Machine Learning

Algorithms that improve from data rather than from explicit instructions.

Parent category: Artificial Intelligence

Articles (9)

• What Is a Support Vector Machine and How Does It Work?
  A support vector machine separates two classes with the widest possible margin, and only the training points closest to that boundary decide where it falls.
• Open Datasets for Machine Learning: The Essential Landscape
  The open datasets that built modern machine learning — ImageNet to Common Crawl — where to find them, their licenses, and how to choose one for your project.
• k-Nearest Neighbors
  A simple instance-based machine learning algorithm that classifies or regresses by majority vote among the k closest training examples.
• k-Means Clustering
  An unsupervised learning algorithm that partitions data into k clusters by iteratively assigning points to the nearest centroid.
• Cross-Validation
  Resampling techniques for estimating how well a model performs on unseen data, most commonly k-fold cross-validation.
• Gradient Descent
  The iterative optimization algorithm at the heart of training machine learning models: how it works, its variants, and its pitfalls.
• Overfitting and Regularization
  When models memorize instead of generalize, and the standard techniques — from data to penalty terms — that prevent it.
• Decision Trees and Random Forests
  Interpretable rule-based models built by recursive splitting, and the forests of randomized trees that made them competitive.
• Reinforcement Learning
  How agents learn from rewards and punishments through trial and error: Markov decision processes, value functions, and policy learning.

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