A brief review of Machine Learning

Table of Contents

Machine learning is an application of Artificial Intelligence to learn and improve from experience without being explicitly programmed. This article discusses Machine Learning (ML) with applications, history, categories, and market value.

What is Machine Learning?

Maching learning (ML) is a method of data analysis that focuses on the analytical model building to access data and use it for self-learning. Machine learning is one of the most famous subfields of artificial intelligence. We are using machine learning widely for many purposes in our daily life, for example, Traffic alerts using Google Maps, Chatbot (Online Customer Support), Google Translation, Speech and Image Recognition, Prediction, Extraction of information, and so on. 

“Much of what we do with machine learning happens beneath the surface. Machine learning drives our algorithms for demand forecasting, product search ranking, product and deals recommendations, merchandising placements, fraud detection, translations, and much more. Though less visible, much of the impact of machine learning will be of this type — quietly but meaningfully improving core operations.” — Jeff Bezos

The term machine learning was coined by Arthur Samuel in 1959 [1] and also used the synonym self-teaching computers. Nils John Nilsson’s book “Learning Machines: Foundations of Trainable Pattern-classifying Systems” in 1965 [2] deals mostly with machine learning for pattern classification.

“Once computers can effectively reprogram themselves, and successively improve themselves, leading to a so-called “technological singularity” or “intelligence explosion,” the risks of machines outwitting humans in battles for resources and self-preservation cannot simply be dismissed.” — Gary Marcus, Founder of Geometrical Intelligence

What are the Catigories of Machine Learning?

Machine Learning can be divided into three basic categories:

  • Supervised Learning allows learning from labeled training data to predict outcomes.
  • Unsupervised Learning allows analyzing unlabeled datasets.
  • Reinforcement learning is based on rewarding desired behaviors or punishing undesired ones.

Why are Machine Learning​ Important?

According to Fortune business insights, the global machine learning market value was USD 21.17 billion in 2021. It is expected to reach USD 209.91 billion by 2029, registering a compound annual growth rate of 38.8% from 2022 to 2029 [3].

“We are entering a new world. The technologies of machine learning, speech recognition, and natural language understanding are reaching a nexus of capability. The end result is that we’ll soon have artificially intelligent assistants to help us in every aspect of our lives.” — Amy Stapleton

How are growing global Machine Learning markets?

According to Fortune business insights, the global machine learning market value was USD 21.17 billion in 2021. It is expected to reach USD 209.91 billion by 2029, registering a compound annual growth rate of 38.8% from 2022 to 2029 [3].

“We are entering a new world. The technologies of machine learning, speech recognition, and natural language understanding are reaching a nexus of capability. The end result is that we’ll soon have artificially intelligent assistants to help us in every aspect of our lives.” — Amy Stapleton

EDITORIAL TEAM
EDITORIAL TEAM
TechGolly editorial team led by Al Mahmud Al Mamun. He worked as an Editor-in-Chief at a world-leading professional research Magazine. Rasel Hossain and Enamul Kabir are supporting as Managing Editor. Our team is intercorporate with technologists, researchers, and technology writers. We have substantial knowledge and background in Information Technology (IT), Artificial Intelligence (AI), and Embedded Technology.

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