Microsoft Azure Machine Learning in Practice: From Fundamentals to Deployment - with Rafal Lukawiecki
This intensive, 5-day, hands-on course will teach you everything necessary to prepare your data, build, evaluate, and, most importantly, validate machine learning models, before deploying them to production using the newest, 2020 version of Microsoft Azure Machine Learning. Thanks to the brand-new, powerful, and highly visual Microsoft Azure ML tools very little coding will be necessary to achieve your goals.
How you will benefit
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 area of industry and science.
What did our students say about Rafal`s last course?
"Rafal er en utrolig dyktig formidler, og kurspresentasjonene var gjennomgående noe av det beste jeg har vært på av kursing."
"Rafal er utrolig dyktig , godt forberedt og hyggelig/ hjelpsom."
"Fantastisk kursholder! Venter med spenning på flere av hans kurs."
Audience and Prerequisites
You do not need to have a prior coding experience in any programming language to benefit from this course, yet you will learn all the fundamentals of machine learning, including the most important algorithms and their performance metrics such as precision and recall, validation charts and curves, like the ROC chart, and much more!
About Rafal Lukawiecki
As 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 in data sets both big and small. Rafal is also known for his work in business intelligence, data protection, enterprise architecture, and solution delivery. While majority of his clients come from consumer and corporate ﬁnance, entertainment, healthcare, IT, retail, and the public sectors, Rafal has worked in almost all industries.
He has been a popular speaker at major IT conferences since 1998.
Course content - detailed description
We will use the brand-new Azure ML Designer UI, and the completely new Azure ML Studio (very different from the older one) 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 most of your work will be done using the graphical UI, you will also see how to code, in Python and in a little R, for Azure ML Service 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.
Being practical, we will also show you how to prototype your work using the classic version of Azure ML, which can be much faster, cheaper and easier to use, especially on smaller datasets, than the newer version. That may be especially useful during earlier stages of your projects, while you are researching, and before you are committed to them. Conveniently from a learning perspective, the way you work using the visual GUI in both classic and the new versions of Azure ML is almost the same. Please note, however, that classic Azure ML will only take a very small portion of the course, about an hour—almost all of our time will focus on the new, 2020 version.
You will also learn about Automated ML, which can be very helpful at the early stages of a 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 also learn how to update your models on an ongoing basis—a strength of the new Azure ML service.
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.
At the end of the course you will not only know how machine learning works, but also how to get started, what parts of Azure ML to use and when, and how to know that you are making good progress—and when to stop, because you have reached the limits of what is possible. In addition to that, Rafal will make sure to answer your own questions from your area of interest to help you achieve as much success as machine learning allows. Above all, you will learn from the experience of a recognised industry expert, Rafal Lukawiecki, who has been practicing machine learning, data science and AI for well over a decade on many successful commercial projects.