• Machine Learning uses real data to train algorithms that can be

• used for anomaly detection, computer vision, and natural language
• processing. In this course, you'll learn about datasets and how to
• manipulate data for them. Next, you'll learn the difference between
• labeled and unlabeled data and why some AI models require labeled
• data. You'll examine the features that should be used for a
• selected dataset. Next, you'll learn about the types of machine
• learning algorithms that are available, including regression
• algorithms, classification algorithms, and clustering algorithms.
• Finally, you'll explore the difference between supervised and
• unsupervised machine learning models. This course is one in a
• collection that prepares learners for the Designing and
• Implementing a Data Science Solution on Azure (DP-100) exam.

• Azure Machine Learning Studio can be used to create and train

• machine learning models. Support is provided for multiple
• development tools, programming languages such as Python and R, and
• numerous machine learning frameworks. In this course, you'll learn
• about the services provided by the Azure Machine Learning Studio,
• how to create an Azure account, and how to register and signup to
• use Azure Machine Learning Studio. You'll also explore available
• Azure Machine Learning Studio components, which can be used to
• create machine learning workflows, ingest data from an Azure Blob
• storage resource, create and use an Azure Machine Learning
• workspaces, and create and use a compute resource. This course is
• one in a collection that prepares learners for the Designing and
• Implementing a Data Science Solution on Azure (DP-100) exam.

• Machine learning regression models are used to predict numeric

• labels for the features of an item. In this course, you'll learn
• more about using regression models in the Azure Machine Learning
• Studio. First, you'll learn about why regression models are used,
• the available types of regression models in machine learning, and
• the steps required to train a regression model. Next, you'll
• examine the best metrics for determining which regression model to
• use. You'll learn how to use a subset of data to train the
• regression model and run the training pipeline. Finally, you'll
• explore how to use an existing pipeline to create a new inference
• pipeline and create and deploy a predictive service. This course is
• one in a collection that prepares learners for the Designing and
• Implementing a Data Science Solution on Azure (DP-100) exam.

• Machine learning classification models are used to predict the

• class or category that an item belongs to. For example, using
• patient characteristics such as age, weight, and BMI to predict if
• they are at risk for specific diseases. In this course, you'll
• learn about using classification models in the Azure Machine
• Learning Studio. You'll explore the available types of
• classification models and the steps required to train a
• classification model. Next, you'll learn the ideal metrics for
• determining the best classification model to use for the given
• data. Finally, you'll examine how to use an existing pipeline to
• create a new inference pipeline and create and deploy a predictive
• service for a classification model. This course is one in a
• collection that prepares learners for the Designing and
• Implementing a Data Science Solution on Azure (DP-100) exam.

• Machine learning clustering models are used to group similar

• items based on their features and use unsupervised learning. In
• this course, you'll learn about using clustering models in the
• Azure Machine Learning Studio. First, you'll explore the available
• types of clustering models in Azure Machine Learning Studio and the
• steps required to train a clustering model. Next, you'll learn how
• to train and evaluate a clustering model. Next, you'll examine how
• to create a K-means clustering model in Azure Machine Learning
• Studio. Finally, you'll learn how to create and deploy a new
• inference pipeline to create a predictive service for a clustering
• model. This course is one in a collection that prepares learners
• for the Designing and Implementing a Data Science Solution on Azure
• (DP-100) exam.

• Data scientists spend a majority of their time exploring and

• analyzing data, which may become the foundation for a machine
• learning model. The Azure Machine Learning Studio provides Jupyter
• Notebooks that can be used to perform data analysis. In this
• course, you'll learn about project Jupyter and how it is used for
• by data scientists to perform data analysis. You'll explore how to
• create a compute instance in Azure Machine Learning Studio and
• clone a sample training repository. Next, you'll learn how to use a
• Jupyter Notebook to perform basic data analysis and , create a
• regression model, classification model, and clustering model.
• Finally, you'll examine how to use Jupyter Notebook to perform deep
• learning using PyTorch and perform deep learning using TensorFlow.
• This course is one in a collection that prepares learners for the
• Designing and Implementing a Data Science Solution on Azure
• (DP-100) exam.

• Azure Machine Learning workspaces provide an environment for

• performing experiments and managing data, computer targets, and
• other assets. Other assets can include notebooks, pipelines, and
• trained models. This course will focus on using the Azure Machine
• Learning SDK. In this course, you'll learn to create an Azure
• Machine Learning workspace by creating a machine learning
• resources, creating compute resources, and cloning a notebook.
• Next, you'll examine how to install the Machine Learning SDK for
• Python and create code to connect to a workspace. You'll learn to
• create Python scripts to run an experiment, log metrics, and
• retrieve and view logged metrics. Finally, you'll examine how to
• use the Azure Machine Learning SDK to run code experiments, create
• a script to train a model, and run a notebook using Jupyter to
• train predictive models. This course is one in a collection that
• prepares learners for the Designing and Implementing a Data Science
• Solution on Azure (DP-100) exam.

• One of the key components of Azure Cloud platform is the ability

• to store and process large amounts of data. Azure provides several
• data platforms for stored data and numerous services for processing
• data. In this course, you'll explore the differences between
• structured and unstructured data. You'll learn about some of the
• available data storage platforms, including Azure SQL Database,
• Azure Cosmos DB, Azure Data Storage, and Azure Data Lake Storage
• Gen2. In addition, you'll learn about the data processing services
• such as Azure Synapse Analytics, Azure Stream Analytics, Azure
• Databricks, Azure Data Factory, and Azure HDInsight, which are all
• available to perform operations on the data stored in each of the
• data platforms. This course is one in a collection that prepares
• learners for the Designing and Implementing a Data Science Solution
• on Azure (DP-100) exam.

The Azure Cloud platform provides the ability to store various types of data. Azure provides the ability to store data blobs, files, tabular data, and data using a queue. In this course, you'll learn about Azure storage accounts and why they are needed. Next, you'll explore options for storing data using Azure storage containers, Azure file shares, Azure Table storage, and Azure Queue storage. Next, you'll learn about the tools that can be used to create your Azure storage account. Finally, you'll examine how to create Azure storage accounts, containers, and file shares, as well as Azure Table and Queue storage. This course is one in a collection that prepares learners for the Designing and Implementing a Data Science Solution on Azure (DP-100) exam.

• When using the Azure Cloud platform for storing data, strategies

• must be devised to ensure that the storage solution is the best fit
• for the data that is being stored. In this course, you'll learn
• strategies for determining the best Azure Storage option based on
• the type of data being stored and other factors. Next, you'll
• explore the differences between structured and unstructured data
• types and strategies and mechanisms for securing storage account
• data. Finally, you'll learn how to encrypt and decrypt blobs using
• the Azure Key Vault, how to create a virtual machine using an Azure
• Storage Account, how to upload data to Azure Storage in parallel,
• how to download data from Azure Storage, and how to configure
• metrics to monitor data throughput. This course is one in a
• collection that prepares learners for the Designing and
• Implementing a Data Science Solution on Azure (DP-100) exam.

• Once you have data in storage, you'll need to have some

• mechanism for transforming the data into a usable format. Azure
• Data Factory is a data integration service that is used to create
• automated data pipelines that can be used to copy and transform
• data. In this course, you'll learn about the Azure Data Factory and
• the Integration Runtime. Next, you'll explore the features of the
• Azure Data Factory, such as linked services and datasets, pipelines
• and activities, and triggers. Finally, you'll learn how to create
• an Azure Data Factory using the Azure portal, Azure Data Factory
• Linked services and datasets, and Azure Data Factory pipelines and
• activities, as well as how to trigger a pipeline manually or using
• a schedule. This course is one in a collection that prepares
• learners for the Designing and Implementing a Data Science Solution
• on Azure (DP-100) exam.

• Unstructured data is prevalent in business and needs specific

• datastores for storing and managing this type of data. Azure
• provides several database systems that fulfill these needs. In this
• course, you'll learn about the types of non-relational data and
• available Azure non-relational datastores. Next, you'll explore
• Azure Cosmos DB and the available API models that can be used with
• it. Next, you'll learn about the features and how to work with
• Azure data blobs and Azure Data Lake Storage Gen2. Finally, you'll
• examine how to secure data by using dynamic data masking and
• encrypting data at rest and in motion in Azure. This course is one
• in a collection that prepares learners for the Designing and
• Implementing a Data Science Solution on Azure (DP-100) exam.

• Azure Machine Learning Studio can make use of various types of

• data stores and datasets for training and testing data. In this
• course, you'll learn about the types of data stores that are
• available in Azure, including Azure Storage (blob and file
• containers), Azure Data Lake stores, Azure SQL Database, and Azure
• Databricks file system. Next, you'll explore how to create and
• register data stores and the types of datasets that can be created.
• Next, you'll learn how to run a notebook using Jupyter to work with
• data, data stores, and datasets, as well as how to create a compute
• cluster. You'll examine the available compute targets such as local
• compute, compute clusters, and attached compute, as well as the
• types of environments. Finally, you'll learn to create and manage a
• compute instance and a compute cluster in the Azure Machine
• Learning workspace. This course is one in a collection that
• prepares learners for the Designing and Implementing a Data Science
• Solution on Azure (DP-100) exam.

• Azure Machine Learning Studio provides DevOps support in the

• form of orchestrating machine learning pipelines. In this course,
• you'll learn to create, publish, and schedule machine learning
• pipelines. First, you'll examine Azure Machine Learning pipelines
• and how they are used to build, optimize, and manage machine
• learning workflows. Next, you'll explore how to use the Azure
• Machine Learning SDK to create and run machine learning pipelines.
• You'll learn how to use a pipeline to import, transform, and move
• data between steps, as well as how to publish and track pipelines
• and use triggers to schedule a machine learning pipeline based on
• some event. Finally, you'll learn techniques for troubleshooting
• and debugging machine learning pipelines This course is one in a
• collection that prepares learners for the Designing and
• Implementing a Data Science Solution on Azure (DP-100) exam.

• The Azure Machine Learning SDK provides components to quantity

• the importance of features, identify bias in models, and determine
• differential privacy. In this course, you'll learn more about these
• features and how they can be used to increase the quality of your
• machine learning models. First, you'll examine how models can use
• global and local features to quantify the importance of each model
• feature. You'll explore how model explainers can be created using
• the Azure Machine Learning SDK and how to visualize the model using
• the Azure Machine Learning Studio. Next, you'll learn how to use a
• Jupyter Notebook and Python to generate explanations that are part
• of a model training experiment. Finally, you'll learn about
• training model bias and how to analyze model fairness using the
• Fairlearn Python package to detect and mitigate unfairness in a
• trained model. This course is one in a collection that prepares
• learners for the Designing and Implementing a Data Science Solution
• on Azure (DP-100) exam.

• Being able to monitor and analyze an Azure Machine Learning web

• service is crucial to determining the correctness of the server.
• Azure Machine Learning Studio provides the tools required to
• perform this monitoring and analysis. In this course, you'll learn
• how application insights can be used to monitor an Azure Machine
• Learning web service, as well as to capture and review telemetry
• data. Next, you'll examine how to create a data drift monitor and
• schedule it to run using Jupyter Notebook and Python. You'll
• explore problems relating to data privacy and how differential
• privacy works. Finally, you'll learn how to use SmartNoise to
• generate and submit differentially private queries. This course is
• one in a collection that prepares learners for the Designing and
• Implementing a Data Science Solution on Azure (DP-100) exam.

• Being able to monitor data storage system to ensure they are

• operational and working correctly is a crucial part of running your
• business. Azure provides the Azure Monitor service and the Azure
• Log Analytics service to perform this function. In this course, you
• will learn about the features Azure Log Analytics and the Azure
• Monitor service and how it can be used to monitor storage data and
• monitor Azure Blob storage. Next, you'll learn how to access
• diagnostic logs to monitor Data Lake Storage Gen2, how to monitor
• the Azure Synapse Analytics jobs and the adaptive cache and how to
• monitor the Azure Cosmos DB using the portal and resource logs.
• Finally, you'll learn how to configure, manage, and view metric
• alerts using the Azure Monitor and how to configure, manage, and
• view activity log alerts using the Azure Monitor. This course is
• one in a collection that prepares learners for the Designing and
• Implementing a Data Science Solution on Azure (DP-100) exam.

• Being able to monitor data processes to ensure they are

• operational and working correctly is a crucial part of running your
• business. Azure provides the Azure Monitor and Azure Log Analytics
• services to perform this function. In this course, you'll learn
• about the features of the Azure Monitor tools and the concepts of
• continuous monitoring and visualization. You'll explore how to
• create metric charts using the Azure Monitor, collect and analyze
• Azure resource log data, and perform queries against the Azure
• Monitor logs. Next, you'll examine how to create and share
• dashboards that display data from Log Analytics, create Azure
• Monitor alerts, and use the Azure Data Factory Analytics solution
• to monitor pipelines. Finally, you'll learn how to query Azure Log
• Analytics and filter, sort, and group query results. This course is
• one in a collection that prepares learners for the Designing and
• Implementing a Data Science Solution on Azure (DP-100) exam.

• Ensuring that data storage and processing systems are operating

• efficiently will allow your organization to save both time and
• money. There are several tips and tricks that can be used to
• optimize both Azure Data Storage service and processes. In this
• course, you'll learn about cloud optimization and best practices
• for optimizing data using data partitions, Azure Data Lake Storage
• tuning, Azure Synapse Analytics tuning, and Azure Databricks
• auto-optimizing. You'll examine strategies for partitioning data
• using Azure-based storage solutions. Next, you'll explore the
• stages of the Azure Blob lifecycle management and how to optimize
• Azure Data Lake Storage Gen2, Azure Stream Analytics, and Azure
• Synapse Analytics. Finally, you'll learn about optimizing Azure
• Data Storage services such Azure Cosmos DB using indexing and
• partitioning, as well as Azure Blob Storage and Azure Databricks.
• This course is one in a collection that prepares learners for the
• Designing and Implementing a Data Science Solution on Azure
• (DP-100) exam.

• Organizations rely on systems and data to be available and

• operational when they are needed to manage and run their
• businesses. Azure provides functionality to ensure high
• availability of storage systems and the mechanism to ensure a swift
• and painless disaster recovery strategy. In this course, you'll
• learn about high availability and disaster recovery, and how they
• are related to each other and used to provide business continuity.
• Next, you'll examine how to back up, store, and restore SQL Server
• databases on virtual machine instances. You'll move on to explore
• the purpose and features of Azure Always On availability groups and
• elastic pools, as well as how they are used by Azure SQL databases
• to provide business continuity. Finally, you'll learn about using
• Azure Database for PostgreSQL - Hyperscale to create highly
• available and distributed databases. This course is one in a
• collection that prepares learners for the Designing and
• Implementing a Data Science Solution on Azure (DP-100) exam.