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Azure ML: A Brief Introduction

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.

15 July 2014
28900 views

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.

6 October 2015
7389 views

What is Azure ML Classic?

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.

16 December 2014
5451 views

Data Science for Business

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.

11 December 2014
4167 views

Data Science Concepts: Cases and Statistics

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.

10 December 2014
3133 views

Introduction to Azure ML Classic

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

29 January 2015
2970 views

Data Science Concepts: Machine Learning and Models

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.

16 December 2014
2412 views

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!

25 October 2016
2275 views

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

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.

24 December 2019
2157 views

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.

23 December 2016
1163 views

What is Artificial Intelligence?

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

21 March 2019
864 views

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.

21 March 2019
749 views

Microsoft Machine Learning Technologies: View Towards 2020

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.

24 December 2019
728 views

Machine Learning for Security Applications: Why?

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.

31 August 2019
305 views

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.

21 March 2019
250 views

Machine Learning for IT Security: From ML to Security AI

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

4 October 2019
218 views

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