Machine Learning – In a nutshell

Machine Learning Defined
Machine learning simply is the science of getting machines to learn and act in a similar way to us humans. Then at the same time autonomously learning from some real-world examples and sets of data provided.

Examples include the self-driving cars and Amazon’s list of recommendations. Both use machine learning to make repeatable decisions, perform specific tasks and independently adapt with little to no human interaction.


Machine learning is not a new technology. The algorithms that drive today’s pattern recognition and machine learning applications have been around for many years. However, it is only now that machine learning models are starting to interact with more complex data sets and learn from previous computations and predictions to produce reliable decisions and results. Build the right model and you have a better chance of avoiding unknown risks and identifying profitable opportunities across your business.

machine learning
machine learning

Types of Machine Learning
Machine learning uses a broad range of machine learning tools, techniques and ideas. Here are some of the most common types of machine learning techniques and algorithms along with a brief summary of how each can be used to solve problems.

Supervised Learning
Some of the most simplistic tasks fall under supervised learning. For example, a handwriting recognition algorithm would typically be classified as a supervised learning task. However, these tasks can only be carried out if the computer is given correct input-output pairs.

Unsupervised Learning
Where supervised machine learning algorithms look for patterns from a data set of correct answers, unsupervised learning tasks find patterns that are often impossible for a human to identify. For example, a marketing algorithm might use unsupervised learning to identify segments of prospects with similar buying habits.

Reinforcement Learning
Instead of providing the computer with correct input-output pairs, reinforcement learning provides the machine with a method to measure its performance with positive reinforcement. Similar to how humans and animals learn tasks, the machine tries a number of ways to solve a problem and is rewarded with a signal if it is successful. This behaviour is then learned and repeated the next time the same problem is presented.

Machine Learning Applications
There are many machine learning tools and applications currently in use across every industry. Some of the most common include:
Data Security
Malware is a problem that isn’t going to go away anytime soon. The bad news is that thousands of new malware variants are detected every day. The good news is that new malware almost always has the same code as previous versions. This means that machine learning can be used to look for patterns and report anomalies.

Financial Trading
Patterns and predictions are what help keep the stock market alive and stockbrokers rich. Machine learning algorithms are in use by some of the world’s most prestigious trading companies to predict and execute transactions at high volume and high speed.

Marketing Personalisation
When you understand your customers, you can serve them better. When you serve them better, you sell more. Marketing personalisation uses machine learning algorithms to create a truly personalised customer experience that is matched to their previous behaviour, likes and dislikes, and location-based data, such as where they prefer to shop.

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