Machine learning is a programming offshoot from artificial intelligence and is founded on the idea that it is possible for computer systems to not only learn data but then use it to identify patterns and decisions in the future.
The crucial aspect of machine learning, and what makes it so clever, is that the idea behind this is that it can find out this behaviour and notice patterns with very little human intervention. In fact, in some cases, a human is not even needed to help with any part of the process.
The development of machine learning
It is worth remembering that machine learning algorithms have been around for a number of years, but what they can now achieve has changed over time, as they have become far more advanced. For example, the ability for machine learning models to be able to apply maths calculations to big data automatically is a new development.
What makes machine learning important?
Machine learning solutions can help organisations be far more efficient and with higher levels of accuracy. Through machine learning, it is possible to create models that can analyse colossal amounts of complex data and produce results quickly.
For companies, machine learning processes are win-win for these very reasons, and that is why models have been used across a number of sectors.
Which sectors are using machine learning?
Predominantly those that are working closely with significant amounts of data on a continual basis, and are looking to get a better insight into this data in order to beat the competition or to work in a more efficient manner.
The industries using machine learning models the most include:
- Financial services: banks and other finance providers are primarily using machine learning to tackle fraud, seek out investment opportunities and provide customer support
- Healthcare: through sensors and other types of devices, data can be quickly analysed to calculate a patient’s health in real-time.
- Oil and gas: in this industry, processes can be used in order to identify new energy sources underground, making machine learning models extremely valuable
- Transport: machine learning is being used in the transport industry to identify potential problems that could arise, as well as to help increase overall profitability.
- Government: machine learning models have been used to mine data for insights to help cut down on costs as well as to reduce identity theft
Types of machine learning methods
The most popular machine learning methods to have been developed so far are supervised and unsupervised learning.
With supervised learning algorithms, this type of algorithm is trained through labelled examples. The algorithm learns through a series of inputs, and compares its output with correct outputs in order to determine errors.
Over the course of time, the model modifies itself based on the information it receives.
It is typically used to predict future events based on previous data, such as to help detect instances of possible fraud in the finance sector.
Unsupervised learning algorithms mean that the computer system is not given a labelled example, so with the data it is given, it then explores it and then figures out what has been shown to it on its own.
Most commonly, unsupervised learning is used when it comes to analysing transactional data. For example, it is popular for this type of data to be used to identify groups of similar customers to then best target them in marketing campaigns.
What machine learning algorithms do I use?
What is best for you and your company will be largely dependent on a number of factors such as :
- How much data you are dealing with
- Quality of data
- The urgency of the task itself
- What you intend to do with the data
It is only once you are able to provide answers to these questions that you can best determine the right machine learning algorithm to pick.