TDWI Chicago, May 6-11: Get Your Hands Dirty With Data – KDnuggets Offer

Attend the Hands-on Lab series and bring practical skills back from Chicago. Save 30% through March 16 with priority code KD30.

Attend the Hands-on Lab series and bring practical skills back from Chicago.
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Hands-on data
KDnuggets readers save 30%
through March 16 with priority
code KD30
If you've ever wanted to learn the basics of machine learning we've got you covered.

TDWI's new Hands-on Lab series brings together practical, in-depth training on today's most widely used tools and technologies in analytics all in one place. From learning beginner programing in Python, building and evaluating machine learning models using scikit-learn or R, to polishing your data mining skills, expert TDWI instructors will help you ground yourself in the basics.

Each lab provides a solid framework for understanding and applying these tools immediately back at the office.

Sessions include:
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R is a popular, open source software environment for statistical computing and graphics. It is being used for data analysis, extracting and transforming data, fitting models, drawing inferences, making predictions, plotting, and reporting results. Learn R's basic functions, in addition to working with data frames, data reshaping, basic statistics, graphing, linear and nonlinear models, clustering, and model diagnostics.
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For those new to programming, learning Python is a great place to start. Python is one of the top languages used in data science and predictive analytics. This course will take you through the basics of Python (through the use of Jupyter notebooks) including the variable types, if/else statements, for loops, and Python syntax.
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Using a series of examples and exercises for each topic, you'll experience the Hadoop tools firsthand and strengthen your learning with discussion about how to implement them.
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This one-day course provides a solid structure to organize your thoughts as well as code snippets and best practices to get started. In this course you will learn to how to build, train, and evaluate machine learning models to predict continuous and discrete quantities using well-tested and freely available Python libraries.
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