Enhance Your Understanding with Machine Learning Concept Cards for quick learning
A field of study that enables computers to learn and make decisions without being explicitly programmed.
A type of machine learning where the model learns from labeled data to make predictions or decisions.
A type of machine learning where the model learns from unlabeled data to find patterns or relationships.
A type of machine learning where the model learns through trial and error interactions with an environment to maximize rewards.
A set of algorithms inspired by the structure and function of the human brain, used for pattern recognition and machine learning tasks.
A subfield of machine learning that focuses on artificial neural networks with multiple layers, capable of learning hierarchical representations of data.
A field of study that focuses on the interaction between computers and human language, enabling machines to understand, interpret, and generate human language.
A field of study that focuses on enabling computers to gain high-level understanding from digital images or videos.
The process of cleaning, transforming, and organizing raw data to make it suitable for machine learning algorithms.
The process of assessing the performance and generalization ability of a machine learning model.
The process of selecting a subset of relevant features from a larger set of features to improve model performance and reduce overfitting.
The process of reducing the number of random variables or features in a dataset while preserving its important structure and relationships.
Techniques that combine multiple machine learning models to improve prediction accuracy and reduce variance.
Algorithms used to group similar data points together based on their characteristics or attributes.
Algorithms used to assign predefined labels or categories to input data based on their features.
Algorithms used to predict continuous numerical values based on input data and their relationships.
Measures used to assess the performance of a machine learning model, such as accuracy, precision, recall, and F1 score.
The tradeoff between a model's ability to fit training data well (low bias) and its ability to generalize to unseen data (low variance).
Overfitting occurs when a model learns the training data too well, resulting in poor generalization to new data. Underfitting occurs when a model is too simple to capture the underlying patterns in the data.
The process of selecting the optimal values for the hyperparameters of a machine learning model to improve its performance.
A technique used to assess the performance of a machine learning model by splitting the data into multiple subsets for training and testing.
Bias measures how far off the predictions are from the true values, while variance measures the variability of predictions for a given input.
An optimization algorithm used to minimize the cost function of a machine learning model by iteratively adjusting the model's parameters.
An algorithm used to train neural networks by propagating the error backwards from the output layer to the input layer.
Functions applied to the outputs of neurons in a neural network to introduce non-linearity and enable the network to learn complex patterns.
Functions used to measure the error or discrepancy between the predicted and actual values in a machine learning model.
Algorithms used to find the optimal values for the parameters of a machine learning model, such as gradient descent and stochastic gradient descent.
Neural networks designed for processing structured grid-like data, such as images, by applying convolutional filters to extract relevant features.
Neural networks designed for processing sequential data, such as time series or natural language, by using feedback connections to retain information over time.
A class of neural networks that consists of a generator network and a discriminator network, trained together in a competitive setting to generate realistic data.
A technique where knowledge gained from training one machine learning model is applied to a different but related model to improve performance.
A technique used to represent words as dense vectors in a continuous vector space, capturing semantic relationships between words.
Systems that predict and recommend items or content to users based on their preferences and behavior patterns.
A flowchart-like structure where each internal node represents a feature, each branch represents a decision rule, and each leaf node represents an outcome.
Ensemble learning method that combines multiple decision trees to make predictions, reducing overfitting and improving accuracy.
A supervised learning algorithm that separates data points into different classes by finding the optimal hyperplane with the largest margin.
A simple yet powerful algorithm that classifies new data points based on the majority class of their k nearest neighbors in the training set.
A dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving its important structure.
An unsupervised learning algorithm that partitions data points into k clusters based on their similarity, aiming to minimize the within-cluster sum of squares.
A classification algorithm that models the probability of a binary or categorical outcome using a logistic function.
A classification algorithm based on Bayes' theorem with the assumption of independence between features.
A common loss function used in regression tasks that measures the average squared difference between the predicted and actual values.
A common evaluation metric that measures the proportion of correctly classified instances out of the total number of instances.
Evaluation metrics used in binary classification tasks. Precision measures the proportion of true positives out of the predicted positives, while recall measures the proportion of true positives out of the actual positives.
A metric that combines precision and recall into a single value, providing a balanced measure of a model's performance.
A table that summarizes the performance of a classification model by showing the counts of true positives, true negatives, false positives, and false negatives.