This 4-day class is the first of the two data science courses taught by Rafal Lukawiecki. Some of the topics will be introduced at level 200, without requiring you to have data science prerequisites, on the first day. However, as the class progresses, the level of the training will quickly increase to 300 and 400.
You will learn machine learning, data mining, some statistics, data preparation, and how to interpret the results. You will see how to formulate business questions in terms of data science hypotheses and experiments, and how to prepare inputs to answer those questions. We will cover common issues and mistakes, how to resolve them, like overtraining, and how to cope with rare events, such as fraud. At the end of this course you will be able to plan and run data science projects.
As a practicing data miner, Rafal will also share his decade of hands-on experience while teaching you about Azure Machine Learning (Azure ML) which is the foundation of Cortana Analytics Suite, and its highly-visual, on-premise companion, the SQL Server Analysis Services Data Mining engine, supplemented with the free open source and Cortana’s Revolution Analytics R software. We will use some Excel, however, most of our time will be spent in ML Studio, some in R, RStudio, SSDT, SSMS, and the Azure Portal.
Prerequisites: No formal prerequisites. Basic knowledge of SQL Server Data Tools, Excel and any analytical experience helps. Best of all: prepare questions that you would like to answer using predictive analytics and machine learning.
Format: 60% lectures, 20% demos, plus 20% time allocated to help you follow the demos and tasks on your own equipment, if you bring a laptop. You will be challenged to find answers to 4 problems during the course, and you will have a chance to build your own models in SSAS, Azure ML and R. Doing that will help you learn, however, it is not a requirement: you are welcome to observe the demos and ask questions, too. If you bring your own data, you are welcome to analyse it, too. You will get a list of free or evaluation-edition software to preinstall before attending. You will need your own Azure account: free one is OK, but the paid one is better—and it can be inexpensive, or even free during a trial. You can copy course experiments and data into your ML workspace for learning and future reference.
Target audience: Analysts, power users, predictive and BI developers, database and other professionals who wish to embrace machine learning, budding data scientists, consultants.
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