Fundamentals of Machine Learning + Intermediate into Machine Learning in R, SQL Server 2017 and Microsoft ML Server

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.

Course focuses on the newest technologies of Microsoft Machine Learning Server and SQL Server 2017.  By popular demand, second part (3 days) of this course teaches programming in R, however most of the course is also applicable to Python programmers, as the key libraries are the same.

This course has two parts:

Programm:

  • Machine Learning Fundamentals
  • Algorithms
  • Data
  • Process of Data Science
  • Introduction to Model Building
  • Introduction to Model Validation
  • Working with R
  • Data Preparation in R
  • Plots and Visualisations in R
  • Clustering, Segmentation, Anomaly Detection
  • Classification
  • Classifier Validation
  • Regressions
  • Regression Validation
  • Deployment to Production

Please note: we reserve the right to amend the order of the modules to best suit the dynamic character of the class and to answer questions as they arise. Some subjects will only be covered if time allows, but your satisfaction is guaranteed.

Target audience: 

  • Analysts, budding data scientists, database and BI developers, programmers, power users, DBAs, predictive modellers, forecasters, consultants.
  • If you have attended a prior course on Machine Learning, like Rafal’s week-long class Practical Data Science that was offered in 2015–2017, and if you are versed in model validity, accuracy, and reliability consider attending 3-day 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, then this 5-day course is right for you.

Learning methods: 50% lectures, 30% demos, 20% tutorials.
Assesment methods: Execution of independent work.
Assesment form: Independent practical tasks on relevant topics.

More information:

  • Price includes coffee breaks and lunch in restaurant Lusikas.
  • You can read more about the programm here
  • Bring your own laptop

Tähelepanu! Teie veebilehtiseja ei vasta kodulehe külastamiseks vajalikele nõuetele. Palun vahetage veebilehitsejat või seadet, millega te veebilehte sirvite.

Attention! Teie veebilehtiseja ei vasta kodulehe külastamiseks vajalikele nõuetele. Palun vahetage veebilehitsejat või seadet, millega te veebilehte sirvite.

Внимание! Teie veebilehtiseja ei vasta kodulehe külastamiseks vajalikele nõuetele. Palun vahetage veebilehitsejat või seadet, millega te veebilehte sirvite.