Traditional programmers hit a stone-wall when they start working on Machine Learning (ML) development projects. They were used to coding based on design derived from requirements, logic/rules and when they move into a new paradigm in which rules are automatically generated from data, they are surprised. In ML paradigm, we do not find any output code and we see an output in the form of a binary object called model that goes into production. You may be wondering without measuring lines of code,… Continue

Is there any similarity in learning process between humans and machines. Human learning process varies from person to person. Once a learning process is set into the minds of people, it is difficult to change it. But, in Machine Learning (ML), it is easy to change the learning method by selecting a different algorithm. In ML, we have well defined processes to understand and estimate the accuracy in learning. Estimation of human learning is usually done through examinations and it cannot be… Continue

Models are simplification or approximation of reality and hence they will not capture all of reality. “All models are wrong, but some are useful” is a famous quote by George Edward Pelham Box (1919–2013). George Box was a British mathematician and professor of statistics at the University of Wisconsin. Statisticians develop theoretical models to predict the behaviour of certain process. The meaning of this quote is that every single model will be wrong and it never represents the exact…

Continue

Machine Learning (ML) development is an iterative process in which the accuracy of predictions made by the models is continuously improved by repeating the training and evaluation phases. In each of these iterations, certain parameters are tweaked continuously by developers. Any parameter manually selected based on learning from previous experiments qualify to be called a model hyper-parameter. These parameters represent intuitive decisions whose value cannot be estimated from data or from…

Continue