What is Machine Learning (ML)?

Define

Machine learning is the systematic research of statistical models and algorithms that machines use in order to accomplish a particular task efficiently without using specific guidance. In machine learning, the systems depend on the patterns and interference to perform tasks.

The machine learning is an AI (artificial intelligence) system prepared for the technological development of human intelligence. Machine learning allows computers to manipulate different situations via analysis, observation, experience, and self-training.

The machine learning is AI (Artificial Intelligence) technologies subset.

Machine learning employs

  1. Syntactic model identification
  2. Natural language processing
  3. Search engines
  4. Machine vision
  5. Computer understanding and knowledge

Machine Learning Working

To make predictions and decisions without programming machine to perform a particular task, the machine learning contains the algorithm. The algorithm is able to learn by using the sample data also called training data like email filtering.

The aim of machine learning is to make a prediction using computer systems.

Machine Learning Algorithm Types

  1. Supervised learning:
    The supervised learning algorithm builds on the mathematical model of data set containing both input and output sample data. Through this data set, the supervised learning algorithm learns to predict the output associated with the input. The supervised learning includes classification and regression.
  2. Unsupervised learning
    The unsupervised learning algorithm builds on the mathematical model of data set containing only input sample data and structures it. This type of algorithm learns from the unstructured sample data set.
  3. Reinforcement learning
    Reinforcement learning is a field of machine learning involved with how software agents give an indication to make move in situations like to maximize some idea of cumulative benefits. Reinforcement type algorithms employ in autonomous vehicles and in learning to play a game facing a human as an opponent.
  4. Feature learning
    Feature learning algorithms also termed as representation learning algorithms. The algorithm attempts to conserve the data in their input and transform the same to makes it useful. This method allows the rebuilding of the inputs appearing from the unnamed data-generating administration. This replaces manual engineering with a machine, which is able to learn and use them to perform a particular task.
  5. Sparse dictionary learning
    Sparse dictionary learning is a technique where a training sample is represented as a continuous sequence of basic functions and is considered a rare model.

Term shares a similar method but different from machine learning

• Data mining
• Knowledge Discovery in Databases (KDD)

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