What is Artificial Intelligence? The term Artificial Intelligence comprises of two words ‘Artificial’ and ‘Intelligence’, where, Artificial means ‘copy of something natural’ and ‘Intelligence’ means ‘able to think.’ So, Artificial Intelligence can be defined as a copy of a human brain with thinking ability. According to John McCarthy, who is known as theContinue Reading
Introduction to Association Rule Learning Association rule learning extracts alliances among the datapoints in a huge dataset. It incorporates the concept of data mining, which helps in finding useful commercial associations or regularities between the variables. It is presently in use in the sales industry to predict if the personContinue Reading
Introduction to Hierarchical Clustering The other unsupervised learning-based algorithm used to assemble unlabeled samples based on some similarity is the Hierarchical Clustering. There are two types of hierarchical clustering algorithm: 1. Agglomerative Hierarchical Clustering Algorithm It is a bottom-up approach. It does not determine no of clusters at the start. ItContinue Reading
Introduction to K-means clustering K-mean clustering comes under the unsupervised based learning, is a process of splitting an unlabeled dataset into the clusters based on some similarity patterns present in the data. Given a set of m nos. of the data item with some certain features and values, the mainContinue Reading
Introduction to ML Clustering Algorithm Clustering falls under unsupervised learning methods. In this, the machine is provided with a set of unlabeled data, and the machine is required to extract the structure from the data from its own, without any external supervision. It searches for similar patterns in the datasetContinue Reading
Introduction to Random Forest Random forest is an ensemble-based supervised learning model. The concept of random forest is used in both classifications as well as in the regression problems. Basically, in ensemble-based learning, multiple algorithms are combined to build a robust prediction model, such that these algorithms can be similarContinue Reading
Introduction to Naïve Bayes Algorithm in Machine Learning The Naïve Bayes algorithm is a classification algorithm that is based on the Bayes Theorem, such that it assumes all the predictors are independent of each other. Basically, it is a probability-based machine learning classification algorithm which tends out to be highlyContinue Reading
Introduction to Decision Trees Decision trees are one of the most powerful classification algorithm that falls under supervised learning-based algorithms. It is used as a tool for making predictions and can be incorporated in different fields. With the help of decision trees, the data-set can be divided in different waysContinue Reading
Support Vector Machines are part of the supervised learning model with an associated learning algorithm. It is the most powerful and flexible algorithm used for classification, regression, and detection of outliers. It is used in case of high dimension spaces, where each data item is plotted as a point inContinue Reading
The Logistic regression model is a supervised learning model which is used to forecast the possibility of a target variable. The dependent variable would have two classes, or we can say that it is binary coded as either 1 or 0, where 1 stands for the Yes and 0 standsContinue Reading
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