Naive Bayes Classifier is a classification technique based on Bayes’ Theorem. It is base on the principle that the predictors are independent of each other. In simple words, we can say that the Naive Bayes classifier assumes that the presence of a particular feature in a class is independent(Unrelated) with the presence of any other feature in the same class.

Support Vector is one of the strongest but mathematically complex supervised learning algorithms used for both regression and Classification. It is strictly based on the concept of decision planes (most commonly called hyperplanes) that define decision boundaries for the classification. A decision plane is one that separates between a set of data having different class memberships.""

K-means clustering is one of the easiest, simple, and most popular unsupervised machine learning algorithms. The goal of K-means is simple: group similar data points together and recognize the underlying patterns. K-means looks for a fixed number (k) of clusters in a dataset, to accomplish this goal. A cluster refers to a collection of data points aggregated together exhibiting certain similarities. The target number k is a hyperparameter (i.e. You have to set its value by yourself before you the learning process begins), which refers to the number of centroids you need in the dataset. A centroid is a fictional or real location representing the center of the cluster. The centroid can be defined in various ways such as by the mean or medoid of the objects (or points) assigned to the cluster (we will deal with K medoid in upcoming articles). Every data point is assigned to each of the clusters by reducing the in-cluster sum of squares." " "

Epoch, batch size, and iteration all are the terms that are related to the data sets used during the training of a machine learning model. Let’s understand what these terms mean. For this let’s consider a training set having 10, 00, 000 data examples in it." "

In this article, we will learn about the linear regression algorithm with examples. First, we will understand the basics of linear regression algorithm, and then we will look at the steps involved in linear regression and finally an example of linear regression