Applications of Machine Learning

1 min read

Machine learning, a prominent subset of artificial intelligence (AI) that gives technical systems the capability to learn and improve from experience without ‘conventional’ programming. Machine learning enables machines to learn and perform tasks without using explicit instructions. With machine learning (ML), computer programs can access business data and use it to ‘learn’ a wide range of useful things.

Machine learning has several applications, such as email filtering and computer vision, and is used to develop algorithms to effectively perform complex tasks without the need for human intervention. Machine learning systems are capable of making accurate decisions at high speeds. With ML, users can analyze large and complex data sets with ease.

The key features of machine learning algorithms include association rule learning, Bayesian networks, clustering, decision tree learning, genetic algorithms, learning classifier systems, and support vector machines. These solutions can perform a variety of tasks and functions based on data. ML algorithms may be capable of supervised learning as well as unsupervised learning.

Prominent machine learning solutions include Microsoft Knowledge Exploration Service, IBM Watson Machine Learning, scikit-learn, GoLearn, machine-learning in Python, Figure Eight (CrowdFlower), Microsoft Recommendations API, Amazon Personalize, Qubole, and Google Cloud TPU.

Applications of machine learning include:

  • Email spam and malware filtering
  • Online customer support
  • Photo-tagging applications
  • Video surveillance
  • Predictions while commuting
  • Virtual personal assistants
  • Product recommendations
  • Web search engine result refining
  • Social media services
  • Online fraud detection

Occasionally, machine learning programs may fail to deliver useful results. This may be because of the lack of suitable data, access issues, bias in data, privacy concerns, badly chosen tasks and algorithms, incorrect tools and people, lack of resources, and evaluation problems.

Regardless of its potential shortcomings, machine learning is increasingly being leveraged in order to provide swift and accurate analysis of massive quantities of data, which helps companies to identify profitable opportunities and dangerous risks.

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