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For the longest time, Machine Learning (ML) was a concept that sounded very futuristic and beyond the scope of everyday use. Today, however, this innovative technology is used in a variety of different areas, some of which we all experience in our day-to-day lives without noticing. For businesses, it is a smart way to increase efficiency and enhance customer experience, while minimising costs. This article will explain the way ML technology works, as well as explore its most common applications and role in revolutionising well-known industries.
Types of Machine Learning
Machine Learning is actually a branch of the wider Artificial Intelligence (AI) technology, known as a method of giving computers the ability to learn. There are also four main types of ML: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Below, we explain each of those.
Supervised learning
Supervised learning is training a model using labelled data, where the desired output is already known. This means that the model is given correct and incorrect answers, so it can learn to identify them in a large pool of data. The two types of tasks that supervised learning can be used for is classification and regression. The former is about categorising, while the latter predicts a continuous value. For instance, classification can find identify spam emails against normal emails, whereas regression can predict a property price based on its features.
Unsupervised learning
Like the name suggests, unsupervised learning is the opposite of supervised learning – a model is trained on unlabelled data and desired output is not known. This kind of method allows the model to find patterns in data on its own, which can be particularly useful when clustering big amounts of data into groups, based on their similar features. Using the same email example, this model would categorise emails by promotional, important, social, etc.
Semi-supervised learning
Semi-supervised learning is a hybrid between supervised and unsupervised learning. This method uses a small amount of labelled data and a large amount of unlabelled data to train a model. This approach is typically taken when the labelled data is expensive or difficult to obtain.
Reinforcement learning
Reinforcement learning involves teaching a model through the use of trial and error, in order for it to make a sequence of decisions which will lead to an optimal outcome. The model is given feedback in the form of rewards or penalties based on its actions, training it which decisions were desired and which weren’t. This method is most commonly used in such industries as gaming and robotics, where autonomous decision-making is needed.
Applications of Machine Learning
Machine learning has numerous useful applications across various sectors – more than some even realise. Here are some examples:
Image and speech recognition
One of the most well-known applications is image and speech recognition. This means that ML is trained to recognise objects or faces with high accuracy. This technology is very popular in modern smartphones, as a way to unlock them. But, it is increasingly used for self-driving cars or security systems, too.
Natural language processing
Natural language processing is another area where machine learning has excelled. NLP is essentially teaching a computer to understand and interpret human language, after which it is capable to generate human-like text. Open AI’s ChatGPT model is a primary example of this technology, but it already existed years ago in customer service chatbots or voice assistants.
Prediction and recommendation
Predictive analytics is another popular application of machine learning. It can analyse data in order to create highly reliable predictions or suggest personalised recommendations. For instance, companies like Netflix or YouTube use this technology to analyse the viewing history of their users in order to recommend videos or TV shows which they may enjoy watching next.
Fraud detection
ML has also played a crucial role in helping companies or governments detect fraudulent activities before it’s too late. Its ability to analyse huge amounts of data and identify any anomalies can save hundreds of hours of manual labour. This is particularly popular in financial services, used for credit card or insurance fraud detection.
Diagnosis and medical uses
In healthcare, Machine Learning can be an important tool for illness diagnosis or treatment research. It can quickly and reliably identify patterns in patients’ health history, recognising hard-to-spot illnesses or making predictions about their health. This tech has a huge potential to revolutionise such areas as cancer diagnosis and personalised treatment.
Machine Learning development
So, how can a business get started in making use of this powerful tool? The answer is specialised ML development services. Here are some examples of such service providers:
- DigitalSense.ai – Machine Learning Digitalsense offers a variety of ML consulting and custom development services. They hold expertise in all the types and applications we have discussed, including NLP, predictive analytics, computer visions, and other.
- Qubika – Provides end-to-end AI and ML deployment services, helping your business take full potential of this technology.
- Dynamind Labs – Focuses on state-of-the-art AI solutions, adapting smart ML models for use in various different industries, based on their unique requirements.
Conclusion
The applications of machine learning are vast and varied, from entertainment to life-saving medical solutions. As this technology continues to evolve, more businesses will be undoubtedly adopting these solutions to their own needs.
