Introduction Boosting is a class of ensemble modeling algorithms where we build a strong model from several weak models. In boosting all the classifiers are present in series. First, a single model is trained on the actual training data. Then the second classifier is built which is trained on the ... Read More

Introduction The average value of a set of data points, observations, or values is known as the Mean of the data. It is the measure of the central tendency. Mathematically, the mean is obtained by dividing the sum of values by the number of values or observations. It is ... Read More

Introduction Regression is the technique of predictive modeling to analyze the relationship between the independent variable and the dependent variable. The relation between the target(dependent variable) and the independent variable may be either linear or non-linear. The target is always continuous value and regression is widely used in forecasting, understanding ... Read More

Introduction A single token is called a unigram. A unigram tagger is the type of tagger that requires only one word for inferring the Parts of Speech of a word. It has the context of a single word.NLTK library provides us with the UnigramTagger and is inherited from NgramTagger. In ... Read More

Introduction WordNet is a large database of words present in the NLTK library in present in many languages for Natural Language related use cases. NLTK library has an interface known as Synset that allows us to look for words in WordNet. Verbs, Nouns, etc. are grouped into sunsets. WordNet and ... Read More

In today’s date, Artificial Intelligence has impacted our lives way in that nothing else could have impacted. It has changed how our daily jobs are done and has involvement in major areas of our industry, lives, and almost everything. Here in this article, we are going to see some areas ... Read More

Introduction Categorical data is a type of data that takes a fixed number of values and there is no possible logical order in such variables. Categorical variables can be blood groups, yes-no situations, gender, ranking (ex. first, second, third), etc. Categorical variables most of the time undergo encodings such as ... Read More

Introduction Machine Learning is a diverse area with a wide range of applications in many domains. As far as we have an abundance of usable data and a problem to solve in a particular area, Machine Learning can do wonders. Machine Learning Projects can vary in terms of complexity from ... Read More

Introduction Non-Negative Matrix Factorization (NMF) is a supervised algorithm used to represent data into lower dimensions which reduces the number of features while preserving enough basic information to construct the original matrix from the reduced feature space. In this article, we will be going explore more about NMF and how ... Read More

Introduction Image Segmentation is the process of dividing a digital image into smaller groups so that processing and analyzing the larger images becomes easier and simpler. Region and Edge-based segmentation are different types of Image Segmentation. Before diving into Region and Edge based Segmentation, let us have a brief overview ... Read More