Module 1: Introduction to Machine Learning- This module introduces machine learning and discussed how algorithms and languages are used.
- What is machine learning?
- Introduction to machine learning algorithms
- Introduction to 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.
- Azure machine learning overview
- Introduction to Azure machine learning studio
- Developing and hosting Azure machine learning applications
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.
- Categorizing your data
- Importing data to Azure machine learning
- Exploring and transforming data in Azure machine learning
Module 4: Preparing Data for use with Azure Machine Learning- This module provides techniques to prepare datasets for use with Azure machine learning.
- Data pre-processing
- Handling 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.
- Using feature engineering
- Using feature selection
Module 6: Building Azure Machine Learning Models- This module describes how to use regression algorithms and neural networks with Azure machine learning.
- Azure machine learning workflows
- Scoring and evaluating models
- Using regression algorithms
- Using neural networks
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.
- Using classification algorithms
- Clustering techniques
- Selecting 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.
- Using R
- Using Python
- Incorporating R and Python into Machine Learning experiments
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.
- Using hyper-parameters
- Using multiple algorithms and models
- Scoring and evaluating Models
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.
- Deploying and publishing models
- Consuming Experiments
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.
- Cognitive services overview
- Processing language
- Processing images and video
- Recommending products
Module 12: Using Machine Learning with HDInsight- This module describes how use HDInsight with Azure machine learning.
- Introduction to HDInsight
- HDInsight cluster types
- HDInsight and 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.
- R and R server overview
- Using R server with machine learning
- Using R with SQL Server