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Azure ML: A Brief Introduction

Azure ML tasks

In order to do predictive analytics with Azure Machine Learning, you just upload, or import current or historical data, build and validate a model, and create a web service that uses your models to make fast, live predictions. This article, by Rafal, introduces these concepts, outlines the supported machine learning algorithms, and overviews the key functions of the ML Studio development tool.

What is Advanced Analytics, Data Science, Machine Learning—and What is their Value?

The most common question Rafal gets asked by budding data scientists is: how can I explain the value of data science to my customers?  This article explains the five key reasons for doing advanced analytics in terms of the short-term and strategic value it provides to business customers. Rafal also takes time to explain the terminology, covering the differences between data mining and machine learning, what is data science and advanced analytics, and he also explains the concept of the scientific method of reasoning.

What is Azure ML?

Rafal shows an Azure ML experimental model

Microsoft Azure ML is a cloud-based platform for designing, developing, testing and deploying predictive models. Let Rafal introduce it to you in this 10-minute video, which shows an experiment and a predictive web service in ML Studio.

Data Science Concepts: Cases and Statistics

Rafal shows density plot using ggplot2 in R

Let Rafal, expert on predictive analytics, data mining, and machine learning introduce the most fundamental data science concepts in this 1-hour video: cases (or observations), with their inputs and predictable outputs, descriptive statistics, and the basic tools, including Azure ML, SQL, Excel, R, RStudio, and Rattle.

Introduction to Azure ML

Rafal discusses a scoring experiment design in Azure Machine Learning

This full-length, 1-hour 40-minute, in-depth video introduces every aspect of Microsoft Azure Machine Learning: tools and concepts, the processuploading datamodellingvalidating results, preparing and publishing scoring experiments and even using deployed machine learning web services by calling them from a Python application.

Data Science Concepts: Machine Learning and Models

Rafal discusses confusion (classification) matrix and prediction thresholds

This 1-hour module, by Rafal, introduces the essence of data science: machine learning and its algorithms, modelling and model validation. Data science differs from traditional, statistics-driven approach to data analysis in that it extensively uses those algorithms for the detection of patterns that help us build predictive models. Make sure to watch this video before you progress to the ones introducing Azure ML.

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