What is KMeans? KMeans is a popular clustering algorithm used in machine learning and data analysis. It's used to partition a dataset into distinct, non-overlapping clusters. The goal of KMeans is...
What is KMeans? KMeans is a popular clustering algorithm used in machine learning and data analysis. It's used to partition a dataset into distinct, non-overlapping clusters. The goal of KMeans is...
What is K-nearest neighbours? K-Nearest Neighbours (KNN) is a simple and widely used classification and regression algorithm in machine learning. It falls under the category of supervised learning...
What is AdaBoost? AdaBoost, short for Adaptive Boosting, is a machine learning algorithm that belongs to the ensemble learning techniques. Ensemble learning involves combining the predictions of...
What is gradient boosting? Gradient Boosting is a powerful machine learning technique for classification and regression tasks. It's an ensemble learning method that combines the predictive abilities...
What is grid search? Grid search is a hyperparameter tuning technique commonly used in machine learning to find a given model's best combination of hyperparameters. Hyperparameters are parameters...
What is softmax regression? Softmax regression, or multinomial logistic regression or maximum entropy classifier, is a machine learning technique used for classification problems where the goal is...
What is dropout in neural networks? Dropout is a regularization technique used in a neural network to prevent overfitting and enhance model generalization. Overfitting occurs when a neural network...
What is multi-class classification in machine learning? Multi-class classification is a machine learning task that aims to assign input data points to one of several predefined classes or...
What is meta-learning? Meta-learning, or learning to learn, is a machine learning approach that focuses on improving the learning process rather than just learning a specific task or problem....
What is ensemble learning in machine learning? Ensemble learning is a machine learning technique that combines the predictions of multiple individual models to improve a machine learning algorithm's...
What is active learning in machine learning? Active learning is a machine learning technique that involves iteratively selecting and labelling the most informative examples from an unlabeled dataset...
Outlier detection in machine learning Outlier detection is a task in machine learning and data analysis involving identifying points that deviate significantly from the rest of the data. These data...
Welcome to our blog post, where we delve into a critical aspect of machine learning that often goes unnoticed but can significantly impact the reliability of our models - data leakage. As...
What is a large language model? A large language model (LLM) is a type of artificial intelligence (AI) trained on massive text and code datasets. This allows them to learn the patterns and...
What is zero-shot classification? Zero-shot classification is a machine learning approach in which a model can classify data into multiple classes without any specific training examples for those...
What is feature scaling in machine learning? Feature scaling is a preprocessing technique used in machine learning and data analysis to bring all the input features to a similar scale. It is...
What is k-fold cross-validation? K-fold cross-validation is a popular technique used to evaluate the performance of machine learning models. It is advantageous when you have limited data and want to...
Introduction to word embeddings Word embeddings have become a cornerstone of Natural Language Processing (NLP), transforming how machines process and understand human language. These vector...
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