What is Machine Learning? Components and Applications

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What is Machine Learning? Components and Applications of Machine Learning

Definition of Machine Learning:

Learning is any process by which a system improves performance from experience.

A branch of artificial intelligence, concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data.

Definition by Tom Mitchell (1998): A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks T, as measured by P, improves with experience E.

Video Tutorial: What is Machine Learning? Components and Applications

Examples of Machine Learning:

i) Handwriting recognition learning problem

  • Task T: Recognising and classifying handwritten words within images
  • Performance P: Percent of words correctly classified
  • Training experience E: A dataset of handwritten words with given classifications

ii) A robot driving learning problem

  • Task T: Driving on highways using vision sensors
  • Performance measure P: Average distance traveled before an error
  • training experience: A sequence of images and steering commands recorded while observing a human driver

iii) A chess learning problem

  • Task T: Playing chess
  • Performance measure P: Percent of games won against opponents
  • Training experience E: Playing practice games against itself

Basic components of the Machine Learning process

The learning process, whether by a human or a machine, can be divided into four components, namely, data storage, abstraction, generalization, and evaluation. The figure illustrates the various components and the steps involved in the learning process.

Basic components of the Machine Learning process

1. Data storage

Facilities for storing and retrieving huge amounts of data are an important component of the learning process. Humans and computers alike utilize data storage as a foundation for advanced reasoning.

In a human being, the data is stored in the brain, and data is retrieved using electrochemical signals.

Computers use hard disk drives, flash memory, random access memory, and similar devices to store data and use cables and other technology to retrieve data.

2. Abstraction

The second component of the learning process is known as abstraction. Abstraction is the process of extracting knowledge about stored data. This involves creating general concepts about the data as a whole. The creation of knowledge involves the application of known models and the creation of new models. The process of fitting a model to a dataset is known as training. When the model has been trained, the data is transformed into an abstract form that summarizes the original information.

3. Generalization

The third component of the learning process is known as generalization. The term generalization describes the process of turning the knowledge about stored data into a form that can be utilized for future action. These actions are to be carried out on tasks that are similar, but not identical, to those that have been seen before. In generalization, the goal is to discover those properties of the data that will be most relevant to future tasks.

4. Evaluation

Evaluation is the last component of the learning process. It is the process of giving feedback to the user to measure the utility of the learned knowledge. This feedback is then utilized to effect improvements in the whole learning process.

Why Machine Learning is so Popular..?

We have seen Machine Learning as a buzzword for the past few years, the reason for this might be the high amount of data production by applications, the increase of computation power in the past few years, and the development of better algorithms.

You may already be using a device or application that utilizes it. For example, GMAIL, WhatsApp, E-Commerce Websites, Video Sharing Platforms, wearable fitness trackers like Fitbit, or intelligent home assistants like Google Home.

Applications of machine learning

The application of machine learning methods to large databases is called data mining. In data mining, a large volume of data is processed to construct a simple model with valuable use, for example, having high predictive accuracy.

The following is a list of some of the typical applications of machine learning.

  1. In the retail business, machine learning is used to study consumer behavior.
  2. In finance, banks analyze their past data to build models to use in credit applications, fraud detection, and the stock market.
  3. In manufacturing, learning models are used for optimization, control, and troubleshooting.
  4. In medicine, learning programs are used for medical diagnosis.
  5. In telecommunications, call patterns are analyzed for network optimization and maximizing the quality of service.
  6. In science, large amounts of data in physics, astronomy, and biology can only be analyzed fast enough by computers. The World Wide Web is huge; it is constantly growing and searching for relevant information cannot be done manually.
  7. In artificial intelligence, it is used to teach a system to learn and adapt to changes so that the system designer need not foresee and provide solutions for all possible situations.
  8. It is used to find solutions to many problems in vision, speech recognition, and robotics.
  9. Machine learning methods are applied in the design of computer-controlled vehicles to steer correctly when driving on a variety of roads.
  10. Machine learning methods have been used to develop programs for playing games such as chess, backgammon, and Go.


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