Some of the key takeaways from the session are:
- All machine learning implementations within an organization have to go through 4 fundamental steps: pre-processing, feature cleaning, feature engineering, and modeling.
- Metalearning, having the machine learning system learn from the raw data as it goes through the different possibilities of pre-processing, feature cleaning, feature engineering and modeling techniques.
- Leveraging Master Algorithms that can deal with raw data and figure out the best path will give everyone access to building these complex machine learning models without having written a single line of code.
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