Many organizations have become adept at identifying what they need from software development projects, based on a keen understanding of their business goals. Even so, they’re often surprised to find out that the end results don’t achieve the transformative impact they were expecting. Their mistake? Overlooking the importance of Conway’s Law.
In 1967, Melvin Conway coined a phrase at the end of his publication ‘How do committees invent?’ that was subsequently made popular by Fred Brooks in his book The Mythical Man-Month, where he dubbed it ‘Conway’s Law’, which states:
Murphy’s Law Probably one of the most famous of all laws, mostly because it is not only applicable to Software Development.
If something can go wrong, it will. First derivation: If it works, you probably didn’t write it. Second derivation: Cursing is the only language all programmers speak fluently. Conclusion: A computer will do what you write, not what you want.
Defensive programming, version control, doom scenario’s (for those damned zombie-server-attacks), TDD, MDD, etc.
Source https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/
It is used to estimate real values (cost of houses, number of calls, total sales etc.) based on continuous variable(s). Here, we establish relationship between independent and dependent variables by fitting a best line. This best fit line is known as regression line and represented by a linear equation
$$Y= a *X + b$$
The best way to understand linear regression is to relive this experience of childhood.
Don’t get confused by its name! It is a classification not a regression algorithm. It is used to estimate discrete values (binary values like 0/1, yes/no, true/false ) based on given set of independent variable(s). In simple words, it predicts the probability of occurrence of an event by fitting data to a logit function. Hence, it is also known as logit regression. Since, it predicts the probability, its output values lies between 0 and 1 (as expected).
Broadly, there are three types of Machine Learning Algorithms..
1. Supervised Learning How it works: This algorithm consist of a target or outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data.
