43 docs tagged with "machine learning"

Implement the AdaBoost algorithm to combine multiple weak classifiers into a strong ensemble model. This feature will visualize the boosting process and support various base learners.

In this post, we will explore Autoencoders, a type of artificial neural network used for unsupervised learning that focuses on efficiently encoding input data and reconstructing it.

This guide covers the Beam Search algorithm, a heuristic search technique commonly used in sequence generation tasks to find the most probable output sequence in machine learning.

CatBoost is an efficient gradient boosting algorithm that handles categorical features well, making it a powerful tool for classification and regression problems.

This post explores Convolutional Neural Networks (CNN), a specialized neural network architecture widely used for tasks involving image processing and computer vision.

In this post, we'll explore DBSCAN, a density-based clustering algorithm used to identify clusters of arbitrary shape and noise in datasets.

In this post, we'll explore the Decision Tree Algorithm, a popular machine learning model used for classification and regression tasks.

An introduction to Deep Q-Networks, a reinforcement learning technique that combines Q-Learning with deep neural networks to handle complex, high-dimensional state spaces.

In this post, we’ll explore the Extra Trees Algorithm, an ensemble learning model used for classification and regression tasks, known for its efficiency and randomness in both feature selection and data sampling.

This post explores Gaussian Mixture Models (GMM), a probabilistic model for representing normally distributed subpopulations within a larger population.

Explore the Gradient Boosting Machines (GBM) algorithm for machine learning, including popular variants like XGBoost and LightGBM. Learn how it builds models sequentially, improving performance by correcting errors from previous models.

In this post, we'll explore Hidden Markov Models (HMMs), a statistical model that represents systems with hidden and observable states, commonly used for sequence data in various domains.

Hierarchical clustering is a method of grouping similar data points into clusters based on their relative distances, creating a hierarchy that can be visualized as a dendrogram.

Implement hierarchical clustering algorithms that build a hierarchy of clusters using either agglomerative or divisive methods. This feature will include visualizations to help users understand the clustering process.

In this post, we'll explore the Independent Component Analysis (ICA) Algorithm, a powerful technique in statistical data analysis.

Implement the K-Means clustering algorithm to partition data into K clusters based on feature similarity. This feature will include visualizations to help users understand the clustering process.

In this post, we'll explore the k-Nearest Neighbors (k-NN) Algorithm, one of the simplest and most intuitive algorithms in machine learning.

In this post, we'll explore the Linear Regression Algorithm, one of the most basic and commonly used algorithms in machine learning.

In this post, we'll explore the Logistic Regression Algorithm, a widely used classification model in machine learning.

This post delves into Long Short-Term Memory (LSTM), a type of recurrent neural network designed to overcome the vanishing gradient problem, enabling better learning of long-term dependencies in sequential data.

In this post, we’ll explore the Naive Bayes Theorem, a fundamental probabilistic algorithm used for classification tasks based on Bayes' Theorem and the assumption of conditional independence.

Build and visualize neural networks with support for feedforward, convolutional, and recurrent architectures. Explore how these models learn from data using backpropagation and gradient descent.

Implement Principal Component Analysis (PCA) to reduce the dimensionality of high-dimensional data while preserving its essential features. Visualize the transformed data to gain insights into underlying patterns.

An introduction to policy gradient methods in reinforcement learning, including their role in optimizing policies directly for better performance in continuous action spaces.

In this post, we’ll explore Principal Component Analysis, a fundamental technique in unsupervised learning used for dimensionality reduction and data visualization.

An overview of the Q-Learning Algorithm, a model-free reinforcement learning method that learns the optimal action-value function to guide decision-making.

In this post, we’ll dive into the Random Forest Algorithm, an ensemble learning model used for classification and regression tasks, known for its robustness and versatility.

This post explores Recurrent Neural Networks (RNN), a class of neural networks designed to handle sequential data and time-series information, commonly used for tasks involving natural language processing, speech recognition, and more.

In this post, we’ll explore the concept of regression in supervised learning, a fundamental approach used for predicting continuous outcomes based on input features.

In this post, we'll explore Reinforcement Learning, a type of machine learning used for decision-making and optimizing actions.

Definition:

The Silhouette Score is a metric used to evaluate the quality of clustering results by measuring cohesion and separation among clusters.

In this post, we'll delve into Singular Value Decomposition (SVD), a matrix factorization technique used in linear algebra with applications in dimensionality reduction, image processing, and recommendation systems.

This post covers Statistical Anomaly Detection, a technique used to identify data points that deviate significantly from the norm based on statistical models and methods.

Stochastic Gradient Descent (SGD) is an optimization algorithm used to minimize the loss function in machine learning and deep learning models.

In this post, we’ll explore the concept of supervised learning, a fundamental approach in machine learning where models are trained using labeled data.

SVM is a powerful machine learning model known for its effectiveness in classification tasks and its ability to handle high-dimensional data.

This post explores Support Vector Machines (SVM), a powerful classification algorithm that finds the optimal hyperplane to separate different classes in high-dimensional datasets.

An overview of t-SNE, a popular technique for visualizing high-dimensional data in two or three dimensions.

This post explores t-SNE (t-distributed Stochastic Neighbor Embedding), a popular dimensionality reduction technique used to visualize high-dimensional data in a low-dimensional space.

This post covers Time Series Forecasting, a method used to predict future data points in a time-ordered sequence based on past data.

In this post, we’ll explore the concept of unsupervised learning, a fundamental approach in machine learning where models are trained using unlabeled data.

XGBoost is a highly efficient and scalable machine learning algorithm known for its accuracy and speed in solving both classification and regression problems.