Rapid Fire on Machine Learning

Let’s consider a scenario of a fire emergency: True Positive: If the alarm goes on in case of a fire. Fire is positive and prediction made by the system is true. False Positive: If the alarm goes on, and there is no fire. System predicted fire to be positive which is a wrong prediction, hence … Read more

Let me explain you this with an analogy: Imagine that, your girlfriend gave you a birthday surprise every year for the last 10 years. One day, your girlfriend asks you: ‘Sweetie, do you remember all the birthday surprises from me?’ To stay on good terms with your girlfriend, you need to recall all the 10 … Read more

It is a statistical error that causes a bias in the sampling portion of an experiment. The error causes one sampling group to be selected more often than other groups included in the experiment. Selection bias may produce an inaccurate conclusion if the selection bias is not identified. Selection bias occurs when the data used … Read more

Suppose your friend invites you to his party where you meet total strangers. Since you have no idea about them, you will mentally classify them on the basis of gender, age group, dressing, etc. In this scenario, the strangers represent unlabeled data and the process of classifying unlabeled data points is nothing but unsupervised learning. … Read more

The answer will depend on the type of company. Here are some examples. Clustering algorithms to build custom customer segments for each type of marketing campaign. Natural language processing for headlines to predict performance before running ad spend. Predict conversion probability based on a user’s website behavior in order to create better re-targeting campaigns. To … Read more