When Should we apply the Aggregations(mean, sum , count, max, min) and how to deal with correlations features I am beginners to  machine learning , I worked  on the some basics machine learning problems. I had a little bit confusion about  feature Engineering. some people are using aggregations functions  for model input data and some people are not using. I don't understand when should I use aggregation function, In the same way how to deal with  correlation features and should I delete  the highly correlated features with target variable.
please help me to understand these things.

The Kaggle example you mentioned has a lot of ungrouped raw data. Some of the aggregations are just to get summary information about some credit agencies etc.
If some of the features are related in terms of their meaning, e.g. if day0_spend, day1_spend, day2_spend are three features of a person, then it makes sense to calculate sum/min/max of these, creating new features. So, in some cases, it makes sense to apply aggregation functions to a subset of features. But, I doubt the benefit you'd get by blindly selecting a subset and a function.

... should I delete the highly correlated features with target
variable

No, they're valuable features because of this correlation.
