What is a Random Forest classifier? A Random Forest classifier is a machine learning algorithm that falls under ensemble learning. It's used for both classification and regression tasks. The "Random...
The Natural Language Processing (NLP) Blog
Topic: Machine Learning
What Are Gaussian Mixture Models (GMMs)? & How To Python Tutorial With Scikit-Learn
What are Gaussian Mixture Models (GMMs)? Gaussian Mixture Models (GMM) are probabilistic models representing a probability distribution as a mixture of multiple Gaussian (normal) distributions. It...
DBSCAN Made Simple & How To Python Tutorial With Scikit-Learn
What is DBSCAN? DBSCAN stands for "Density-Based Spatial Clustering of Applications with Noise." It is a popular clustering algorithm used in machine learning and data mining to group data points...
Dimensionality Reduction Explained, Top 5 Techniques & How To Tutorial In Python
What is dimensionality reduction in machine learning? Dimensionality reduction is a technique used in machine learning and data analysis to reduce the number of features or variables in a dataset...
Principal Component Analysis Made Easy & How To Python Tutorial With Scikit-Learn
What is the meaning of PCA in machine learning? PCA stands for Principal Component Analysis. It is a statistical technique used in data analysis and machine learning to simplify the complexity of...
One-shot Learning Explained, How It Works & How To Tutorial In Python
What is one-shot learning? One-shot learning is a machine learning paradigm that trains models to recognize new objects or patterns based on a single example or a minimal number of examples....
KMeans Explained, Practical Guide To Data Clustering & How To In Python
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...
K-Nearest Neighbours Explained, Practical Guide & How To Tutorial In Python
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...
AdaBoost: Practical Introduction With How To Python Tutorial For Classification & Regression
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...
Gradient Boosting Explained & How To Tutorials In Python With XGBoost
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...
![Practical Guide To Grid Search [How To In Python For SVM & Logistic Regression]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/08/grid-search.jpg?fit=960%2C540&ssl=1)
Practical Guide To Grid Search [How To In Python For SVM & Logistic Regression]
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...
Softmax Regression Explained And How To Tutorial In Python & PyTorch
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...
Understanding Dropout in Neural Network: Enhancing Robustness and Generalization
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...
![Multi-class Classification Explained With 3 How To Python Tutorials [Scikit-Learn, PyTorch & Keras]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/08/multi-class-classification.jpg?fit=1200%2C675&ssl=1)
Multi-class Classification Explained With 3 How To Python Tutorials [Scikit-Learn, PyTorch & Keras]
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...
Understanding Meta-Learning — How To Be More Effective With Less Data
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....
Top 6 Most Powerful Ensemble Learning Techniques Explained & Algorithms That Implement Them
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...
![Deep Dive into Active Learning in Machine Learning [How To In Python & PyTorch]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/08/active-learning-try-fail.jpg?fit=1200%2C675&ssl=1)
Deep Dive into Active Learning in Machine Learning [How To In Python & PyTorch]
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...
Comparative Guide: Top 7 Outlier Detection Algorithms & How To Tutorials In Python
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...
