Course Description
Module 1: Introduction to Machine Learning
This module introduces machine learning and discussed how algorithms and languages are used.
Lessons
- What is machine learning?
- Introduction to machine learning algorithms
- Introduction to machine learning languages
Lab : Introduction to machine Learning
- Sign up for Azure machine learning studio account
- Run a simple experiment from gallery
- Evaluate an experiment
After completing this module, students will be able to:
- Describe machine learning
- Describe machine learning algorithms
- Describe machine learning languages
Module 2: Introduction to Azure Machine Learning
Describe the purpose of Azure Machine Learning, and list the main features of Azure Machine Learning Studio.
Lessons
- Azure machine learning overview
- Introduction to Azure machine learning studio
- Developing and hosting Azure machine learning applications
Lab : Introduction to Azure machine learning
- Explore the Azure machine learning studio workspace
- Clone and run a simple experiment
- Clone an experiment, make some simple changes, and run the experiment
After completing this module, students will be able to:
- Describe Azure machine learning.
- Use the Azure machine learning studio.
- Describe the Azure machine learning platforms and environments
Module 3: Managing Datasets
At the end of this module the student will be able to upload and explore various types of data in Azure machine learning.
Lessons
- Categorizing your data
- Importing data to Azure machine learning
- Exploring and transforming data in Azure machine learning
Lab : Visualizing Data
- Prepare Azure SQL database
- Import data
- Visualize data
- Summarize data
After completing this module, students will be able to:
- Understand the types of data they have
- Upload data from a number of different sources
- Explore the data that has been uploaded
Module 4: Preparing Data for use with Azure Machine Learning
This module provides techniques to prepare datasets for use with Azure machine learning.
Lessons
- Data pre-processing
- Handling incomplete datasets
Lab : Preparing data for use with Azure machine learning
- Explore some data using Power BI
- Clean the data
After completing this module, students will be able to:
- Pre-process data to clean and normalize it
- Handle incomplete datasets
Module 5: Using Feature Engineering and Selection
This module describes how to explore and use feature engineering and selection techniques on datasets that are to be used with Azure machine learning.
Lessons
- Using feature engineering
- Using feature selection
Lab : Using feature engineering and selection
- Merge datasets
- Use PCA to reduce dimensions
- Select some variables and edit metadata
After completing this module, students will be able to:
- Use feature engineering to manipulate data
- Use feature selection
Module 6: Building Azure Machine Learning Models
This module describes how to use regression algorithms and neural networks with Azure machine learning.
Lessons
- Azure machine learning workflows
- Scoring and evaluating models
- Using regression algorithms
- Using neural networks
Lab : Building Azure machine learning models
- Using Azure machine learning studio modules for regression
- Evaluate machine learning models
- Add further regression models
- Create and run a neural-network based application
After completing this module, students will be able to:
- Describe machine learning workflows.
- Explain scoring and evaluating models
- Describe regression algorithms
- Use a neural-network
Module 7: Using Classification and Clustering with Azure machine learning models
This module describes how to use classification and clustering algorithms with Azure machine learning.
Lessons
- Using classification algorithms
- Clustering techniques
- Selecting algorithms
Lab : Using classification and clustering with Azure machine learning models
- Using Azure machine learning studio modules for classification.
- Add k-means section to an experiment
- Add PCA for anomaly detection.
- Evaluate the models
After completing this module, students will be able to:
- Use classification algorithms
- Describe clustering techniques
- Select appropriate algorithms
Module 8: Using R and Python with Azure Machine Learning
This module describes how to use R and Python with azure machine learning and choose when to use a particular language.
Lessons
- Using R
- Using Python
- Using Jupyter notebooks
- Supporting R and Python
Lab : Using R and Python with Azure machine learning
- Adding R and Python scripts
- Using Python with Visual Studio IDE
- Add a Jupyter notebook
- Run Jupyter notebook
- Import packages for R/Python
- Data visualization using R/Python
- R programming to work on a time series
After completing this module, students will be able to:
- Explain the key features and benefits of R
- Explain the key features and benefits of Python
- Use Jupyter notebooks
- Support R and Python
Module 9: Initializing and Optimizing Machine Learning Models
This module describes how to use hyper-parameters and multiple algorithms and models, and be able to score and evaluate models.
Lessons
- Using hyper-parameters
- Using multiple algorithms and models
- Scoring and evaluating ensembles
Lab : Initializing and optimizing machine learning models
- Using hyper-parameters
- Build an ensemble using stacking
- Evaluate the ensemble
After completing this module, students will be able to:
- Use hyper-parameters
- Use multiple algorithms and models to create ensembles
- Score and evaluate ensembles
Module 10: Using Azure Machine Learning Models
This module explores how to provide end users with Azure machine learning services, and how to share data generated from Azure machine learning models.
Lessons
- Deploying and publishing models
- Exporting data
Lab : Using Azure machine learning models
- Deploy machine learning models
- Consume a published model
- Export data
- Use exported data in machine learning model
After completing this module, students will be able to:
- Deploy and publish models
- Export data to a variety of targets
Module 11: Using Cognitive Services
This module introduces the cognitive services APIs for text and image processing to create a recommendation application, and describes the use of neural networks with Azure machine learning.
Lessons
- Cognitive services overview
- Processing text
- Processing images
- Creating recommendations
Lab : Using Cognitive Services
- Create and run a text processing application
- Create and run an image processing application
- Create and run a recommendation application
After completing this module, students will be able to:
- Describe cognitive services
- Process text through an application
- Process images through an application
- Create a recommendation application
Module 12: Using Machine Learning with HDInsight
This module describes how use HDInsight with Azure machine learning.
Lessons
- Introduction to HDInsight
- HDInsight cluster types
- HDInsight and machine learning models
Lab : Machine Learning with HDInsight
- Deploy an HDInsight cluster
- Use the HDInsight cluster
- Display data in Power BI
After completing this module, students will be able to:
- Describe the features and benefits of HDInsight
- Describe the different HDInsight cluster types
- Use HDInsight with machine learning models
Module 13: Using R Services with Machine Learning
This module describes how to use R and R server with Azure machine learning, and explain how to deploy and configure SQL Server and support R services.
Lessons
- R and R server overview
- Using R server with machine learning
- Using R with SQL Server
Lab : Using R services with machine learning
- Deploy DSVM
- Explore the data science VM
- Configure R server
- Run a sample R server application
- Deploy a SQL server 2016 Azure VM
- Configure SQL Server to allow execution of R scripts
- Execute R scripts inside T-SQL statements
- Use R to visualize data
After completing this module, students will be able to:
- Implement interactive queries
- Perform exploratory data analysis
Audience
The primary audience for this course is people who wish to analyze and present data by using Azure Machine Learning._x000d_
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The secondary audience is IT professionals, Developers , and information workers who need to support solutions based on Azure machine learning.