Is there a clear definition of what is machine learning?

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For many, machine learning is mostly techniques that in one way or another, perform supervised clustering using cross-validation, training sets and predictive modeling. Techniques can be statistics, SVM, neural networks, AI, pattern recognition, association rules, etc. Output can be a keyword taxonomy, stock trading system, transaction scores, automated medical diagnosis etc.

Paraphrasing a simple definition from the Stanford ML course ... as taught by Andrew Ng ... 'Machine learning is the science of getting computers to generate results they have not been explicitly programmed to generate.'


Machine Learning is a process where in you form algorithms and train your machine to generate answers for you. (Learning and writing an algorithm)

Dr. Vincent correctly pointed out that combination bundle also called machine learning.

Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed"

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Machine learning is a subfield of artificial intelligence. I think you should follow this for more details https://mytutorsource.hk/blog/educational-trends-in-2021/. It is defined as a computer's ability to learn and conclude things, without being directly programmed. Machine learning uses databases, patterns, programmed algorithms and statistical techniques that receive and examine input data to speculate results. As new data is entered, the machine learning system also develops and improves its performance over time. Machine learning is used in various industries and fields, such as medical diagnosis, forecast, image recognition, transcription, learning association, image processing, etc.

There are four kinds of machine learning algorithms.

  1. Supervised learning in which machine is taught by examples, data is fed with labels and operator helps in correcting answers.
  2. Semi-supervised learning is where data is available with labels but a human operator is also needed to correct some places.
  3. Unsupervised learning is when a computer program works with unlabelled data.
  4. Reinforcements learning, It allows machines and software programs to automatically decide the ideal conclusion within a particular context

With the machine learning approach, instead of writing a program by hand for each specific task, for a particular task we collect a lot of examples that specify the correct output for giving an input. The machine learning algorithm then takes these examples and produces a program that does the job. The program produced by the learning algorithm may look very different from a typical handwritten program. More here.


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