Introduction to AzureML
Richard Conway
5 h 30 m
Lecture Overview
This course explores Microsoft Azure’s AzureML service offering for students that are either new to machine learning or new to Azure. The course starts with an introduction to various aspects of building and “experiments” in AzureML and using MLStudio to create cohesive machine learning workflows. Each topic looks at different aspects of AzureML as well as introduces different concepts in machine learning such as regression, clustering and classification and when you would use each. The course moves onto more advanced topics such as how the R language can be used to enrich AzureML as well as being able to define neural networks and lastly how to integrate into more complex data orchestrations involving other services in Azure.
Lecture Modules
In this module you will learn how to create an AzureML workspace and create new experiments. You will learn how to share experiments with your colleagues and customize your workspace. You will learn how to use MLStudio and save changes in your experiments.
In this module you will learn how to read and write data to and from a database, storage and Azure tables. You will be able to read data from the web through HTTP and OData and upload datasets from your local machine or consume them from Azure storage.
In this module you will learn how to manipulate data using the workflow tasks in MLStudio. This will include, cleaning data, joining datasets, adding columns, filtering using expressions and adding metadata to columns.
In this module you will learn about math operations, linear correlation and hypothesis testing. This will enable you to whether or not the results of your experiments are statistically significant or not.
In this module you will learn how to perform regression analysis to predict a continuous variable. You will also look at classifiers and how you can predict two or more discrete classes of output. You will learn how to analyse the results through a ROC curve and the coefficient of determination to enable a feedback loop in improving your model.
In this module you’ll learn how to use K-Means clustering to determine whether data points belong to a particular cluster. You’ll learn how to use clustering to build a pipeline of machine learning models which can be enriched to give better results together than separately.
In this module you’ll learn how to build a recommender system given a set of ratings and features for users and products. You’ll be able to choose which movies to watch given the genres you like and ratings and what others are watching and choose which things you should buy from a product catalogue given what others have bought.
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