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Machine Learning Algorithms used in solving
some of the popular
real world problems
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* The majority of all
the real world problems in machine
learning are classification and regression problems which are to be performed over structured data ( that
means
it doesn't take into account much of unstructured data into the account )
* In the
real world machine learning environment
, deep learning also plays a very shallow and limited role in the field of machine
learning and therefore it is considered as one of the smallest factors that go into building
of applications over machine learning
* One of the the mostly
used algorithm to augment the model
performance is XGBoost algorithm
.This was created by developer and machine learning engineer Tianqi Chen who changed the face of
applied machine learning which as already mentioned is the best case use of
machine learning algorithms and the way augmentation of built model
performances is done which come under the category of gradient boosting
algorithms which also happens to work over and on top of structured data .
* Therefore , gradient boosters like XGBoost are considered as the gold
standard for modelling over
structured data problems used in data competitions .Again it needs to be re-iterated
and kept in memory eternally that
almost all of the data problems use XGBoost and other gradient boosters for working over any data problem .
* It is
also believed that the top cloud based companies like Google , Microsoft and
Amazon also use gradient boosters over their cloud paltform for analysis into structured data problems
* Google's Cloud based Machine Learning Platform is called as
AutoML . In this platform and
application , the AutoML tables use gradient boosters and deep learning models for hyperparameter tuning
Last
modified: 14:20
