This beginner-friendly Azure Machine Learning project introduces learners to building a complete machine learning workflow using Azure ML Designer. The main goal of the project is to predict car prices using a regression model, making it a practical introduction to real-world data science applications in Microsoft Azure.

The course begins by guiding learners through setting up an Azure Machine Learning workspace and configuring compute resources needed for model training. Students then explore a dataset and perform essential data preparation steps such as cleaning, transforming, and preparing data for machine learning.

Next, learners build a regression model using Azure ML Designer’s visual interface. The course explains how to split data into training and testing sets, train the model, and evaluate its performance using key metrics to understand prediction accuracy.

After model training, students create a prediction pipeline to generate real-time price forecasts. The course also covers deploying the trained model as a web service using Azure Container Instances, allowing real-world usage of the model through API endpoints.

This project is designed for beginners with no prior coding experience. It provides step-by-step guidance to help learners understand the full machine learning lifecycle, from data preparation to deployment.

By the end of the project, learners will have built and deployed a working machine learning model capable of predicting car prices using Azure Machine Learning Designer.