Machine learning

Machine learning

Explore Our Recent Machine learning Technologies

Supervised Learning
In supervised learning, the machine learning model is trained on labeled data, where the input data is accompanied by corresponding output labels. The model learns from this labeled data to make predictions or classify new, unseen data instances. Common algorithms for supervised learning include linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.
Unsupervised Learning
Unsupervised learning involves training machine learning models on unlabeled data, where there are no predefined output labels. The goal is to discover patterns, structures, or relationships within the data. Clustering and dimensionality reduction are common techniques used in unsupervised learning. Algorithms like k-means, hierarchical clustering, and principal component analysis (PCA) are commonly used in unsupervised learning tasks.
Reinforcement Learning
Reinforcement learning is a type of machine learning where an agent learns to interact with an environment to maximize a reward signal. The agent takes actions in the environment, receives feedback in the form of rewards or penalties, and learns to take actions that maximize the cumulative reward over time. This approach is used in areas such as robotics, game playing, and autonomous systems.

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