Machine learning classification

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • You want to determine if an email is “spam” or “not spam”
  • Given an incoming insurance claim, you want to determine what the “next best action” is (e.g., pay it off automatically, send it to a senior claim adjuster, give it to a junior claim adjuster, etc.)
  • You want to predict what the price of a stock will be tomorrow.
  • Image recognition
  • Speech recognition
  • Forecasting
  • Linear and logistic regression
  • Support vector machine
  • Naive Bayes
  • Neural networks
  • Gradient boosting
  • Classification trees and random forest
  • Clustering algorithm, such as K-means, hierarchical clustering or mixture models. These algorithms try to discriminate and separate the observations in different groups.
  • Dimensionality reduction algorithms (which are mostly unsupervised) such as PCA, ICA or autoencoder. These algorithms find the best representation of the data with fewer dimensions.
  • Anomaly detections to find outliers in the data, i.e. observations which do not follow the data set patterns.
  • The most advanced Go player is currently a computer. You can find a paper explaining the system here.
  • Financial Portfolio Management
  • DNA fragment assembly

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Data Scientist, Artificial Intelligence, Machine Learning, Author of “Artificial Intelligence with Python”

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Alberto Artasanchez

Alberto Artasanchez

Data Scientist, Artificial Intelligence, Machine Learning, Author of “Artificial Intelligence with Python”

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