Machine learning

Solving the traditionally hard, unscalable problems like predicting maintenance and extracting data from handwritten documents.
Servers in a server room.

We understand the potential that machine learning has to revolutionise businesses of all sizes and industries.

That’s why we’re dedicated to providing our clients with high-quality machine learning services that can help them unlock new growth opportunities and drive success.

What is machine learning, in simple terms?

Machine learning is a type of artificial intelligence that allows computers to learn and make decisions without being explicitly programmed. It can help businesses unlock the power of their data to make better decisions and improve operations.

It helps solve traditionally difficult problems, where an exact solution definition isn’t available. Instead, machine learning algorithms can be fed bulk amounts of data and build their own solution to the problem at hand.

There’s a broad range of applications when it comes to machine learning, including:

  • Predicting customer behaviour: Machine learning can help businesses predict how customers will behave, allowing them to tailor their marketing efforts and improve the customer experience.
  • Detecting fraud: Machine learning can help businesses detect fraudulent activity, improving security and reducing the risk of loss.
  • Improving operations: Machine learning can help businesses optimise their operations by identifying patterns and trends in their data and making recommendations for improvement.

What is needed for machine learning?

To be practical, machine learning typically requires:

  • Large amounts of data: Machine learning algorithms need a large amount of data to learn from, so the more data a business has, the more accurate the predictions and decisions made by the algorithms will be.
  • A clear goal: Machine learning algorithms need to be trained to solve a specific problem or achieve a specific goal, so it’s important to have a clear understanding of what you want the algorithm to do.
  • A machine learning algorithm: There are many different types of machine learning algorithms, and the best one for a given problem will depend on the specific circumstances.

Practical examples.

Some practical examples of machine learning include:

Fraud detection: Machine learning algorithms can be trained to identify patterns and anomalies in transaction data that may indicate fraudulent activity. This can help financial institutions reduce losses from fraud and improve the customer experience by catching fraudulent transactions more quickly.

Customer service chatbots: Machine learning can be used to build chatbots that can handle customer inquiries and provide personalised responses in real time. These chatbots can help reduce the workload on human customer service agents and improve the overall customer experience.

Predictive maintenance: Machine learning algorithms can be used to analyse data from sensors on industrial equipment to predict when maintenance will be needed. This can help companies reduce downtime and improve the efficiency of their operations.

Medical diagnosis: Machine learning algorithms can be trained on large datasets of medical images and patient records to assist in the diagnosis of diseases such as cancer or diabetes. This can help doctors make more accurate and timely diagnoses, ultimately improving patient outcomes.

Music and movie recommendations: Many music and video streaming services use machine learning algorithms to recommend content to users based on their past listening or viewing habits. This can help users discover new content and improve their overall experience with the service.

Less common, but novel examples.

With the power of machine learning, many more “traditionally hard” problems for computers to solve become solvable, including:

  • Document OCR: Optical character recognition (OCR) is a type of machine learning that allows computers to recognise and extract text from images and documents. It can be used to digitise paper documents, making them easier to store, search, and share.
  • Voice recognition: Machine learning can be used to develop voice recognition systems that can understand and respond to human speech. These systems can be used in a variety of applications, including virtual assistants, language translation, and voice-controlled devices.
  • Image recognition: Machine learning can be used to develop image recognition systems that can classify and identify objects in images. These systems can be used in a variety of applications, including security, surveillance, and marketing.
  • Predictive maintenance: Machine learning can be used to develop predictive maintenance systems that can predict when equipment is likely to fail and schedule maintenance before it does. This can help businesses reduce downtime and improve efficiency.

So why not solve the hard problems?

We’ll help you use the latest breakthroughs in machine learning to tackle your organisation’s challenges to more efficiently and more effectively

Wouldn't you rather know? automate? grow? innovate? analyze? personalise? excel? accelerate? maximise?

Let’s turn the hidden potential in your data into a catalyst for your organisation’s transformation.