Machine Learning | An Introduction

Introduction

Machine Learning is undeniably one of the most influential and powerful technologies in today’s world. More importantly, we are far from seeing its full potential. There’s no doubt, it will continue to be making headlines for the foreseeable future. This article is designed as an introduction to the Machine Learning concepts, covering all the fundamental ideas without being too high level.

Machine learning is a tool for turning information into knowledge. In the past 50 years, there has been an explosion of data. This mass of data is useless unless we analyse it and find the patterns hidden within. Machine learning techniques are used to automatically find the valuable underlying patterns within complex data that we would otherwise struggle to discover. The hidden patterns and knowledge about a problem can be used to predict future events and perform all kinds of complex decision making.

We are drowning in information and starving for knowledge — John Naisbitt

Most of us are unaware that we already interact with Machine Learning every single day. Every time we Google something, listen to a song or even take a photo, Machine Learning is becoming part of the engine behind it, constantly learning and improving from every interaction. It’s also behind world-changing advances like detecting cancer, creating new drugs and self-driving cars.

The reason that Machine Learning is so exciting, is because it is a step away from all our previous rule-based systems of:

if(x = y): do z        

Traditionally, software engineering combined human created?rules?with?data?to?create answers to a problem. Instead, machine learning uses?data?and?answers?to?discover the rules behind a problem.

To learn the rules governing a phenomenon, machines have to go through a?learning process,?trying different rules and learning from how well they perform. Hence, why it’s known as Machine Learning.

There are multiple forms of Machine Learning; supervised, unsupervised , semi-supervised and reinforcement learning. Each form of Machine Learning has differing approaches, but they all follow the same underlying process and theory. This explanation covers the general Machine Leaning concept and then focusses in on each approach.

Process

Data Collection: Collect the data that the algorithm will learn from

Data Preparation: Format and engineer the data into the optimal format, extracting important features and performing dimensionality reduction.

Training: Also known as the fitting stage, this is where the Machine Learning algorithm actually learns by showing it the data that has been collected and prepared.

Evaluation: Test the model to see how it performs.

Tuning: Fine tune the model to maximize it's performance.