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Machine Learning with Azure

  • Course Code: Data Science - Machine Learning with Azure
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 3 Days Audience: This course is geared for those who wants to Implement machine learning, cognitive services, and artificial intelligence solutions by leveraging Azure cloud technologies

Course Snapshot 

  • Course: Machine Learning with Azure 
  • Duration: 3 days 
  • Skill-level: Foundation-level Linux and azure skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Implement machine learning, cognitive services, and artificial intelligence solutions by leveraging Azure cloud technologies 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Implementing Machine learning (ML) and Artificial Intelligence (AI) in the cloud had not been possible earlier due to the lack of processing power and storage. However, Azure has created ML and AI services that are easy to implement in the cloud. Hands-On Machine Learning with Azure teaches you how to perform advanced ML projects in the cloud in a cost-effective way. The course begins by covering the benefits of ML and AI in the cloud. You will then explore Microsoft’s Team Data Science Process to establish a repeatable process for successful AI development and implementation. You will also gain an understanding of AI technologies available in Azure and the Cognitive Services APIs to integrate them into bot applications. This course lets you explore prebuilt templates with Azure Machine Learning Studio and build a model using canned algorithms that can be deployed as web services. The course then takes you through a preconfigured series of virtual machines in Azure targeted at AI development scenarios. You will get to grips with the ML Server and its capabilities in SQL and HDInsight. In the concluding lessons, you’ll integrate patterns with other non-AI services in Azure. 

Working in a hands-on learning environment, led by our Machine Learning with Azure expert instructor, students will learn about and explore: 

  • Learn advanced concepts in Azure ML and the Cortana Intelligence Suite architecture 
  • Explore ML Server using SQL Server and HDInsight capabilities 
  • Implement various tools in Azure to build and deploy machine learning models 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Discover the benefits of leveraging the cloud for ML and AI 
  • Use Cognitive Services APIs to build intelligent bots 
  • Build a model using canned algorithms from Microsoft and deploy it as a web service 
  • Deploy virtual machines in AI development scenarios 
  • Apply R, Python, SQL Server, and Spark in Azure 
  • Build and deploy deep learning solutions with CNTK, MMLSpark, and TensorFlow 
  • Implement model retraining in IoT, Streaming, and Blockchain solutions 
  • Explore best practices for integrating ML and AI functions with ADLA and logic apps 

Audience & Pre-Requisites 

This course is geared for attendees with basic Linux and computing skills who wish to intending to start practical Machine Learning with azure 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills, Microsoft azure and Machine Learning knowledge 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics

  1. AI Cloud Foundations 
  • The importance of artificial intelligence 
  • The emergence of the cloud 
  • The Microsoft cloud – Azure 
  1. Data Science Process 
  • TDSP stages 
  • Tools for TDSP 
  1. Cognitive Services 
  • Cognitive Services for Vision APIs 
  • The Computer Vision API 
  • Face API 
  • Cognitive Services for Language APIs 
  • Cognitive Services for Speech APIs 
  • Cognitive Services for Knowledge APIs 
  • Cognitive Services for Search APIs 
  1. Bot Framework 
  • What is a bot? 
  • Creating a bot with Bot Service 
  1. Azure Machine Learning Studio 
  • Deploying an Azure AI Gallery template 
  • Building an experiment 
  • Deploying a model as a web service 
  1. Scalable Computing for Data Science 
  • Different scalable compute options in Azure 
  • Introduction to DSVMs 
  • DLVM 
  • Batch AI service 
  • ACI 
  • AKS 
  1. Machine Learning Server 
  • What is Microsoft ML Server? 
  • Machine learning with Python 
  1. HDInsight 
  • R with HDInsight 
  • Getting started with Azure HDInsight and ML services 
  • HDInsight and data analytics with R 
  • Enriching data for analysis 
  1. Machine Learning with Spark 
  • Machine learning with Azure Databricks 
  • Getting started with Apache Spark and Azure Databricks 
  • Using SQL in Azure Databricks 
  • Machine Learning with HDInsight 
  • HDInsight and Spark 
  • Working with data in a Spark environment 
  • Configuring the data science virtual machine 
  • Setting up an HDInsight cluster with Spark 
  1. Building Deep Learning Solutions 
  • What is deep learning? 
  • Overview of the Azure Notebook service 
  • Overview of Azure Deep Learning Virtual Machine toolkits 
  • An overview of the Microsoft Machine Learning Library for Apache Spark (MMLSpark) 
  • Overview of TensorFlow on Azure  
  1. Integration with Other Azure Services 
  • Logic Apps 
  • Azure Functions 
  • Azure Data Lake Analytics 
  • Azure Data Factory 
  1. End-to-End Machine Learning 
  • Using the Azure Machine Learning SDK for E2E machine learning 

Student Materials: Each student will receive a Student Guide with course notes, code samples, software tutorials, diagrams and related reference materials and links (as applicable). Our courses also include step by step hands-on lab instructions and and solutions, clearly illustrated for users to complete hands-on work in class, and to revisit to review or refresh skills at any time. Students will also receive the project files (or code, if applicable) and solutions required for the hands-on work. 

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