Machine learning models aim for generalization to new data. Underfitting (too simple) and overfitting (too complex) hinder this. Achieving balance ensures effective, robust performance.
K-Means is a popular clustering algorithm, simplifying data partitioning based on similarity for efficient pattern recognition in diverse applications.
The concepts of linear regression, classification, clustering, and Hidden Markov Model (HMM) represent different types of machine learning algorithms or models.
In machine learning, tensors are crucial multi-dimensional arrays for data representation and manipulation in diverse algorithms.
Machine learning encompasses supervised, unsupervised, reinforcement, semi-supervised, and self-supervised learning paradigms, each tailored for specific tasks and objectives.