AI Features in Power BI | Complete Tutorial with Real Examples | Techcanvass

In this session, we explore AI features in Power BI and how Artificial Intelligence is transforming modern Business Intelligence (BI) tools. If you want to understand what is Power BI, how to use Power BI, and see real Power BI examples, this webinar will guide you through practical demonstrations.

You will learn how AI in Power BI helps analysts automate data preparation, generate insights, forecast trends, and perform advanced analysis without complex coding. We also demonstrate key capabilities like Fuzzy Matching, Quick Insights, Forecasting, Key Influencers, Word Cloud analysis, and Natural Language Query (Q&A).

This video is useful for:
* Business Analysts
* Data Analysts
* Power BI beginners
* Professionals exploring AI in Power BI
* Anyone looking for practical Power BI examples

⏱ Video Timestamps

00:00 – Webinar Introduction: AI Features in Power BI
01:00 – How AI Works (Data Collection → Model Training → Deployment)
02:00 – Introduction to Data Analytics
03:00 – Types of Data Analytics
04:00 – Diagnostic Analytics Explained
05:00 – Understanding Why Events Happened in Data
06:00 – Predictive Analytics Explained
07:00 – Prescriptive Analytics Explained
08:00 – Examples of Prescriptive Analytics
09:00 – AI vs Machine Learning
10:00 – Introduction to AI in Power BI
11:00 – Prerequisites for Using AI Features in Power BI
12:00 – AI-Enhanced Data Preparation Overview
13:00 – Automatic Data Type Detection in Power BI
14:00 – Handling Missing Data Using AI
15:00 – Data Cleaning Techniques in Power BI
16:00 – Data Preparation Best Practices
17:00 – Introduction to Fuzzy Matching
18:00 – Power BI Interface Overview
19:00 – Understanding the Example Dataset
20:00 – Data Model & Relationships in Power BI
21:00 – Creating and Managing Table Relationships
22:00 – Category Data Structure Explained
23:00 – Data Modeling Concepts
24:00 – Preparing Data for Visualization
25:00 – Building Category-Wise Sales Visualization
26:00 – Identifying Category Mapping Issues
27:00 – Data Inconsistency in Categories
28:00 – Solving Data Mismatch with Fuzzy Matching
29:00 – Data Cleaning with AI in Power BI
30:00 – How Fuzzy Matching Works in Power BI
31:00 – Applying Data Transformation Steps
32:00 – Applying Fuzzy Matching to Fix Data Issues
33:00 – Verifying Cleaned Data Results
34:00 – Fixing Category Mapping in Visualization
35:00 – Introduction to AI-Powered Data Analysis
36:00 – Preparing Visualizations for AI Analysis
37:00 – Creating a Column Chart for Analysis
38:00 – Using AI to Analyze Visualizations
39:00 – AI Insights Generated from Data
40:00 – Top Customers with the Highest Returns
41:00 – AI-Generated Insights Summary
42:00 – Moving to Power BI Service
43:00 – Understanding AI-Generated Insights
44:00 – Payment Methods & Discount Insights
45:00 – Employee Distribution by Store Size
46:00 – Introduction to Predictive Analytics
47:00 – Data Requirements for Forecasting
48:00 – Creating Sales Forecast in Power BI
49:00 – Setting Year and Month in X-Axis
50:00 – Preparing Data for Forecasting
51:00 – Forecast Visualization Setup
52:00 – Data Loading for 5-Year Sales Dataset
53:00 – Building the Sales Trend Line Chart
54:00 – Enabling Forecast Feature in Power BI
55:00 – Viewing Future Sales Predictions
56:00 – Understanding Confidence Interval
57:00 – Interpreting Forecast Results
58:00 – Time Series Prediction Without Coding
59:00 – Introduction to Root Cause Analysis
01:00:00 – Creating a New Visualization in Power BI
01:01:00 – Introduction to Key Influencers Visualization
01:02:00 – Adding Fields to “Explain By” Section
01:03:00 – Machine Learning Processing in Power BI
01:04:00 – Key Influencers Results Generated
01:05:00 – Regional Sales Insights
01:06:00 – Factors Decreasing Total Sales
01:07:00 – Customer Type Impact on Sales
01:08:00 – Store Size Impact on Sales
01:09:00 – Category-Based Customer Segmentation
01:10:00 – Root Cause Analysis Summary
01:11:00 – Introduction to Text Analysis from Customer Feedback
01:12:00 – Using Word Cloud Visualization
01:13:00 – Creating Word Cloud from Customer Reviews
01:14:00 – Understanding Stop Words in NLP
01:15:00 – Cleaned Word Cloud Insights
01:16:00 – NLP-Based Text Analysis Explained
01:17:00 – Introduction to Q&A Visual
01:18:00 – Enabling Q&A in Power BI Settings
01:19:00 – Q&A Feature Changes in Latest Power BI Version
01:20:00 – Setting Up Q&A Configuration
01:21:00 – Creating Synonym: Revenue for Total Sales
01:22:00 – Training the Q&A Model with Sample Questions
01:23:00 – Q&A Feature Generating Results
01:24:00 – Saving Natural Language Queries
01:25:00 – Final Summary of AI Features in Power BI
01:26:00 – Conclusion of the Presentation

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