An Introductory Guide to Machine Learning
It can seem from the mainstream media that every company is working on a machine learning project that promises to increase their revenue, eliminate competition, and provide revolutionary business insights.
With big promises like that, you might wonder what machine learning is and how it can help your business.
This guide will help to answer some of your questions.
1. The Definition of MLThe definition of machine learning is not set in stone. There are many different variations out there, and their nuances depend on whom you ask and what field they work in.
1.1 Machine learning vs. AI vs. deep learning
Traditional media often portraits deep learning as a synonym for AI. That is incorrect.
Machine learning is sometimes identified as one of the main techniques to achieve true AI, but it is just a subfield of AI, not its counterpart.
Deep learning is further a subfield of machine learning, which makes both areas subfields of AI.
What is missing from this venn diagram is computer science, which would encapsulate all three fields.
Data science is another term that you might have heard in relations to machine learning.
Data science is a bit more challenging to place, as it is an umbrella term that includes machine learning, as well as other disciplines of computer science that have little to do with AI.
1.2 Machine learning in one elegant sentence
When searching for a complete definition of machine learning, one has to turn to the leading authorities in the field.
In their course on AI, University of Helsinki defines machine learning as “systems that improve their performance in a given task with more and more experience or data”.
Andrew Ng, a Stanford University professor and a Coursera co-founder, has another definition. In his widely popular online course on machine learning, Andrew defines machine learning as “the science of getting computers to act without being explicitly programmed”.
That is, as you will learning in the guide, not entirely accurate, as a big part of the machine learning process requires much effort from data scientists.
Another prominent figure in the machine learning field is Tom Mitchell.
His definition of machine learning came out already in 1997. He explained machine learning with a problem, which read:
“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E”.
What can you take from these definitions? First of all, machine learning revolves around computers progressively getting better at problem-solving.