In a nutshell, machine learning detects patterns in data and make predictions from patterns.
Anywhere with a volumn of data, if one wants to find a formula to predict from it, machine learning may come to use.
Examples from the right picture about usages of machine learning, from data to predictions.
Let’s get this clear from confusion. Any differences between artificial intelligence, machine learning, and deep learning? The right picture from NVIDIA helps explain:
Artificial intelligence is an academic discipline founded in the early 1950s.
Machine learning is the study of algorithms that learn by experience. It’s been gaining momentum since the 1980s and is a subfield of AI.
Deep learning is a newer subfield of machine learning using neural networks. It’s been very successful in certain areas (image, video, text, and audio processing).
Simple answer: to the naive audience, either one for causal conversation.
Safe answer: Machine learning, as most specific than AI.
“If it’s written in Python, it’s machine learning; if it’s written in PowerPoint, it’s AI.”
A machine learning algorithm is also just software. So then what’s the big difference?
With “normal” software, we tell the computer what to do.
With machine learning, we tell the computer how to figure out the answer itself, using the data we feed it.
Let ‘s consider the machine learning algorithms that learns as a ‘student bot’,
There is another bot termed teacher, it has the information/data to teach/train and test the student bot.
Everytime a student after training class fails the test and there a ‘Repair bot’ comes to re-wire the brain of failing student, and put them to test again and again, before helpless student finally gets dispelled from class.
Imagine participating student bots are thousands or millions (each born with a distinct algorithm) in number, eventually there emerges a convincing student top the tests for use.