Everything necessary to prepare your data, build, evaluate, and, most importantly, validate machine learning models, before deploying them to production, using the newest version of Microsoft Azure Machine Learning.

We will use the latest version of Azure ML Designer UI, and Azure ML Studio, to teach all the fundamentals of machine learning. You will understand why and how to use specific algorithms, notably: classifiers such as Boosted Decision Trees, Logistics Regression and Neural Networks, both linear and non-linear regressions, clustering, and recommenders. Though almost all of your work will be done using the graphical UI, you will also see how to code for Azure ML Service in Python and in a little R using the most popular Python libraries, such as scikit-learn. Although deep learning is not a focus for this course, you will also see how easy it can be to use it with Azure ML. If you already have some programming experience: that is great—but it is not necessary, as everything needed to use Azure ML, including every line of code, will be carefully explained during the course. If you are interested in learning R for more advanced ML and data science, please see our other course by Rafal that focuses on R and Microsoft ML and SQL Servers—which we do not cover in this course.

You will also learn about Automated ML, which can be helpful at the early stages of machine learning projects, especially while you are still trying to understand the business domain you are modelling, or if the data that you have acquired is confusing or unclear to you. Azure ML provides an easy-to-use interface for rapidly building and evaluating multiple models using AutoML, which you will have a chance to practice using during the course. We will also explain why at the later stages of your project your model reliability will benefit more from relying on your knowledge and experience than from pure automation.

From an operational perspective, you will learn about Azure and non-Azure (other clouds and on-premise) resources needed to support machine learning both during modelling, and later, during the deployment to production. You will see how to consume your models in bespoke apps, processing pipelines, and in analytics tools, such as Power BI, at that stage of your projects. You will learn how to update your models on an ongoing basis—a strength of the new Azure ML service. For those projects where coding is important, whether in Python, R or another language, we will also show you how to set-up brand-new Azure ML Compute Instances, which come with a rich, preconfigured development environment for both data scientists and ML/AI engineers.

Model validity is the most important aspect of any machine learning project. A lot of time has been dedicated to explain it in detail: many validity metrics, such as: precision, recall, AUC, F1 score, accuracy (which is rarely a good metric), and the many charts we use to analyse models, especially: confusion matrix, lift/gain charts, ROC curve, precision-recall curve, calibration charts, scatter plots, and others used for regression evaluation like histograms of residuals.

Above all, this course will not only teach you the technology and how to use it, but, much more importantly, you will understand how machine learning works, how to avoid common mistakes, such as overfitting/overtraining, how to balance model accuracy against its reliability, and how to relate key ML performance metrics to your business goals, making your bosses and clients happy with your progress and results. You will gain clarity how to start your projects and how to finish them. You will understand what types of work are suited to ML, and which are unlikely to deliver results. You will discover what makes good first projects in your own area of specialisation. These are the key benefits of studying machine learning with Rafal Lukawiecki: industry veteran who has been practicing ML, data mining, statistical learning, and data science with his customers for well over a decade, and who has studied artificial intelligence at Imperial College in the ‘90s under the guidance of the leaders and the inventors of this are of industry and science.

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

There are no prerequisites other than 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 be able to follow the course.

This course will teach you machine learning using Azure ML: you do not need to understand ML or data science before attending.

50% lectures, 30% demos, 20% lab tutorials.

You are encouraged to follow the demos on your machine, and you will be challenged to find answers to a few larger problems during the tutorials. We will provide you with all the necessary data sets. While both the demos and the tutorials 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 work with your own data. As each training centre is different, you will receive an email, two weeks before the course starts, explaining how to prepare your computer for the course, unless the centre is providing one for you. In any case, preparation is easy, because we rely on a combination of Azure web services and Azure virtual machines. You can also copy course experiments and data for future learning and reference after the course.

Because of Rafal’s 10+ years of real-world machine learning experience.

You will not only learn all the concepts and tools that you need to know from an experienced teacher who has trained over 900 data scientists world-wide, a highly-respected presenter, capable of holding your attention, but, above all, from a practitioner of machine learning. Rafal Lukawiecki has been delivering ML, data mining, and data science projects for customers in retail, banking, entertainment, healthcare, manufacturing, education, and government sectors for twelve years. Because of that, you will learn:

• 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.

Learn from Rafal who has done it all, not from those who just teach it—this is why it is Practical Machine Learning.

Selection of comments from students who have attended the previous version of Rafal’s machine learning and data science course:

The course was an immense learning experience, tapping into the vast knowledge base that is Rafal. His presentation skills and technique made the learning experience very enjoyable. The pace at which he managed to deliver the content was remarkable, even when delayed to answer questions he still managed to run through the enormous subject matter and keep to schedule. All in all it was a very enjoyable learning experience that has fuelled my desire to learn more on the subject.
Sean, Globoforce, Ireland

The prospect scoring project was a huge success. We did A/B testing two weeks ago and found a 50% increase in lead conversion. 95% of the work was data and feature engineering and 5% was running the boosted decision tree. I also told the client from the beginning that Rafal told me that success in ML is low and when it is successful it doesn’t last. When the A/B test was a rousing success we were all in shock.
Suhail, Centric Consulting, USA (first successful ML project just after attending this course)

This was a 5 star course. Rafal is a world class teacher who brings the right combination of practical, technical and theoretical experience to the course. I have a Masters in Analytics and have worked on an Analytics Project for 3 years and yet I still learnt so much from this course. Without a doubt the best course I have been on.
Brian, Department of Social Affairs, Ireland

I highly recommend this course. Rafal’s knowledge, teaching skills and humour makes complex challenges much easier to grasp and understand.
Asbjørn, Genus AS, Norway

I initially stumbled across the Practical Data Science course having seen and been impressed by videos of Rafal speaking at Microsoft Ignite. I appreciated and enjoyed the way he discussed his (extensive) practical experience in the field as much as the technology and am pleased to say the course was no different. I came into the course from a background of working with database’s, but the world of data science is something I’ve always wanted to get more involved in. This course seemed to be ideally tailored for this.
Callum, UK public sector company

I had the pleasure of attending “Practical Data Science” in Copenhagen with Rafal. The course was great, and is just the way it is described—not only was it practical and exciting, but followed by in depth understanding of theory. Rafal is a great instructor, and certainly one of the best experts that I have had the chance to meet. Throughout the whole course I learned a lot and Rafal even took time to debate specific problems that we were contemplating.
Philip, Inspari A/S, Denmark

I can only recommend this course. Rafal is an excellent teacher. He shows real world examples that are directly applicable.
Jacquel, Datalytics AG, Switzerland