Machine Learning | Introduction - 1

Machine Learning deals with the set of algorithm which can learn a pattern in different types of user beahviour and help developer to solve various problems:

  • Collaborative Filtering (Basket Analysis)
  • Classification (Auto Tagging, Spam Filtering)
  • Prediction
  • Pattern recognition (Clustering)
  • Text extraction (Named Entity Recognition)
We can distribute Machine Learning Algorithm in tow categories

  1. Supervised Learning
  2. Unsupervised Learning

Supervised Learning

In simple words Supervised learning means Learn By Examples. We have  prior knowledge about certain domain. Using Supervised Learning we can create a model from existing knowledge which helps us to classify new data. For example:

Spam Classification

We mark existing text as HAM or SPAM and generate a model using this model new text can be categorised in one of the category.

Un-Supervised Learning 

In unsupervised Learning we do not have any supervisor, rather algorithm tries to learn a mapping from existing data and applies to new data. For example:

Document Clustering

In document clustering we try to group similar documents like we have different types are getting posted to some site using this algorithm we can group them in politics, business, technology etc categories.

List of Algorithms

  • Classification
    • Logistic Regression (SGD)
    • Bayesian
    • Support Vector Machines (SVM) 
    • Hidden Markov Model
  • Clustering
    • Canopy Clustering 
    • K-Means Clustering 
    • Fuzzy K-Means 
    • Expectation Maximization 
    • Mean Shift Clustering 
    • Hierarchical Clustering 
    • Dirichlet Process Clustering 
    • Latent Dirichlet Allocation 
    • Spectral Clustering 
    • Minhash Clustering 
    • Top Down Clustering