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.


Machine learning in cloud
binary numbers in CPU for machine learning
We’ve put together an introductory guide to machine learning that briefly covers all significant areas of machine learning as a field. We hope that you can pick the topics that apply to your area of work or interest, and do further research with more confidence. This article will teach you a basic definition of machine learning, how a machine learning process actually works, and different types of problems this field can solve. After that, we will look at a few common algorithms before jumping into discussing challenges and limitation of machine learning. Happy learning! ?

1. The Definition of ML

The 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.
Before looking at the definitions, let’s place machine learning in the realm of other practices it’s closely related to: computer science, artificial intelligence, deep learning, and data science.  

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.

Artificial intelligence, machine learning and deep learning relation representation
Machine learning elements

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.