Microsoft Azure Machine Learning Fundamentals

Chapter 1 Introduction to the science of data 

• What is machine learning? 

• Predictive analytics 

• Everyday examples of predictive analytics 

• Early history of machine learning 

• Summary 

Chapter 2 Getting started with Azure Machine Learning 

• Core concepts of Azure Machine Learning 

• High-level workflow of Azure Machine Learning 

• Azure Machine Learning algorithms 

• Supervised learning

• Unsupervised learning 

• Deploying a prediction model 

• Summary 

Chapter 3 Using Azure ML Studio 

• Azure Machine Learning terminology 

• Azure Machine Learning pricing and availability 

• Create your first Azure Machine Learning workspace 

• Create your first Azure Machine Learning experiment 

• Download dataset from a public repository 

• Upload data into an Azure Machine Learning experiment 

• Create a new Azure Machine Learning experiment 

• Visualizing the dataset 

• Split up the dataset 

• Train the model 

• Selecting the column to predict 

• Score the model 

• Visualize the model results 

• Evaluate the model 

Chapter 4 Creating Azure Machine Learning client and server applications 

• Why create Azure Machine Learning client applications? 

• Azure Machine Learning web services sample code 

• Moving beyond simple clients 

• Cross-Origin Resource Sharing and Azure Machine Learning web services 

• Create an ASP.NET Azure Machine Learning web client 

• Making it easier to test our Azure Machine Learning web service 

• Validating the user input 

• Create a web service using ASP.NET Web API 

• Processing logic for the Web API web service 

Chapter 5 Regression analytics 

• Linear regression 

• Azure Machine Learning linear regression example 

• Download sample automobile dataset 

• Upload sample automobile dataset 

• Create automobile price prediction experiment 

• Summary 

Chapter 6 Cluster analytics 

• Unsupervised machine learning 

• Cluster analysis 

• KNN: K nearest neighbor algorithm 

• Clustering modules in Azure ML Studio

• Clustering sample: Grouping wholesale customers 

• Operationalizing a K-means clustering experiment 

• Summary 

Chapter 7 The Azure ML Matchbox recommender

• Recommendation engines in use today 

• Mechanics of recommendation engines 

• Azure Machine Learning Matchbox recommender background 

• Azure Machine Learning Matchbox recommender: Restaurant ratings 

• Building the restaurant ratings recommender 

• Creating a Matchbox recommender web service 

• Summary 

• Resources 

Chapter 8 Retraining Azure ML models 

• Workflow for retraining Azure Machine Learning models 

• Retraining models in Azure Machine Learning Studio

• Modify original training experiment 

• Add an additional web endpoint 

• Retrain the model via batch execution service

• Summary