Machine Learning Supervised and Unsupervised Learning | The Dope Pedia

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Machine Learning

Machine learning is the science of making a machine learn and behave like a human mind by entering data and information without being explicitly programmed. In other words, we let the machine decide on its own and let it find a solution with the data we entered or it is also said that machine learning is a study of the algorithms that improve automatically with data experience. Since we have been feeding a lot of data to these machines they detect patterns to find solutions. It is considered a part of artificial intelligence. The algorithms of machine learning are built upon simple data also known as Training Data. Machine learning also includes terms like supervised learning, unsupervised learning, clustering, classification, association, a regression that is discussed below.

There are a variety of applications that are used by Machine learning algorithms such as

  1. Speech reorganization  –it is a technology that helps to understand the spoken words, convert them into text and using right set of algorithms, answers the user eg siri, alexa
  • Email filtering – it is process where to organize email according to a specific criteria
  • Computer vision – it helps computer to get a high level of understanding from various videos and images. Tasks such as acquiring processing analyzing and understanding images so that a machine can produce appropriate solutions

Machine Learning can be further categorized

  • SUPERVISED MACHINE LEARNING
    • CLASSIFICATION
      • BINARY CLASSIFICATION
      • MULTI CLASSIFICATION
    • REGRESSION
  • UNSUPERVISED MACHINE LEARNING
    • CLUSTERING
    • ASSOCIATION
  1. SUPERVISED MACHINE LEARNING

 Supervised machine learning means that the machine is trained with data that is well labeled i.e data that you already know the solution and further works with that data set. Supervised learning that helps in predicting the answer with the labeled data. It is the same as a School Teacher helping a student learn things by guiding them

Supervised learning can further be categorized into 2 groups

  1. Classification
  2. Regression

Classification –  Classification is a part of supervised learning where algorithms are also known as the core of a variety of numbers of data mining problems. It can only be used for simple data

Classification can further be categorized into 2 parts

  1. Binary Classification
  2. Multi Classification

Regression – Regression is a type of supervised learning where the output or the answer of the particular problem is the continuous value. It is already known data that is easier to predict such as weight-based on height

Some common Supervised Machine Learning Algorithms are K Nearest Neighbours, Logistics Regression, Neural Networks, Decision Trees, etc

2. Unsupervised learning

Unsupervised learning means no supervision will be provided to the machine which allows the machine to work on the machine work on data that is unsupervised or unlabeled. Therefore it is up to the machine to find a solution on its own and give the response under unsupervised learning

Unsupervised machine learning can further be categorized into 2 groups

  1. Clustering
  2. Association

Clustering – Type of unsupervised learning where machine categorizes in groups based on the behavior of the object which means similar objects in one cluster whereas different object in different clusters

Association – Type of unsupervised learning is a rule-based machine learning which helps in discovering relations of variables in large data sets or  discovers the possibility that item or the object will occur again in the collection or not 

Some common Unsupervised Machine Learning Techniques and Algorithms are Dimensionally Reduction, K Nearest Neighbors, Distribution Models, mixture models, etc.

Difference between Supervised Learning and Unsupervised Learning

SUPERVISED MACHINE LEARNING To train model supervision is required under supervised learningUNSUPERVISED MACHINE LEARNING No supervision is required to train a model under unsupervised learning  
In supervised learning, we can predict the output when we have inputIn unsupervised learning, we only have input data and output data cannot be predicted
Results produced by Supervised learning have higher accuracyUnsupervised learning produces results that have comparatively lesser accuracy
Supervised learning allows you to be very specificUnsupervised learning does not allow you to be specific  

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Also, Read- Artificial Intelligence

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