Use of Correlation Map in Machine Learning
It depends. A high correlation between two features suggests that they represents almost the same information. For some problems like clustering, it is always useful to remove redundant features while some algorithm like Gradient Boosting in xgboost is not affected at all by such features. So, it all depends on what you want to do with your data set.

As per my opinion, if your dataset has too many features, then I would suggest to check the correlation between those features and apply PCA to reduce the dimensionality of your dataset especially if you are doing tasks like clustering or regression.