Machine Learning
Algorithm
A set of rules or steps used by a machine to learn patterns from data and make decisions.
Artificial Neural Network (ANN)
A computing system inspired by the structure of biological neural networks, used in deep learning.
Bias
A systematic error in a machine learning model that leads to inaccurate results due to incorrect assumptions.
Classification
A type of supervised learning where the model categorizes inputs into predefined classes (e.g., spam detection).
Clustering
An unsupervised learning technique used to group similar data points together based on shared characteristics.
Deep Learning
A subset of machine learning using neural networks with multiple layers to analyze complex patterns.
Feature Engineering
The process of selecting, transforming, and creating variables to improve a model’s performance.
Gradient Descent
An optimization algorithm used to minimize errors in machine learning models by adjusting weights iteratively.
Overfitting
A situation where a model learns noise instead of patterns, performing well on training data but poorly on new data.
Reinforcement Learning
A learning paradigm where an agent interacts with an environment and learns by receiving rewards or penalties.
Supervised Learning
A machine learning approach where models are trained using labeled data to make predictions.
Unsupervised Learning
A machine learning method where models learn from unlabeled data to find hidden patterns or structures.
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