Project Botticelli

Most Popular

Presentations (PPTs)

Over 50 of the popular PPTs (not PDFs) on: Artificial Intelligence, Azure ML, Azure AI (incl Cognitive Services and Bots), Data Science, Power BI, Microsoft R and ML Servers, R in general, and advanced analytics. Includes Microsoft Ignite, Data Science Summit and the Advanced Analytics and Data Science Roadshows, as well as various keynotes and roadshows focused on the future of data and databases from global conferences from this and the past years are available here.

Introduction to Data Mining with Microsoft SQL Server

If you ever wanted to learn data mining, and predictive analyticss, start right here! Microsoft SQL Server comes with easy-to-use data mining tools, requiring very little formal knowledge of the subject to get started. This free data mining video tutorial is the first module, in this series, dedicated to explaining how to perform advanced analytics of your own data. In this video we explain: what is data mining, why would you use it, and how it is related to Big Data analytics, and we illustrate it with two short demos, showing Outlier Detection and Market Basket Analysis.

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 Are Decision Trees?

What are Decision Trees?

A decision tree is a tree of nodes. Each node represents an input value that makes the most profound difference to an output that you wish to study. This free 10-minute video by Rafal introduces this powerful analytical tool, and explains the concepts while analysing simple retail data in a demo.

Data Mining Concepts and Tools

This 50-minute video introduces the fundamental concepts of Data Mining, a powerful analytical technology. You will learn about the process of data mining and the SQL Server Analysis Services (SSAS) Data Mining architecture, and its key concepts, including: Cases, representing your data, Mining Structures, used to describe Cases, Mining Models, and Mining Algorithms, which extract patterns hiding in your data. We briefly introduce 9 of the Microsoft data mining algorithms: Naïve Bayes, Clustering, Decision Trees, Association Rules, Sequence Clustering, Neural Networks, Logistics Regression, Linear Regression, and Time Series. You will also learn about Column Content and Data Types, Discretization, and data Distributions, as you follow the module and the 5 demos shown in it.

Books about BI

This article lists books about BI, Statistics, Data Warehousing and Management, which we recommend in conjunction with our courses and seminars.

Why Cluster and Segment Data?

Cluster-based data segmentation

Clustering is a popular data mining technique, often used for segmentation. Rafal introduces it in this short video, focusing on the reasons why it is useful for finding non-traditional segments. In the demo, you will see a clustering model, and we will use it to categorise new data in Excel.

Data Mining Model Building, Testing and Predicting with Microsoft SQL Server and Excel

Data Mining Model Building, Testing, and Predicting  Microsoft SQL Server

This 1-hour-20-minute video discusses the entire lifecycle of a Data Mining Model. You will learn how to build models and mining structures, starting by creating a Data Source and Data Source View, how to train it with your data, and how to view the results. Most importantly, you will also understand how to verify a model's validity, by applying tests of accuracy, reliability, and usefulness. You will understand, and you will also see being used, such key verification techniques as: a Lift Chart, Profit Chart, Classification Matrix, and Cross Validation. Finally, you will see how to predict unknown outcomes using your model. Not only will you hear in-depth explanations, but you will also see 11 live demos, showing you all the aspects of working with Data Mining Models, including using SQL Server Data Tools (SSDT), and Microsoft Excel, for predicting (scoring) sales to future, potential customers, based on their demographic characteristics, and their shopping habits, just discovered using a Decision Tree, and a simple mining model.

Predictive Analysis with Microsoft SQL Server & Excel

Market Basket Analysis Using Microsoft SQL Server 2008 R2 and Excel

Predictive Analysis is an advanced form of Business Intelligence, which uses Data Mining. In this short demo you will see how Microsoft Excel makes it easy to use.

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.

HappyCars Sample Data Set for Learning Data Mining

Data Mining Structures Included in the HappyCars Sample Data Set

HappyCars is our educational sample data set, used for teaching data science and data mining. It comes with SQL Server tables containing sample data, such as Customers, NonCustomers, Sales, and CustomerActivity, plus a few utility views, amongst others. It also comes with a SQL Server Data Tools (SSDT) project, HappyCarsDM, which contains a prebuilt data source and views, and a series of Mining Structures containing Mining Models, which we explain in the videos of our online Data Mining training course. We also provide a version suitable as a SQL Azure Database, which is particularly useful while learning Azure ML. It is available, at no additional cost, to our Full Access Members, as an educational aide, helpful when following our videos.

Decision Trees in Depth

Microsoft Decision Trees

Decision Trees are the most useful Microsoft data mining technique: they are easy to use, simple to interpret, and they work fast, even on very large data sets. At heart, a decision tree is just a tree of nodes. Each node represents a logical decision, which you can just think of as a choice of a value of one of your inputs that would make the most profound difference to the output that you wish to study. This almost 2-hour, in-depth video by Rafal starts with an explanation of the three key uses of decision trees, which are: data classification, regression, and associative analysis, and then takes you on a comprehensive tour of this data mining algorithm, covering it in slides and detailed, hi-def demos, which you can follow. Once you try a decision tree a few times, you will realise how easy, and useful they are to help you understand any sets of data.

Clustering in Depth

Clustering: Cluster Profiles Diagram in SSDT

Microsoft Clustering is a workhorse algorithm of data mining. It is used to explore data, segment and categorise it, and to detect outliers (or exceptions, anomalies). Each cluster represents naturally occuring groupings of your data points, grouped by their similarities, as described by their various attributes. In this in-depth, 1-hour 50-minute video, Rafal explains clustering concepts, the entire process, and all of the algorithm parameters. The detailed 12-part demo, which forms the heart of this tutorial, shows you the iterative process of clustering, explaining how to segment your own data, such as customers, or products.

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.

What is Market Basket Analysis?

Market Basket Analysis Introduced

Market Basket Analysis shows you which products are sold together with other products for reasons other than coincidence or independent popularity. Watch this 10-minute, free video by Rafal to get a better understanding of your sales patterns.

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.

Association Rules in Depth

Rafal shows association rules network dependency viewer

Association Rules is one of the most useful and well understood predictive analytical techniques, and a fundamental algorithm of classical data mining. Let Rafal teach you how to use it in SQL Server in this hands-on, detailed, 1-hour 40-minute, demo-filled video. You will learn about preparing data for analysis, making it efficient, use of the visualisers, including the dependency network viewer, and, most importantly, you will learn how to interpret the results. Rafal takes great care to explain the often confused metrics of the strengths of association: Rule Probability and Rule Importance, giving you plenty of examples to make it easier. Finally, no data mining algorithm would be complete without a set of parameters you can control, and all of them, including the very important MINIMUM_SUPPORT and MINIMUM_PROBABILITY are explained in this video.

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.

Machine Learning for Security Applications

Hugh Simpson-Wells Interviews Rafal Lukawiecki about Machine Learning for Security Applications

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.

Online Courses