FAQ on Machine Learning

Keep the design of the model simple. Try to reduce the noise in the model by considering fewer variables and parameters. Cross-validation techniques such as K-folds cross validation help us keep overfitting under control. Regularization techniques such as LASSO help in avoiding overfitting by penalizing certain parameters if they are likely to cause overfitting. To … Read more

Classification is used to produce discrete results, classification is used to classify data into some specific categories .for example classifying e-mails into spam and non-spam categories. Whereas, We use regression analysis when we are dealing with continuous data, for example predicting stock prices at a certain point of time. Classification and regression are two fundamental … Read more

 it is a supervised machine learning algorithm where we need to provide the labelled data to the model it then classifies the points based on the distance of the point from the nearest points. Whereas, on the other hand, K-Means clustering is an unsupervised machine learning algorithm thus we need to provide the model with … Read more

In supervised machine learning algorithms, we have to provide labelled data, for example, prediction of stock market prices, whereas in unsupervised we need not have labelled data, for example, classification of emails into spam and non-spam. In machine learning, the distinction between supervised and unsupervised learning lies primarily in the presence or absence of labeled … Read more

E-commerce websites like Amazon make use of Machine Learning to recommend products to their customers. The basic idea of this kind of recommendation comes from collaborative filtering. Collaborative filtering is the process of comparing users with similar shopping behaviors in order to recommend products to a new user with similar shopping behavior. The “People who … Read more