Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) – these are the three trending buzzwords that have created a great hype over the Internet and other media platforms for some time now. Irrespective of whether people hold a sound knowledge of the data science or not, everyone is actively making their own statements explaining the differences between these technologies, which thereby creating a mysterious situation for the newbies and laymen to understand the true differences between them. To make the things easy, this article will initially explain “what AI, ML, and DL are?”, and later discusses the key differences between them.
What is Artificial Intelligence (AI)?
The definition of AI as per Wikipedia is – “the intelligence demonstrated by the
machines, rather than humans or other animals”.
In simple words, Artificial Intelligence (AI) can be referred as the ‘skill for a machine to exhibit its intelligent behavior’.
What is Machine Learning (ML)?
Machine Learning as found in the Wikipedia is “the sub-field of computer science that gives computers the skill to learn without being explicitly programmed”.
Machine learning in simple words can be stated as ‘the ability of a machine to learn and achieve intelligence’.
What is Deep Learning (DL)?
According to Wikipedia, “Deep Learning is a subset of Machine Learning,
which examines the computer algorithms that learn and improve on their own”.
Deep learning is one of the best machine learning techniques that resembles how the human brain works (neural networks).
Read More: https://kovidacademy.com/blog/artificial-intelligence-machine-learning-d...
Kovid Academy plans to be the leading provider of training for technology domains with potential for significant market disruption, including Predictive Analytics, Machine Learning, IoT (Internet of Things) Technologies and SMAC (Social, Mobility, Analytics, Cloud). Our customized approach will serve the diverse needs of organizations and individuals that seek to discover actionable insights by leveraging diverse data sources.