Book Image

Artificial Intelligence with Power BI

By : Mary-Jo Diepeveen
Book Image

Artificial Intelligence with Power BI

By: Mary-Jo Diepeveen

Overview of this book

The artificial intelligence (AI) capabilities in Power BI enable organizations to quickly and easily gain more intelligent insights from unstructured and structured data. This book will teach you how to make use of the many AI features available today in Power BI to quickly and easily enrich your data and gain better insights into patterns that can be found in your data. You’ll begin by understanding the benefits of AI and how it can be used in Power BI. Next, you’ll focus on exploring and preparing your data for building AI projects and then progress to using prominent AI features already available in Power BI, such as forecasting, anomaly detection, and Q&A. Later chapters will show you how to apply text analytics and computer vision within Power BI reports. This will help you create your own Q&A functionality in Power BI, which allows you to ask FAQs from another knowledge base and then integrate it with PowerApps. Toward the concluding chapters, you’ll be able to create and deploy AutoML models trained in Azure ML and consume them in Power Query Editor. After your models have been trained, you’ll work through principles such as privacy, fairness, and transparency to use AI responsibly. By the end of this book, you’ll have learned when and how to enrich your data with AI using the out-of-the-box AI capabilities in Power BI.
Table of Contents (18 chapters)
Part 1: AI Fundamentals
Part 2: Out-of-the-Box AI Features
Part 3: Create Your Own Models

Training a model with Azure ML Designer

As a data analyst, you may be an aspiring data scientist, but not yet fluent in creating training scripts written in Python while using common libraries such as scikit-learn to train a model. Therefore, we'll keep things approachable and train a model with the visual drag-and-drop interface that's provided by Azure ML: the Azure ML Designer.

Remember that everything that you can do with the Designer can also be replicated by creating scripts and running them with the Python SDK or the Azure CLI.

When you want to train a model with the Designer, there are several common algorithms you can choose from. With these built-in components, you can easily train a regression, classification, or clustering model. In this section, we will train a regression model on the world happiness dataset to fill any empty rows that we have and fix missing data more intelligently than by simply using a mean or median.

In Chapter 2, Exploring Data...