Machine Learning

Pronunciation: /məˈʃin ˈlɜrnɪŋ/

Definitions of machine learning

noun a process in which a computer program learns from data to improve its performance on a specific task

Example Sentences

A1 Machine learning is a type of technology.

A2 Many companies use machine learning to improve their products.

B1 Understanding the basics of machine learning is essential for data scientists.

B2 Machine learning algorithms can analyze large amounts of data to make predictions.

C1 Researchers are constantly developing new machine learning techniques.

C2 Machine learning models can be trained to recognize patterns in complex data sets.

Examples of machine learning in a Sentence

formal Machine learning is a branch of artificial intelligence that allows computers to learn from data.

informal I'm learning about machine learning in my computer science class.

slang I heard that machine learning is like teaching computers to think for themselves.

figurative In the world of technology, machine learning is like giving computers the ability to learn and adapt on their own.

Grammatical Forms of machine learning

• plural: machine learnings
• comparative: more machine learning
• superlative: most machine learning
• present tense: machine learns
• future tense: will machine learn
• perfect tense: has machine learned
• continuous tense: is machine learning
• singular: machine learning
• positive degree: machine learning
• infinitive: to machine learn
• gerund: machine learning
• participle: machine learned

Origin and Evolution of machine learning

First Known Use: 1959 year

Language of Origin: English

Story behind the word: The term 'machine learning' originated from the field of artificial intelligence, specifically from the concept of creating algorithms that allow computers to learn from and make predictions or decisions based on data.

Evolution of the word: Initially used in the context of computer science and artificial intelligence, the term 'machine learning' has evolved to become a widely recognized field in which algorithms and models are used to analyze and interpret data to make predictions and decisions without being explicitly programmed.