Perform Cloud Data Science with Azure Machine Learning

Master the art of data analysis and presentation using Azure Machine Learning to elevate your skills and prepare for the 70-774 Microsoft certification, specifically designed for aspiring data scientists and IT professionals.

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Essential Skills Gained

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Explain machine learning principles and usage of algorithms and languages.

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Implement data preprocessing and feature engineering techniques for machine learning.

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Develop and deploy machine learning models using Azure Machine Learning.

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Integrate R and Python into Azure Machine Learning experiments for robust data analysis.

Format

  • Instructor-led
  • 5 days with lectures and hands-on labs.

Audience

  • Aspiring Data Scientists
  • IT Professionals
  • Software Developers
  • Information Workers

Description

The main purpose of the course is to give students the ability to analyse and present data by using Azure Machine Learning, and to provide an introduction to the use of machine learning with big data tools such as HDInsight and R Services. This course also prepares the student for the 70-774 Microsoft certification exam. This class will be taught by a Microsoft Data Platform MVP and Microsoft Certified Trainer with many years of experience implementing customer solutions using the Microsoft Data Stack. This instructor is a key contributor to the 70-774 exam reference book published by Microsoft Press.

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Upcoming Course Dates

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Course Outline

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Module 1: Introduction to Machine Learning

  1. What is machine learning?

  2. Introduction to machine learning algorithms

  3. Introduction to machine learning languages

  4. Lab: Sign up for Azure machine learning studio account

  5. Lab: View a simple experiment from gallery

  6. Lab: Evaluate an experiment

Module 2: Introduction to Azure Machine Learning

  1. Azure machine learning overview

  2. Introduction to Azure machine learning studio

  3. Developing and hosting Azure machine learning applications

  4. Lab: Explore the Azure machine learning studio workspace

  5. Lab: Clone and run a simple experiment

  6. Lab: Clone an experiment, make simple changes and run it

Module 3: Managing Datasets

  1. Categorizing your data

  2. Importing data to Azure machine learning

  3. Exploring and transforming data in Azure machine learning

  4. Lab: Prepare Azure SQL database

  5. Lab: Import data and visualize

  6. Lab: Summarize data

Module 4: Preparing Data for use with Azure Machine Learning

  1. Data pre-processing

  2. Handling incomplete datasets

  3. Lab: Explore data using Power BI

  4. Lab: Clean the data

Module 5: Using Feature Engineering and Selection

  1. Using feature engineering

  2. Using feature selection

  3. Lab: Prepare datasets

  4. Lab: Use Join to Merge data

Module 6: Building Azure Machine Learning Models

  1. Azure machine learning workflows

  2. Scoring and evaluating models

  3. Using regression algorithms

  4. Using neural networks

  5. Lab: Using Azure machine learning studio modules for regression

  6. Lab: Create and run a neural-network based application

Module 7: Using Classification and Clustering with Azure Machine Learning Models

  1. Using classification algorithms

  2. Clustering techniques

  3. Selecting algorithms

  4. Lab: Using Azure machine learning studio modules for classification

  5. Lab: Add k-means section to an experiment

  6. Lab: Use PCA for anomaly detection

  7. Lab: Evaluate the models

Module 8: Using R and Python with Azure Machine Learning

  1. Using R

  2. Using Python

  3. Incorporating R and Python into Machine Learning experiments

  4. Lab: Exploring data using R

  5. Lab: Analyzing data using Python

Module 9: Initializing and Optimizing Machine Learning Models

  1. Using hyper-parameters

  2. Using multiple algorithms and models

  3. Scoring and evaluating Models

  4. Lab: Using hyper-parameters

Module 10: Using Azure Machine Learning Models

  1. Deploying and publishing models

  2. Consuming Experiments

  3. Lab: Deploy machine learning models

  4. Lab: Consume a published model

Module 11: Using Cognitive Services

  1. Cognitive services overview

  2. Processing language

  3. Processing images and video

  4. Recommending products

  5. Lab: Build a language application

  6. Lab: Build a face detection application

  7. Lab: Build a recommendation application

Module 12: Using Machine Learning with HDInsight

  1. Introduction to HDInsight

  2. HDInsight cluster types

  3. HDInsight and machine learning models

  4. Lab: Provision an HDInsight cluster

  5. Lab: Use the HDInsight cluster with MapReduce and Spark

Module 13: Using R Services with Machine Learning

  1. R and R server overview

  2. Using R server with machine learning

  3. Using R with SQL Server

  4. Lab: Deploy DSVM

  5. Lab: Prepare a sample SQL Server database and configure SQL Server and R

  6. Lab: Use a remote R session

  7. Lab: Execute R scripts inside T-SQL statements

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