All language subtitles for 11 - Module Summary.en

[Autogenerated] and this discussion of

cross validation brings us to the very end

of this model. Very understood. The rule

of features in machine learning. We

started off the discussion off the

importance of data in machine learning. If

you feed garbage into your model, you're

going to get garbage out. Your model is

only as good as your data. In this

context, we discuss how machine learning

algorithms can learn from data, and he

started what features and labels mean.

They're also referred to as ex variables

and by values. We then got a big picture

understanding off all off the processes

involved in the common machine learning

workflow be understood exactly where in

this workflow data, pre processing and

feature engineering fit in. We then saw

how feature engineering could mean many

different things. It's a broad array of

techniques, all of which fall another

feature engineering umbrella And finally,

the rounded our discussion off by talking

off the kinds of data that you will work

with the new building animal models. We

spoke off the importance off training

tests and validation data, and he also

discussed K fold cross validation to build

more robust models in the next model will

get hands on and we see how we can prepare

and pre process all data for machine learning.