The purpose of machine learning is to discover patterns in your data and then make predictions based on often complex patterns to answer business questions, detect and analyse trends and help solve problems.
Machine learning in business and other fields is effectively a method of data analysis that works by automating the process of building data models.
Data Scientist, Objective IT
Our data science team use specialised machine learning techniques to predict the answers to our clients’ problems. Using a combination of state-of-the-art extreme-gradient-boosting machine and generalized linear modelling learning algorithms, our clients receive accurate predictions to their business questions.
Machine Intelligence and
Machine learning in business and other areas such as healthcare and governmental departments is not simply another term for AI (Artificial Intelligence). While AI is the umbrella term given for machines emulating human abilities, machine learning is a specific branch of AI where machines are trained to learn how to process and make use of data; another description often used is ‘machine intelligence’.
The objective of machine learning in business is not only for effective data collection, but to make use of the ever increasing amounts being gathered by manipulating and analysing it without heavy human input.
Machine intelligence enables complex and larger data to be processed and analysed along with the desired results being achieved such as determining customer trends, detecting fraud, spotting buying trends and other primary objectives.
Machine learning in business therefore offers an important commercial benefit in being able to make the best use of your data.
Indeed, a key objective of machine learning is to enable you to keep up with those competitors already making best use of their data to maximise business opportunities.
Most commercial and non-commercial organisations benefit from machine learning, so it’s highly likely that some form of machine intelligence can be put to use in your business.
How Machine Learning Is Used
The objective of machine learning varies depending on what field it’s deployed in. Some examples include:
Financial services – data can be analysed and machine intelligence can help spot investment trends so helping investors plan their trading and for institutions to prevent fraud.
Governmental – machine intelligence can help identify ways for cost savings to be made so improving efficiency and maximising budgets.
Health – data can be analysed to identify trends and improve diagnosis; the increase in wearable tech and sensors produce considerable data about patients that machine intelligence can make use of.
Retail – the objective of machine learning is usually to help retailers understand their customers better and personalise their interactions; websites recommending purchases based on the customer’s buying history or web pages visited is a classic case of machine learning in action.
Fraud – the increased use of systems and activities such as online shopping and financial transactions increases fraudulent behaviour, so another objective of machine learning in business is to help organisations combat losses through fraud.
How We Help Achieve Your
Objective of Machine Learning
We start by consulting with you to understand what your primary objective of machine learning is – or group of objectives.
We can start by building small training sets of data that can then be applied to all of your data automatically.
For example, by understanding the characteristics and behaviour of your best and worst clients, we can use these as a training set that can be applied against larger data sets. The result will be to sort prospective clients into those with a high probability of being a ‘best’ profitable client, and those who may need further nurturing through relationship building.
Machine learning in business can also be used to:
- Compete Intelligently
- Enhance Customer Service
- Improve Lead Nurturing
- Manage your Sales Funnel
- Detect Fraudulent Activity
- Outsmart the opposition
- Predict Journey times
- Predict how long jobs may take
- Score your prospects and customers
- Understand the demographics and buying behaviour of your market
You can find out more about the concept of machine learning in business by taking a look at our blog: “How to get your data to talk to you” or read on below to learn more about the technical side.
More About Machine Learning
As mentioned earlier, machine learning is a subset of AI that provides computers with the ability to learn without being specifically programmed. A key requirement of machine learning in business is in finding patterns in large volumes of data and using those patterns to perform predictive analysis; a key offering in this area is Microsoft’s Azure Machine Learning.
Machine learning models fall into two broad categories:
In supervised learning, the model is “trained” with a large volume of data, and algorithms are then used to predict an outcome from future inputs. Most supervised learning models use:
- Regression algorithms to compute an outcome from a continuous set of possible outcomes such as your score on a test or:
- Classification algorithms to compute the probability of an outcome from a finite set of possible outcomes; the objective of machine learning here might be to detect fraudulent credit card transactions or flag up spam e-mail.
In unsupervised learning, the computer isn’t trained initially in terms of being told the ‘right’ answer but is presented with a set of data and challenged to find relationships in it.
Talk to us today to see how machine learning can help in your business.
Main Objective of
The main objective of Artificial Intelligence (AI) is for machines to not only be developed enough to undertake tasks that humans perform, but to do so in a way that humans do involving ‘thought’ and action derived from intelligence.
So, it’s not just a repetitive task that can simply be programmed into a computer, it’s functions that require ‘thought’ and intelligence – a key example being the self-driving car that is expected to form a natural part of life before long. We’re nearly there already with certain models being able to ‘think’ – such as recognising road signs being approached and alerting the driver – or ensuring the vehicle stays in the correct lane on the road by realising when it’s drifting off course and correcting accordingly.
Another example is when IBM’s Deep Blue beat world champion chess player Gary Kasparov in 1997, and the same company’s Watson tech won the US quiz show Jeopardy! To do so it used natural language processing to analyse and process huge quantities of data to answer questions posed by a human in fractions of a second to beat two of the top players the show had ever had.
More recent and now ‘everyday’ examples of AI in action is the way routine life is made easier include Google search and the use of virtual personal assistants such as Alexa and Apple’s Siri and the use of image recognition software in some smartphones and at airports. So, a main objective of artificial intelligence is to make everyday life easier and more convenient: after all it’s far easier to simply look at your phone to unlock it through facial recognition than have to remember a passcode or take a glove off to use fingerprint recognition.
Machine learning is used to ‘teach’ the machine what to do, and then AI puts it into practice using a set of algorithms. The computer isn’t programmed as it would be to perform regular routine tasks so there’s no lines of code. The next main objective of Artificial Intelligence is to create machines with deeper human levels of intelligence – ‘deep learning’ – using a biologically inspired type of neural network in the machine. Data is processed through various neural layers allowing the machine to go ‘deep’ in its learning to literally deep think.