Found 664 Articles for Machine Learning

Introduction Data Science and machine learning are the trending fields in current business scenarios, where almost all kinds of product and service-based companies are leveraging Machine learning and data science techniques to enhance their productivity and advance their workflows. In such cases, many data aspirants are trying to enter the field, but the issue here is with the role. As one single individual can not master all the fields in AI and hence the need for selection of roles comes, which becomes very confusing but important for the career. In this article, we will discuss the machine ... Read More

Introduction Most companies and businesses are leveraging and integrating data science and machine learning techniques into their workflow to enhance their sales, marketing, and productivity of the projects and workings on the same. The viral fever, or the TVF, is one of the biggest content creation companies which creates movies, web series, and serials, which is India based company. The TVF uses data science and machine learning techniques to enhance its productivity and user experience. In this article, we will discuss how TVF makes a profit using data science and machine learning, which techniques they might be using, ... Read More

Introduction In machine learning, the performance and accuracy of the model completely depend n the data that we are feeding to it, and hence it is the most influential parameter in model training and model building. Mainly while dealing with the supervised machine learning problems, we have mostly categorical and continuous variables in the dataset. There are some benefits of converting categorical variables into continuous variables. In this article, we will discuss some of the benefits of converting categorical variables to continuous variables, how it affects the model's performance, and what is the core idea behind doing so. ... Read More

Introduction Data analysis is an approach in the field of data science and machine learning where the dataset is analyzed well in order to get the relationship between dataset features and get an idea about the behavior of the data and its parameters. In data analysis, trial and error play a major role while developing a machine learning model. It has certain advantages that allow data analysts or data scientists to make the model more reliable and predictive according to the dataset available. In this article, we will discuss the role of trial and error in data analysis, ... Read More

Logistic regression is a type of classification algorithm used in machine learning very frequently. It is one of the easiest and most efficient classification algorithms, which is tried out on almost every model built for classification problems. However, we can also use logistic regression for solving regression problems, although in such cases, we cannot expect great accuracy and reliability of the model. In this article, we will discuss logistic regression, how we can use it for regression problems, what steps are needed, and the code example for the same. This article will help one to understand the behavior of ... 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 also called the expected value. The mean itself is not restricted to this simple form but has different types such as the arithmetic mean, geometric mean, harmonic mean, and weighted average. It is given mathematically as, $$\mathrm{Mean=\frac{\sum x}{N}}$$ where, x = set of observations N = number of observations Different ... 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 cause and effect as well as in predictive analysis. In this article let us explore various regression techniques available. Regression Techniques Linear Regression − It is the simplest of all regression techniques. In Linear Regression the independent and the target variable are linearly related or dependent on each ... 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 this article let us understand the training process of Unigram Tagger in NLP. Unigram Tagger and its training using NLTK WORKING The UnigramTagger is inherited from the ContextTagger. A context() method is implemented. The context method has the same arguments as the choose_tag() From the context() method, a ... Read More

Introduction In NLP tokenizing text into sentences is a very crucial preprocessing task. Tokenization is the process of breaking the text corpus into individual sentences. In NLTK, the default tokenizer does a good task to tokenize text however it fails to do so in cases where the text contains punctuations, symbols, etc. that are non-standard. In such cases, we need to train a tokenizer. In this article let us explore the training of a tokenizer and also see the usage of filter words or stopwords. Tokenizing a Sentence in NLP The default tokenizer in NLTK can be used on the ... 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 Synsets The below diagram shows the structure of WordNet. In WordNet, the relationship between words is maintained. For example, words like sad are similar and find the application under similar contexts. These words can be interchanged during usage. These kinds of words are grouped for synsets. Each synset is ... Read More