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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 Classic?

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 for Business

Rafal introduces the process of data science projects

Why should you care about data science? Let Rafal, who specialises in it, explain how it can help you improve your business, understand your customers and products, make your employees happier, and your own job even more satisfying, in his 34-minute video. Data science combines four data handling approaches with the scientific method of reasoning, which can guide the way in which you should run experiments before making business decisions.

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 Classic

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.

How to Succeed with Your First Data Science Projects

I have had my share of successful and failed projects since I have embarked on data science ten years ago. While I am happy to say that the rate with which I now succeed on customer projects is much better than in the past, that is not just because I know my field better. It is because I am better at setting my own and my customer’s expectations, and by being more careful in choosing the projects that I want to dive into. I would like to share some of my observations with those of you who are newer to this field. I would like to save you some frustration and to help you succeed as often as possible. Read on!

Next Year in Machine Learning, Data Science, AI and BI

ML, BI, DS, and AI Trends that Shaped 2019

The trends that shaped 2019 and predictions for the future of machine learning and analytics. Why you should abandon Hadoop, brush up on statistics and obsess less about technology.

Code and Data Samples (R, R Services, SSAS)

Download code and the data for a few of the demos used in Rafal’s Practical Data Science courses, including: classifier performance in R, mortgage default logistic regression and the 10 million row data set, cross-sell and recommendations using Association Rules in SQL Server Analysis Services Data Mining.

What is Artificial Intelligence?

What is Artificial Intelligence?

This video introduces the history of Artificial Intelligence (AI) and explains how it can be implemented using Machine Learning.

What is Artificial Stupidity?

What is Artificial Stupidity?

Artificial Intelligence of today easily becomes Artificial Stupidity. This short video explains its dangers, and suggests when it is OK to use AI.

Microsoft Machine Learning Technologies: View Towards 2020

New Azure Machine Learning: Performance Metrics

What is new in the new Azure Machine Learning? What is changing in the rest of the Microsoft machine learning platform, notably ML Server and SQL Server ML Services, the 2019 Big Data Clusters, and frameworks: Automated ML, ML.NET, MicrosoftML, RevoScale, and MLLSpark? You will also see an overview of the popularity of non-Microsoft tools in this video—make sure to read the essay, under the video, too.

Machine Learning for Security Applications: Why?

Hugh Simpson-Wells Interviews Rafal Lukawiecki about Machine Learning

This interview with Rafal Lukawiecki and Hugh Simpson-Wells discusses applications of machine learning for IT security purposes, including: authentication, authorisation, log analysis, and breach prevention.

The Future of AI—How to Avoid Artificial Stupidity

The Future of AI—How to Avoid Artificial Stupidity

In this detailed video I explain how to get started in building not just good AI, but, above all, the kind that avoids the risks of artificial stupidity, by combining several powerful approaches: machine learning, programming in logic, and modern data.

Machine Learning for IT Security: From ML to Security AI

Security Artificial Intelligence

This video explains how to get started with machine learning for IT security purposes, leading to powerful, potentially dangerous, autonomous Security AI systems.