Data for Machine Learning

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  • What Does Good Data look like?
    • We all know that data is important for machine learning success, but what does it really look like? What steps do you need to take to get from scattered, unprocessed data to nice clean learning data? This week takes an overarching view to describe how your problem and data needs interact, and what processes need to be in place for successful data preparation.
  • Preparing your Data for Machine Learning Success
    • Now that you have your data sources identified, you need to bring it all together. This week describes what you need to prepare data overall.
  • Feature Engineering for MORE Fun & Profit
    • Data is particular to a problem. This week we'll discuss how to turn generic data into successful fuel for specific machine learning projects.
  • Bad Data
    • There are so many ways data can go wrong! This week discussed some of the pitfalls in data identification and processing.

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