Machine Learning (ML) is an advanced technology that has had transformative effects on modern businesses in recent years. It offers smart tools and techniques for data analysis, covering a variety of different services. In fact, ML is used in numerous industries to enhance operations and improve decision-making. So, in this article, we will explore the different types of machine learning services as well as its practical applications across different sectors.
Types of machine learning
Machine learning can be categorised into four main types:
Supervised learning
Supervised learning means that algorithms are trained on labelled data, with a specific output in mind. This type of ML is usually used for tasks like spam detection in emails, as the model can learn to identify it based on previous examples of email spam.
Unsupervised learning
On the opposite, unsupervised learning is training the algorithms without labelled data responses. This essentially makes the model identify new or hidden patterns and structures within the data, that are not yet known. For instance, it could be used for customer segmentation in business, where they are grouped based on purchasing behaviour.
Semi-supervised learning
Combining the two different approaches, semi-supervised ML uses a mix of labelled and unlabelled data, with the former typically being the smaller proportion. This method can be used in situations where labelling data is useful, but costly and time-consuming, such as in medical industry.
Reinforcement learning
The last type of machine learning, reinforcement learning, trains algorithms to make decisions based on the desired outcomes. The algorithm will be rewarded for the wanted behaviours and penalised for others. This is typically most useful in robotics or game playing, where the modern learns to achieve specific goals through trial and error.
Common machine learning services
Using the preferred methods, businesses and organisations can acquire a wide range of different machine learning services. These can be broadly categorised into the following types:
Predictive analytics
Predictive analytics is a service very useful to businesses when it comes to strategy creation and decision-making. It uses historical data to predict potential future outcomes, such as to forecast sales, manage inventory, improve customer retention, and so on.
Natural Language Processing (NLP)
NLP enables machines to understand and interpret human language by processing our language. This data can then be used to perform data analysis, such as sentiment analysis, and gauge customer sentiment on different topics based on social media posts, forums, reviews, chatbots, etc.
Computer vision
Computer vision allows ML to interpret information and make decisions based on visual data. This service is often used in facial recognition systems or quality control processes in manufacturing businesses.
Classification
Classification is a type of algorithm created to look through a large amount of data and sort it into predefined categories. For instance, this can be essential for tasks like email filtering, where emails can be automatically put into the categories of important, spam, promotional, and so on.
Clustering
Similarly to classification, clustering algorithms can analyse large quantities of information and group similar data points together. This model is more commonly used in market research, for example, to identify certain consumer groups based on their purchasing habits and create marketing strategies accordingly.
Applications of machine learning
Many of the above ML services can be used in multiple scenarios, as well as work together simultaneously. While this technology is gaining more recognition now, a lot of well-known companies are already making use of it within their operations. Here are some examples from various different industries:
Finance and banking
JPMorgan Chase, the England-based investment banking company, uses AI and ML technologies for fraud detection and risk assessment within their operations. Their ML models are used to analyse transaction patterns and flag any suspicious activities, which helps them prevent fraud and ensure higher security in financial transactions.
Healthcare
In healthcare, Galleri® test utilises machine learning capabilities for early cancer detection. It analyses blood samples and identifies any signals of cancer, which can then be reviewed by a medical professional. This enables earlier diagnosis and treatment, which can significantly improve a patient’s chances of full recovery.
Retail and e-commerce
The industry giant, Amazon, is a great example of a retail company that started taking advantage of machine learning technology early on. The organisation uses ML in various different ways: to analyse customer browsing and purchase history, provide personalised product recommendations, and enhance the overall shopping experience for the customers.
Agriculture
In the agriculture industry, John Deere uses with machine learning and machine vision technology to improve crop management and farming precision. They incorporate ML into their farming equipment, which can analyse soil conditions, crop health, and even weather patterns in order to recommend the most optimal practices for higher yields.
Conclusion
All in all, machine learning is a powerful tool that can be utilised in a number of industries and business processes. It can have game-changing effects on an organisation’s operations, significantly improving their overall efficiency. While ML is already utilised by the most successful companies globally, we can expect it to become more and more common in businesses going forward.
