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Feature engineering as an essential to applied machine learning.

Using domain knowledge to strengthen your predictive model or prescriptive model out of prediction can be both difficult and expensive.

Same way MLaa S can be free gift to all new comers and can provide foundation for every system to solve, learn and work.

Dont worry AI OS are not far which will be the best combination of OS based on AI and MLaa S built in on top of MLaa P (Machine Learning as Platform).

I did not know the secrete and never understood that the chance of not being selected in any of draws (say n) from samples (say n) with replacement as .

I never know that each bootstrap sample or bagged tree will contain on average approximately of the observations.

What we call “data” are observations of real world phenomena.

Feature engineering as we spoke about earlier here.

Clearly MLaa S platform should if not already covers use cases like data modeling & data preprocessing as this task is most time, attention and focus consuming and one small mistake is enough to ruin the fun. Experimentation is the another task which can come as use case due to the nature of machine learning which its all about learning and experimenting.

Getting predefined templates and dashboards for our work model and required intelligence like payment intelligence, info-security intelligence, potential spending and earning intelligence etc.

To help fill the information gap on feature engineering, MLaa S hands-on can help and support beginning-to-intermediate data scientists how to work with this widely practiced phenomena.

When I came across the assertion that each bootstrap sample always contain on average approximately of the observations.

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