What is full form of ML

Full Form of ML in AI

The Full Form of ML in AI is Machine Learning. It is nothing but a branch or an application of Artificial Intelligence (AI). It is an essential part of the Computer that helps in various aspects. In Machine Learning human trains the machine to learn from its past data and use it for taking further actions. Machine Learning is the branch of computer science that gives a computer the potential to learn without being definite programmed. Machine Learning comes under Artificial Intelligence while Deep Learning is the part of machine learning. The Neural Network is a sub-field of Deep Learning.

A Brief History of Machine Learning (ML)

A Computer Scientist whose name was Arthur Samuel coined the name Machine Learning. He was working with IBM. Firstly he developed a computer program for checking game playing. By playing games the computer learned more from the experiences and use these experiences to make further predictions. Samuel coined the term 'Machine Learning in 1952. So Machine Learning is not a new concept it comes into existence in the 1940s and developing day today.

Classification of Machine Learning (ML)

The primary types of Machine learning are as follows:-

  • Supervised Machine Learning
  • Semi-Supervised Machine Learning
  • Unsupervised Machine Learning
  • Reinforcement Learning

Supervised Machine Learning - It help organizations solve a lot of problems which is based on real-life situations. Some methods of Neural Network is also used in Supervised Learning.

Semi-Supervised Machine Learning - It works as a medium between Supervised and Unsupervised Machine learning.

Unsupervised Machine Learning - It can identify distinctions and similarities in any information and due to this, it provides an ideal solution for pattern and image identification, strategies in cross-selling, and analyzing the exploratory data.

Reinforcement Machine Learning - It is the model of Behavioral Machine Learning which is just similar to Supervised Machine Learning but its algorithm are not trained for using sample data.

Advantages of Machine Learning (ML)

  • It has automation capability.
  • Machine Learning is continuously Improving.
  • It can handle a large variety of data very easily.
  • Recognition of patterns and trends.

Some Challenges or Disadvantages of Machine Learning (ML)

Undoubtedly the technology of machine learning made the life of a human being very easy and comfortable but it creates problems in various ways. Some of the problems are- 

  • Technical singularity
  • It made a massive impact on jobs because it decline the demand for human resources.
  • The privacy of an individual is also concerned as the challenge of Machine learning.
  • There is a chance of massive error because Machine learning is autonomous.

Nowadays, Machine Learning (ML) becomes an essential part of multiple technologies. Therefore its future is bright and the primary motive of Machine Learning is to train the computer automatically without the intervention of Human beings.