techniques to enhance predictive analytics and drive strategic insights.

Participants will learn how to build and deploy machine learning models, integrate AI-driven analytics into Qlik, and create powerful visualizations that translate complex predictions into actionable business intelligence. By the end of the course, you’ll be able to leverage AI and ML in Qlik to unlock deeper insights and support data-driven decision-making.

Prerequisites

To benefit fully from this course, participants should have:

1. Basic Knowledge of Qlik: Familiarity with Qlik Sense or QlikView, including experience with data visualization and dashboard creation.
2. Understanding of AI and ML Concepts: Basic knowledge of AI and machine learning principles, including supervised and unsupervised learning techniques.
3. Data Handling Skills: Experience in data preparation, transformation, and integration within Qlik.
4. Programming Knowledge: Basic proficiency in programming languages like Python or R, particularly for building and integrating machine learning models.

Table of Contents

1: Introduction to AI and ML in Qlik
1.1 Overview of AI and Machine Learning in Business Intelligence
1.2 Role of Qlik in AI and ML Analytics
1.3 Course Objectives and Structure

2: Setting Up AI and ML Environments
2.1 Configuring Python/R for AI and ML Integration
2.2 Installing Required Libraries and Packages
2.3 Connecting Qlik to AI and ML Environments

3: Data Preparation for AI and ML Models
3.1 Importing and Preparing Data in Qlik for Machine Learning
3.2 Data Cleaning, Transformation, and Feature Engineering
3.3 Case Study: Preparing Data for ML Modeling

4: Building Machine Learning Models
4.1 Overview of Machine Learning Algorithms (e.g., regression, classification, clustering)
4.2 Implementing ML Models in Python/R
4.3 Integrating Model Results with Qlik
4.4 Case Study: Building and Evaluating a Machine Learning Model

5: Integrating AI and ML with Qlik
5.1 Importing ML Model Outputs into Qlik
5.2 Automating Model Predictions and Updates
5.3 Using Qlik’s Data Integration Features for ML Analytics

6: Visualizing AI and ML Insights in Qlik
6.1 Designing Visualizations for AI and ML Results
6.2 Creating Interactive Dashboards to Display Predictions and Insights
6.3 Case Study: Visualizing Machine Learning Outputs in Qlik

7: Advanced AI and ML Techniques
7.1 Advanced Machine Learning Techniques and Their Application
7.2 Leveraging AI for Enhanced Predictive Analytics
7.3 Customizing Qlik Visualizations for Complex ML Insights

8: Real-World Applications and Use Cases
8.1 Case Study 1: Predictive Maintenance and Operational Efficiency
8.2 Case Study 2: Customer Segmentation and Personalization
8.3 Case Study 3: Fraud Detection and Risk Management

9: Troubleshooting and Optimization
9.1 Common Issues and Solutions in AI and ML Integration with Qlik
9.2 Performance Optimization for AI and ML Models and Dashboards
9.3 Troubleshooting Integration and Visualization Challenges

10: Final Project and Course Review
10.1 Final Project: Developing an AI and ML-Enhanced Dashboard in Qlik
10.2 Recap of Key Concepts and Techniques
10.3 Q&A Session and Next Steps for Continued Learning

This course equips you with the skills to integrate AI and ML models into Qlik, visualize predictions, and build data-driven dashboards. The final project consolidates all concepts, helping you design a real-world AI-enhanced dashboard for business intelligence.

If you are looking for customized info, Please contact us here

Reference for AI

Reference for ML