Machine Learning

Few-Shot Learning is a machine learning technique that enables models to learn from a small amount of labeled data.

One-Shot Learning is a machine learning technique that enables models to recognize and classify new instances with minimal training data.

Zero-Shot Learning is a machine learning technique that enables models to generalize and make accurate predictions on new, unseen classes without any training examples.

Unsupervised Learning Algorithms is a machine learning technique that enables data analysis without the need for labeled data or predefined outputs.

Support Vector Machines is a powerful machine learning algorithm used for classification and regression tasks.

Random Forests is a machine learning algorithm that combines multiple decision trees to make accurate predictions.

Precision and Recall is an evaluation metric used in information retrieval and machine learning to measure the effectiveness of a predictive model.

Overfitting Regularization Techniques is a set of methods used to prevent models from fitting too closely to training data and performing poorly on unseen data.

Overfitting and Underfitting is a common challenge in machine learning where the model’s performance is impacted by the complexity or simplicity of the model.

Naive Bayes Classifiers is a machine learning algorithm that uses Bayes’ theorem to make predictions based on the probability of certain events occurring.

Multi-Task Learning is a machine learning technique that allows models to learn multiple related tasks simultaneously, leading to improved performance and efficiency.

Loss Functions is a mathematical function that measures the difference between the predicted and actual values in machine learning models.

Learning Rate is an important parameter in machine learning algorithms that controls the speed at which a model learns from data.

K-Nearest Neighbors is a machine learning algorithm that classifies data based on its proximity to other data points.

Instance-based Learning is a machine learning approach that makes predictions based on the similarity of new instances to previously seen instances.