Ways to improve the quality of Machine Learning

Before we venture off on our journey to improvise what is probably the biggest field of study, research, and development, it is only apt and fitting that we understand it first, even if at a very basic level.

So, just to provide a very brief overview for understanding, Machine Learning or ML for short is one of the hottest and the most trending technologies in the world at the moment, which is actually derived from and works as a subsidiary application of the field of Artificial Intelligence. It involves making use of abundant pieces of discrete datasets in order to make the power systems and computers of today sophisticated enough to understand and act the way humans do. The dataset that we give to it as the training model works on various underlying algorithms in order to make computers even more intelligent than they already are and help them to do things in a human way: by learning from past behaviors.

CHALLENGES IN CREATING GOOD MACHINES AND MODELS

Quality data

Many people and programmers often take the wrong step in this crucial juncture thinking that the quality of the data would not affect the program much. Sure, it would not affect the program but will be the key factor in determining the accuracy of the same.

Scalability

Absolutely no ML program/project worth it's salt in the entire world can be wrapped up in a single go. As technology and the world change, day by day so does the data of the same world change at torrid paces. This is why the need to increase/decrease the capacity of the machine in terms of its size and scale is highly imperative.

Model designing

The final model that has to be designed at the end of the project is the final piece in the jigsaw, which means there cannot be any redundancies in it. But many a time it happens that the ultimate model nowhere pertains to the ultimate need and aim of the project.

THE precautionary measures

When we talk or think of Machine Learning, we should keep in mind that the learning part of it is the deciding factor which is done by humans only. So here are some things to keep in mind in order to make this learning part more efficient:

Choose the right data set: one that pertains and sticks to your needs and does not wander off from that course in high magnitudes. Say, for example, your model needs images of human faces, but rather your data set is more of an assorted set of various body parts. It will only lead to poor results in the end.
Make sure that your device/workstation is devoid of any pre-existing bias which would be impossible for any kind of math/statistics to catch. Say, for example, a system contains a scale that has been trained to round-off a number to its nearest hundred. In the event your model contains precise calculations where even a single decimal digit would cause high fluctuations, it would be highly troublesome. Test the model on various devices before proceeding.
The processing of data is a machine process, but creating its dataset is a human process. And as such, some amount of human bias can consciously or unconsciously be blended into it. So, while creating large datasets, it is important that one try and keep in mind all the possible setups possible in the said dataset.

Source: HOB

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