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 fully up-to-date for 2019 and can be attended in the classroom or via 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.
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 learn much from this course.
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% 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.
We provide pre-built machines, but if you’d rather use your own laptop, please tell us in advance.
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.
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, 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 Skype.
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
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
Algorithms, frameworks, model validity
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
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
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
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:
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
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:
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