What are Recommender Systems?
Recommender Systems are a type of AI that is used to predict what a user might like based on their interests, preferences, and historical data. The recommendation engine is personalized for the user, making suggestions that are not just based on similar tastes but also connections and social context. Recommendations include posts, products, or anything…
Read MoreDifference between R square and Adjusted R square?
Linear Regression, a machine learning algorithm, is widely used and evaluating its performance is essential. Two important metrics that are used to evaluate Linear Regression are R-squared and Adjusted R-squared. These metrics help determine the degree of the model fit and how much of the variance in the target variable is explained by the independent…
Read MoreDifference between Decision tree and Random Forest?
When it comes to the domain of machine learning algorithms, two prevalent models are the decision trees and the random forests. While both are employed for classification and regression, they diverge in their data analysis and model building methodologies. Decision Trees A decision tree is a model that segments the presented data into minor subsets…
Read MoreDifference between Artificial Intelligence, Machine Learning and Deep Learning?
Artificial intelligence (AI) is a term that encompasses computer systems designed to imitate human intelligence. It is an exciting field that has attracted considerable attention in many industries, including finance, hospitality, education and entertainment. Artificial intelligence is planned to simulate human behavior and thought processes, making it one of the most important trends of this…
Read MoreWhat is bag of words?
We are going to talk about a Natural Language Processing concept called the Bag of words model. When you’re applying an algorithm in NLP, it works with numbers and not words or sentences. We can’t feed our text directly into algorithms like that in order to analyze text data, it needs to be converted into…
Read MoreWhat is CatBoost and How Does it Improve Machine Learning?
CatBoost is a powerful machine learning library that was developed by researchers at the University of Montreal, McGill University, and Google Brain. It was designed to speed up the training of deep neural networks and improve the accuracy of predictions in machine learning models. Using CatBoost for Text Classification One of the key benefits of…
Read MoreLightGBM: A High-Performance Gradient Boosting Framework for Machine Learning
LightGBM, an open-source gradient boosting framework, provides swift and accurate solutions for various machine learning applications. Developed by Microsoft Research, LightGBM aims to deliver highly efficient training processes, making it a top choice among large corporations. Parallelized Tree Architecture: The Inner Workings of LightGBM LightGBM’s core lies in its parallelized tree architecture, which allows for…
Read MoreXGBoost: The Decision Tree-Based Ensemble Machine Learning Algorithm
XGBoost is a data science algorithm that has revolutionized predictive modeling. Created in 2013 by Tianqi Chen and Guodong Ji, this machine learning algorithm has become popular due to its remarkable predictive capabilities. In this composition, we will delve into the intricacies of XGBoost, its essential attributes, and why it is a perfect option for…
Read MoreWhat is Stop word in NLP?
Stop words are the most common words in any language that do not carry any meaning and are usually ignored by NLP. In English, examples of stop words are “a”, “and”, “the” and “of”. In NLP, stop words are typically removed from a text before it is processed for analysis. This is done to reduce…
Read MoreDifference between Computer Vision and Machine Learning?
Computer Vision Computer vision is a newer and more advanced technology than machine learning, which researchers started developing in the 1950s. They continued working on this, but the technology wasn’t mature until recently. Computer Vision began as a simple two-dimensional tool, making it easier for scientists to recognize statistical patterns. It was in 1978 that…
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