• Supervised Learning (Classification): is powerful when the classifications are known to be correct for instance, when dealing with diseases.
  • Unsupervised Learning (Clustring): can be useful to find hidden structure in unlabeled data.
  • Reinforcement Learning (Regression): can provide powerful tools which allow agents to adopt and improve quickly, even in complex scenarios such as strategy games. It interacts with its environment in discrete time steps.

AI in Steps

Most common steps towards creating artificial intelligence are -

  • Know the Domain, what you are solving for
  • Study the data — Data Mining
  • Cleanse , Normalize Data, develop tools
  • Choose a Model
  • Test with Few Models —> Shortlist the Optimum Models — >Pick the best Model
  • Train/Fine Tune/AB Test The Model
  • Correct If Model Overfitting or Underfitting
  • Quantify The Model — Monitoring Errors, Learnings, Positive Impact

Model Selection

Based on the understanding of the domain you are solving for and data knowledge, one is well equipped to select models that would work best. Some examples -

  1. Supervised Learning
    • k-Nearest Neighbors
    • Linear Regression/Polynomial Regression
    • Support Vector Machines (SVM)
    • Decision Trees, Random Forests
  2. Unsupervised Learning
    • Clustering
    • k-Means
    • Hierarchical Cluster Analysis (HCA)
    • Expectation Maximization
  3. Semi-supervised Learning
  4. Reinforcement Learning
  5. Deep Learning
  6. Recurrent Neural Networks

Library Selection

There are readily available algorithms for different modes of machine learning in different languages, platforms.Some examples -