Power bi with AI

Step 1: Master Power BI Basics

Key Skills: Data modeling, DAX (Data Analysis Expressions), and report creation.
Resources: Beginner Power BI courses and official Microsoft documentation.
Step 2: Understand AI Capabilities in Power BI

AI Features:
Cognitive Services integration.
AI visuals like Key Influencers and Decomposition Tree.
Using AutoML for predictive analytics.
Tools to Learn: Power BI Desktop, Power Query, and the AI Builder in Microsoft Power Platform.
Step 3: Learn Key AI and Data Science Concepts

Key Topics:
Machine learning basics: regression, classification, and clustering.
AI concepts: NLP, computer vision, and predictive modeling.
Resources: Python, R, or Azure Machine Learning Studio.
Step 4: Build Hands-On Projects

Beginner projects:
Sales forecasting dashboards.
Sentiment analysis integration with Power BI.
Advanced projects:
AI-powered customer churn prediction and visualization.
Real-time anomaly detection in business processes.
Step 5: Gain Certifications

Certifications to Consider:
Microsoft Power BI Data Analyst Associate.
Microsoft Certified: Azure AI Engineer Associate.
Microsoft Certified: Data Scientist Associate.
Step 6: Develop Integration Skills

Learn to connect AI tools and data science workflows with Power BI using:
Python and R scripts for advanced analytics.
Azure Machine Learning for deploying models into Power BI.
Step 7: Build a Portfolio

Showcase projects integrating Power BI with AI models, such as:
Predictive dashboards.
AI-based data storytelling applications.
Use GitHub or Power BI Service to demonstrate your work.
Step 8: Stay Updated and Network

Follow Power BI and AI updates through Microsoft blogs and forums.
Join Power BI and AI user groups to exchange knowledge and trends.
Step 9: Apply AI to Real-World Business Problems

Learn to identify key business challenges and design AI-enhanced Power BI solutions.
Emphasize practical use cases like predictive analytics, optimization, and automation.
Step 10: Apply and Grow