The engineering behind Machine Learning

The Machine Learning Engineering

The machine learning technology makes a deep-dive interest of maximum computer science engineers now a days. A huge number of people from different domains has involved in this technology to develop something amazing and better.

In machine learning convention of being tautology for solution of each and every problems doesn’t exist currently. Researchers and various organizations have trying to find the way to make thing applied in machine learning completely.

The horizon of people’s in machine learning increases exponentially with growing trends of it’s applications in real-world and industries. But in the world of statisticians the current dive of majority of public in machine learning quite makes it hypothesis beyond intuitive. The real engineering behinds the machine learning is pure statistics and applied mathematics includes probability and multivariate calculus. The great people behinds generation of this super-exciting technology is Arthur Samuel and contributions of Tom Mitchell, Geoffrey Hinton and many more peoples.

The engineering behind machine learning algorithm is running of mathematical formula which compute the cost or find the insights from the data. The Linear regression algorithm (supervised learning) based on Straight Line formula or K-means Clustering(unsupervised learning algorithm) based on circle formula and centroid concepts which is derived from mathematics. Also take the one of famous supervised learning algorithm Naive Bayes which is based on Naive theorem of probability. So the real engineering behinds machine learning which mimics machine to learn is the principle of training data on particular machine learning algorithm according to problem given (classification or regression) which derived the mathematics and statistics concepts.

For example:- Face recognition and Face identification is quite popular computer vision problems now-a-days both in organizations and institutes. Every one wants to implement this methodology in their vision applications to automate the process of visualization and detection. The real principle in which this identification and recognition perform in facial system is based on the two process facial extraction or selection and classification. The facial recognition algorithms include principle component analysis a mathematical approach for similar component analysis from a vector-space including various graph line matching. Also the recognition process includes Fisher face algorithm (mathematics based  algorithm which perform different calculations on face of individual including distance of forehead clipping with nose) which helps to calculate the identity.

The complete machine learning engineering is combination of both statistics and mathematics the process to make a particular algorithm which makes the process of learning of machines(computer system) without human inference makes possible using the listed domains.

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