Coursera

Snehan Kekre

This course offers a hands-on approach to building machine learning pipelines using Azure ML Studio. You will learn to pre-process data, train and evaluate models, and deploy them as web services. The course is designed to provide practical experience with real-world tasks, boosting your confidence in using the latest tools and technologies.

Machine Learning Pipelines with Azure ML Studio

An overview of the project and its objectives, setting the stage for the tasks ahead.

Introduction and Project Overview

Learn how to clean and prepare data for machine learning models.

Data Cleaning

Understand how to handle class imbalance in datasets to improve model performance.

Accounting for Class Imbalance

Train a Two-Class Boosted Decision Tree model and learn how to tune its hyperparameters for optimal performance.

Training a Two-Class Boosted Decision Tree Model and Hyperparameter Tuning

Evaluate the trained model's performance using test data and scoring metrics.

Scoring and Evaluating the Models

Deploy the trained model as a web service, enabling real-time predictions.

Publishing the Trained Model as a Web Service for Inference

In this project-based course, you will build an end-to-end machine learning pipeline in Azure ML Studio without writing code. Gain practical experience by deploying a trained model as an Azure web service, enhancing your skills in applied machine learning.

Individuals who are new to machine learning and want to learn how to build pipelines using Azure ML Studio.

Beginners in Machine Learning

People interested in data science who want to gain practical experience with machine learning workflows.

Data Science Enthusiasts

IT professionals looking to enhance their skills in deploying machine learning models as web services.

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Machine Learning Pipelines with Azure ML Studio

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