Introduction to Machine Learning, AI and Data Science with Azure ML - with Rafal Lukawiecki

This live classroom course is new for 2018! It focuses on the newest technologies of Microsoft Machine Learning Server and SQL Server 2017.

This 2-day course introduces the most important concepts and Tools, and should be followed by the 3-day course: Intermediate Machine Learning in R on SQL Server and Microsoft ML Server

If you have attended a prior course on Machine Learning, like Rafals week-long class Practical Data Science course offered in 2015-2017, and if you are versed in model validity, accuracy, and reliability, then you should consider attending the Intermediate course only. 
Ask yourself these questions: Can I explain the difference between cross-validation and hold-out testing? Do I know which business metrics correspond to precision and which to recall? Is model accuracy more important than reliability? And how does a boosted decision tree work? If in doubt, please attend both the Introduction course (2 days) and Intermediate course (3 days). 

Audience:

Analysts, power users, predictive and BI developers, database and other professionals who wish to embrace machine learning, budding data scientists, consultants. 

Why attend this class?

Because of Rafals 10+ years of real-world machine learning experience. 
You will not only learn all the concepts and tools that you need to know from a great teacher who has trained almost 500 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 over ten years. 

Because of that, you will learn:

  • how to avoid common pitfalls,
  • how to get ahead of your competition by working faster,
  • 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 called Practical Machine Learning. 

What did our students say about the 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."
 

Prerequisites:

There are no formal prerequisites to attend this course because everyone will benefit from the lectures and the discussions. However, it will be useful with some knowledge of linear algebra, statistics and probabilty and/ or programming. 

Format:

The course format is 50% lectures, 30% demos and 20% tutorials. 
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, and we will explain what free or evaluation edition software needs to be installed to follow the course on your own laptop. In some training centres we are able to provide pre-built machines which you can use instead of your own—please enquire. You will need an Azure account (even a free one) during the course. You can copy course experiments and data into your workspace for learning and for future reference after the course. 

Course content:

To deliver the best possible training we follow the industry. The agenda and course content are subject to continuous improvement and revision without further notice. 

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
  • Tool landscape: open source R vs. Microsoft R, Python, SQL Server, ML Server, Azure ML
  • Teamwork

Algorithms 
There are hundreds of machine learning algorithms, yet they belong to just a dozen of groups, of which 5 are in very common use. 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

Data 
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, signatures
  • 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, 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:

  • CRISP-DM
  • Stating business question in data science term
  • Hypothesis testing and experiments
  • Students 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
  • Splitting data to create a holdout
  • Training a decision tree
  • Scoring the holdout
  • Plotting accuracy

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

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.

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