Thursday, August 2, 2018

What is Machine Learning

What is Machine Learning?

Machine learning (ML) is part of Artificial Intelligence (AI), uses the technique that teaches computers how to do tasks by learning data. 

ML uses difference algorithms to find the pattern, similarity, matches for data classification etc to predict the results.

Machine Learning uses different types of learning mechanisms.
  • Supervised Learning
  • Unsupervised Learning.
In Supervised Learning method, train the model with the known input and output data which predicts the future results. 
In Unsupervised Learning method, train the model using the information that is neither classified and allow the algorithm to act on the information without any guidance. 
Each learning method has sub-categories as mentioned below.
  • Supervised Learning
    • Classification 
Classification learning method is used for categorising a certain observation into a group. 
For ex: 
      • A simple use case would be, to predict if a given email is spam or not? 
      • Classifying consumers reasons of visit in store in order to send them a personalized campaign.
Classification Algorithms
      • Discriminant analysis
      • K-nearest neighbor
      • Support Vector Machine (SVM)
      • Boosted decision trees
      • Bagged decision trees

    • Regression
Regressing learning method is used for predicting and forecasting for continuous values. 
For ex: 
      • Predicting a heart attack based on data from an electro cardiogram
Regression Algorithms
      • Linear model
      • Nonlinear model
      • Regularization
      • Stepwise regression

  • Unsupervised Learning
    • Clustering
Clustering is the process of grouping similar entities together. The goal of this unsupervised machine learning technique is to find similarities in the data point and group similar data points together. 
For Ex: 
      • You can identify different groups/segments of customers and market each group in a different way to maximize the revenue. 
Clustering Algorithms
      • Hierarchical clustering 
      • K-means clustering 
      • Gaussian mixture models
      • Hidden Markov models 
      • Self-organizing maps 
      • Fuzzy c-means clustering





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