Frequently Asked Questions on Lab 3 Lesson - Analyze Your Data

Here, we are going to answer all your questions related to Lab 3 – Analyze Your Data for Crime Analytics & Predictions Labs course.

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Browse through the commonly asked questions below:

Machine Learning Studio is a workbench environment you access through a web browser. Machine Learning Studio hosts a pallet of modules with a visual composition interface that enables you to build an end-to-end, data science workflow in the form of an experiment.

By clicking on the output of a module you can visualize the data and get statistics.

Since the data is being transmitted to the browser and may be large, the data size is limited to prevent slowing down Machine Learning Studio. To visualize all the data/result, it’s better to download the data and use Excel or another tool.

Azure Machine Learning supports a wide range of machine learning models, including supervised and unsupervised learning, deep learning, and reinforcement learning. You can build models for tasks such as classification, regression, clustering, and anomaly detection.

Azure Machine Learning supports a variety of programming languages, including Python, R, and SQL. Python is the most commonly used language for machine learning on Azure, and many of the pre-built Azure Machine Learning tools are designed for Python.

Yes, you can bring your own data to Azure Machine Learning. The platform supports a range of data formats, including CSV, Parquet, and JSON. You can also connect to data sources such as Azure Blob Storage, Azure Data Lake Storage, and SQL Server.

Yes, Azure Machine Learning allows you to deploy your models as web services on the cloud. You can deploy models to Azure Kubernetes Service, Azure Container Instances, or Azure Functions.

While it helps to have some experience with machine learning, Azure Machine Learning provides a range of pre-built tools and services that make it accessible to beginners. Additionally, Azure Machine Learning has a large community of users who share resources and best practices.

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