The demand for text classification is growing significantly in web searching, data mining, web ranking, recommendation systems, and so many other fields of information and technology. This paper illustrates the text classification process on different datasets using some standard supervised machine learning techniques. Text documents can be classified through various kinds of classifiers. Labeled text documents are used to classify the text in supervised classifications. This paper applies these classifiers on different kinds of labeled documents and measures the accuracy of the classifiers. An Artificial Neural Network (ANN) model using Back Propagation Network (BPN) is used with several other models to create an independent platform for labeled and supervised text classification process. An existing benchmark approach is used to analyze the performance of classification using labeled documents. Experimental analysis on real data reveals which model works well in terms of classification accuracy.

Machine learning is the concept of programming the machine in such a way that it learns from its experiences and different examples, without being programmed explicitly. It is an application of AI that allows machines to learn on their own. Machine learning algorithms are a combination of math and logic that adjust themselves to perform more progressively once the input data varies. Being a general-purpose, easy to learn and understand language, Python can be used for a large variety of development tasks. It is capable of doing a number of machine learning tasks, which is why most algorithms are written in Python.

There are so many machine learning algorithms out there, and we can find different kinds of overviews and cheat sheets. When trying to connect the dots of the multitude of machine learning algorithms, I discovered is that there are often several approaches to build/understand an algorithm. There is no better way, and each approach allows us to understand one aspect. One approach can be easier than another depending on what you already know. My aim in this article is to find a meaningful way to connect them.

How to define supervised machine learning? To answer this question concretely, one usually comes up with a machine learning algorithm. But which one would you choose? Which one is the most intuitive to understand for beginners? When I try to decide the order of the three main categories, I wanted to reflect that there is a strong human intuition.

Machine learning is the concept of programming the machine in such a way that it learns from its experiences and different examples, without being programmed explicitly. It is an application of AI that allows machines to learn on their own. Machine learning algorithms are a combination of math and logic that adjust themselves to perform more progressively once the input data varies. Being a general-purpose, easy to learn and understand language, Python can be used for a large variety of development tasks. It is capable of doing a number of machine learning tasks, which is why most algorithms are written in Python.

An overview of supervised machine learning algorithms that are commonly used. Supervised learning algorithms try to predict a target (dependent variable) using features (independent variables). Depending on the characteristics of target variable, it can be a classification (discrete target variable) or a regression (continuous target variable) task. If you are here reading this post, you are well aware of the importance of machine learning. Prevalence of machine learning has been increasing tremendously in the recent years due to the high demand in many business areas and the advancements in technology.