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Learn how to proactively identify threat behaviors by using Microsoft Sentinel queries.

Units in this learning path:

• 1 Introduction
• 2 Exercise Setup
• 3 Query Management Creation
• 4 Bookmarks
• 5 Livestream
• 6 Exercise Hunt for Threats
• 7 Summary

Products: Azure, Microsoft Sentinel

Roles: Solution Architect, Security Operations Analyst

Level: Intermediate

Subjects: Threat Protection

Duration: 65 minutes

Provide an introduction to implementing threat response with Microsoft Sentinel playbooks.

Units in this learning path:

• 1 Introduction
• 2 Exercise Setup
• 3 Playbook Overview
• 4 Playbook Trigger
• 5 Playbook on Demand
• 6 Exercise Create Playbook
• 7 Summary

Products: Azure, Azure Log Analytics, Azure Logic Apps, Microsoft Sentinel

Roles: Security Operations Analyst

Level: Intermediate

Subjects: Threat Protection

Duration: 80 minutes

This module describes how to create, edit, import, and recall time entries, and it explores the various customizations that you can apply.

Units in this learning path:

• Introduction
• Time Entry
• Create Edit Recall Time Entries
• Import Time Entry
• Visualizations Reporting
• Exercise
• Check
• Summary

Products: Dynamics 365, Dynamics Project Operations, Dynamics Finance

Roles: Functional Consultant, Business User

Level: Intermediate

Subjects: Business Applications

Duration: 41 minutes

Do you want to discover how to trace items by their serial and lot numbers? This module explores the Trace and Navigate functions that are used to trace serial and lot numbers in Microsoft Dynamics 365 Business Central.

Units in this learning path:

• 1 Introduction
• 2 Trace
• 3 Navigate
• 4 Exercise
• 5 Check
• 6 Summary

Products: Dynamics 365, Dynamics Business Central

Roles: Functional Consultant, Business User

Level: Intermediate

Subjects: Supply Chain Management

Duration: 24 minutes

Learn how to use MLflow for model tracking when experimenting in notebooks.

Units in this learning path:

• Introduction
• Use Mlflow Model Tracking
• Train Models Notebooks
• Exercise Track Model Training
• Knowledge Check
• Summary

Products: Azure Machine Learning

Roles: Data Scientist

Level: Beginner

Subjects: Machine Learning

Duration: 33 minutes

Learn how to track model training with MLflow in jobs when running scripts.

Units in this learning path:

• Introduction
• Track Metrics Mlflow
• View Metrics Evaluate Models
• Exercise Use Mlflow Track Training Jobs
• Knowledge Check
• Summary

Products: Azure Machine Learning

Roles: Data Scientist

Level: Beginner

Subjects: Machine Learning

Duration: 34 minutes

In this course, you will learn the benefits and technical concepts of trails for AWS CloudTrail. Using trails for CloudTrail, you can archive, analyze, and respond to changes in your Amazon Web Services (AWS) resources. A trail is a configuration…

Learn how to train machine learning models using Spark and the MLlib library in Azure Databricks.

Units in this learning path:

• Introduction
• Understand Machine Learning
• Machine Learning Azure Databricks
• Prepare Data for Machine Learning
• Train Model
• Evaluate Model
• Exercise Machine Learning Databricks
• Knowledge Check
• Summary

Products: Azure Databricks

Roles: Data Scientist

Level: Intermediate

Subjects: Data Management, Machine Learning

Duration: 84 minutes

Train and evaluate classification models

Units in this learning path:

• Introduction
• What Is Classification
• Exercise Model
• Evaluate Classification Models
• Exercise Alternative Classification Metrics
• Multiclass Classification
• Exercise Multiclass Classification
• Knowledge Check
• Summary

Products: Azure

Roles: Data Scientist

Level: Intermediate

Subjects: Machine Learning, Classification Analysis

Duration: 64 minutes

Clustering is a type of machine learning that is used to group similar items into clusters.

Units in this learning path:

• Introduction
• What Is Clustering
• Exercise Model
• Different Types Clustering
• Exercise New Models
• Knowledge Check
• Summary

Products: Azure

Roles: Data Scientist

Level: Intermediate

Subjects: Machine Learning

Duration: 46 minutes

Train and evaluate deep learning models

Units in this learning path:

• Introduction
• Deep Neural Network Concepts
• Convolutional Neural Networks
• Transfer Learning
• Exercise Deep Learning
• Knowledge Check
• Summary

Products: Azure

Roles: Data Scientist

Level: Advanced

Subjects: Machine Learning

Duration: 47 minutes

Regression is a commonly used kind of machine learning for predicting numeric values.

Units in this learning path:

• Introduction
• What Is Regression
• Exercise Model
• Discover New Regression Models
• Exercise Powerful Models
• Improve Models
• Exercise Optimize Save Models
• Knowledge Check
• Summary

Products: Azure

Roles: Data Scientist

Level: Intermediate

Subjects: Machine Learning

Duration: 69 minutes