Algorithms Classification - Machine Learning

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Recommender Learning

Supervised Learning

• Basic
  • Teaching a machine how to do something
  • Giving algorithm dome data set (Training set) in which right answers are given
  • Machine knows the features
  • Example => Support Vector Machine (SVM)
• Methods
  • Regression
    • Predict continuous valued output
    • Find out the value of the label using previous data
  • Classification
    • Discrete valued output (0 or 1 or more)
    • Classify which label a given set of features belongs to
    • Example => Decision Tree, Naive based, Random forest, K Nearest Neighbor

Unsupervised Learning

• Basic
  • Let it learn by itself
  • Find some structure in given data
  • Machine seeing the data for the first time
• Methods
  • Clustering
    • Discover the inherent groupings in the data
    • Cocktail Party Problem
      • 2 People speaking at the same time recorded by 2 different microphones
      • Algorithm tries to separate the voices
      • 
  • Association
    • Association rule learning problem such as people that buy X also tends to buy Y

Reinforcement Learning

Recommender Learning