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Chapters 2-7 (2.4) 8 criteria to machine learning succes (Accuracy, ease…

- - - - Auto ML can outperform many different platforms with minimal math required.
- - - - commercial (such as data robot) sold by different companies for specific reasons. less focus on knowing computer science but still should know some.
      - Open source (typically need a backround in computer programming.)
    - - helps match specific things. (focus is usually on customer interactions)
- - - - You need to assure many things, can we actually solve this problem? Is thts the best problem to solve?
        
        1) Is the project statement presented in the language of business?
        
        Does the project statement specify actions that should result from the
        project?
        
        How could solving this problem impact the bottom line?
- - - - (Excericises were in other notebook)
- - - - In the enxt chapter we move onto determining the prediciotn target.
        
        Chapter 5 excercises were on a different page.
- - - - 6.2) How is the target important for machine learning.
        
        without a target, there is simply no way for humans or machines to learn what associations drive an outcome.
        
        We have discussed two kinds of targets here without explicitly noting their defining characteristics. The first kind is classification, which predicts the category to whicha new case belongs. For example, we might build a model of who gets divorced during the first 10 years of marriage
        
        we can use machine learning to conduct regression.
- - - - Who will use the model?
        
        Is management on board with the project?
        
        Can the model drivers be visualized?
        
        How much value can the model produce
- - - - We also have ethical risks which can be very important to consider.
        
        Another type of ethical risk is "privacy."
        
        Analytics is used not only for core capabilities, but also in related functions
        like Marketing and Human Resources.
        
        The top management team recognizes and drives use of analytics and
        innovative measures of success.
        
        Fact-based decision-making is part of the organizational culture.
        
        Copious data and analytics is performed, and results are shared both
        internally and with partners and customers.
        
        A “test and learn” culture exists where opportunities to learn from data and
        test hypotheses is a natural part of the workplace environment.