Practical Machine Learning, AI & Data Science Part A: Intro to ML using Azure ML

Practical Machine Learning, AI & Data Science Part A: Intro to ML using Azure ML

Rafal’s knowledge, teaching skills and humour make complex challenges much easier to grasp and understand.
I highly recommend this course.

Asbjørn, Norway

Available as a private course

Rafal will teach you and your team privately at a location and time of your choice. Find out more about private training or ask us about it here.
Please contact us for the next dates and availability. Cost per seat: £1400 | $1800 | €1600

Introduction to Machine Learning, AI & Data Science with Azure ML – a 2 day training course with Rafal Lukawiecki

This 2-day course introduces the most important concepts and tools in machine learning that you need to know.

This live, instructor-led course (updated for 2019) can be attended in the classroom or via Teams/Skype.

It focuses on R and the technologies of Microsoft Machine Learning Server, Azure ML, SQL Server, and Azure SQL Database, whilst teaching you everything you need to know to start using machine learning, and to apply data science for analytics.

Who is the Introduction to Machine Learning training course for?

Analysts, budding data scientists, database and BI developers, programmers, power users, DBAs, predictive modellers, forecasters, consultants, anyone interested in using ML for AI.

What you will learn

You will learn all the concepts and tools that you need to know! Rafal Lukawiecki will teach you:

  • everything essential to starting data science, ML, and AI projects
  • all fundamental concepts
  • how to avoid common pitfalls
  • how to work fast yet accurately
  • what is really useful and practical
  • what is more theoretical but still important
  • what hype you should be wary of.

You will be able to ask any questions related to your industry and you will get relevant, pragmatic, no-nonsense answers, helping you get ahead with your own projects.

Rafal has been delivering ML, data mining, and data science projects for customers in retail, banking, entertainment, healthcare, manufacturing, education, and government sectors for more than 10 years, and has trained more than 800 data scientists worldwide. He’s a highly-respected presenter, capable of holding your attention. Above all, you’ll be learning from a machine learning practitioner.

The training comprises 50% lectures, 30% demos and 20% labs.

You are encouraged to follow the demos on your machine, and you will be challenged to find answers to 3 larger problems during the tutorials. While they are a hands-on part of the course, if you prefer not to practice, you are welcome to use that time for additional Q&A, or to analyse your own data. We will provide you with all the necessary data sets.

Bring your own laptop. 

To make your learning experience as productive as possible, we will offer you a running Azure VM that contains all the data and the software used during the course. You will access it using your own laptop and the Microsoft Remote Desktop app, which is freely available on both Windows and macOS. Additionally, we will also provide you a detailed set-up guide, which you are welcome to follow to either deploy your own Azure VM from our image, or to install the necessary free (like R) or evaluation/developer edition (like SQL Server) software on your laptop.

What’s next?

This course is the first of two parts. It continues into days 3, 4 and 5 with Intermediate Machine Learning in R, SQL Azure/Server, and Microsoft ML Server. The two courses together make up the 5 day Practical Machine Learning course.

Rafal Lukawiecki - data scientistAbout Rafal Lukawiecki

As a Data Scientist at Project Botticelli Ltd, Rafal focuses on making advanced analytics and artificial intelligence easy and useful for his clients.

He can help you find valuable, meaningful patterns and statistically valid correlations using data mining and machine learning, and he is also known for his work in business intelligence, data protection, enterprise architecture, and solution delivery.

Rafal has been a popular speaker at major IT conferences since 1998, and he has had the honour of sharing keynote platforms with Bill Gates and Neil Armstrong. A natural educator, he explains complex concepts in simple terms in his enjoyably energetic style.


This course is available as live instructor-led training in the classroom or join the live class by Teams/Skype.

This 2-day course is the first of two parts. It continues into days 3, 4 and 5 with Intermediate Machine Learning in R, SQL Azure/Server, and Microsoft ML Server. The two courses together make up the 5-day Practical Machine Learning course.

Machine Learning Fundamentals

We begin with a thorough introduction of all of the key concepts, terminology, components, and tools. Topics include:

  • Machine learning vs. data mining vs. artificial intelligence
  • Model building vs model deployment
  • Explorative ML vs predictive modelling
  • Getting started
  • Data Driven Decisions
  • Tool landscape: open source R vs. Microsoft R, Python, Azure SQL Database, SQL Server, ML Server, Azure ML
  • Azure ML Studio vs Azure ML Services
  • GUI vs code-first approaches in Azure ML
  • Teamwork
  • Algorithms, frameworks, model validity


There are hundreds of machine learning algorithms, yet they belong to just a dozen groups, 5 of which are commonly used. We will introduce those algorithm classes, and we will discuss some of the most often used examples in each class, while explaining which technology tools (Azure ML, SQL, or R) provide their most convenient implementation. You will also learn how to find more algorithms on the Internet and how to figure out if they are any good for real use. Topics include:

  • What do algorithms do?
  • Algorithm classes in R, Python, ML Server, Azure ML, and SSAS Data Mining
  • Supervised vs. unsupervised learning
  • Classifiers
  • Clustering
  • Regressions
  • Similarity Matching
  • Recommenders
  • Determining which algorithms/packages are good and trustworthy?
  • Correlation is not causation


Machine learning requires you to prepare your data into a rather unique, flat, denormalised format. While features (inputs) are always necessary, and you may need to engineer thousands of them, we do not need labels (predictive outputs) in all cases. Topics include:

  • Cases, observations, samples, rows, and signatures
  • How much data is enough?
  • Does big data help?
  • Inputs and outputs, features, labels, regressors, independent and dependent variables, factors
  • Data formats, discretization/quantizing vs. continuous
  • Indicator columns
  • Feature engineering
  • Azure ML data preparation and manipulation modules
  • Moving data around and its storage, SQL vs. NoSQL, files, data lakes, BLOBs, Data Lakes and Hadoop

Process of Data Science

The process consists of problem formulation, data preparation, modelling, validation, and deployment—in an iterative fashion. You will briefly learn about the CRISP-DM industry-standard approach but the key subject of this module will teach you how to apply the scientific method of reasoning to solve real-world business problems with machine learning and statistics. Notably, you will learn how to start projects by expressing needs as hypotheses, and how to test them. Topics include:

  • Start of every project: stating business needs in data science terms
  • Hypotheses: null vs alternative
  • Hypothesis testing and experiments
  • Evaluating test results
  • The problem with p-values (briefly)
  • Bayesian vs. frequentist approach (briefly)
  • Student’s t-test, Pearson chi-squared test
  • Iterative hypothesis refinement

Introduction to Model Building

At the heart of every project we build machine learning models! The process is simple and it follows a well-trodden path. In this module you will build your first decision tree and get it ready for validation in the next module. Topics include:

  • Connecting to data
  • Selecting features and the label
  • Splitting data to create a holdout
  • Initialising the algorithm (Two-class Boosted Decision Tree)
  • Training a decision tree
  • Scoring the holdout
  • Plotting accuracy
  • Information leakage
  • Dealing with troublesome feature selection

Introduction to Model Validation

The most important aspect of any data science, artificial intelligence, and machine learning project is the iterative validation and improvement of the models. Without validation, your models cannot be reliably used. There are several tests of model validity, most importantly those that check accuracy and reliability. Topics include:

  • Testing accuracy
  • False positives vs. false negatives
  • Classification (confusion) matrix
  • Precision and recall
  • Balancing precision with recall vs. business goals and constraints
  • Introduction to lift charts and ROC curves
  • Testing reliability
  • Testing usefulness
  • The bias-variance trade-off (briefly)

Deployment to Production

Although many models provide immediate, explorative value not needing any further deployment, for many others deploying them into production enables the predictive, and artificially intelligent uses. You will learn a very quick and simple way of deploying Azure ML models as a web service, which is good at earlier stages of your projects. More advanced deployment techniques are covered in the intermediate course. Topics include:

  • Production concerns
  • Web service vs other deployment techniques
  • Experiment preparation for deployment
  • Creating a web service
  • Calling a predictive web service using Request/Response API
  • Consuming predictive web services in Python application and in Excel

This course can be followed up with Intermediate Machine Learning in R, SQL Azure/Server, and Microsoft ML Server. The two courses together make up the 5 day Practical Machine Learning course.

You need to have a general ability to work with data in any form: if you have used a spreadsheet, tables, databases, or you have written a program, no matter how long ago, you will learn much from this course.

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