What is machine learning?
Machine learning is behind many of the tools we use every day, from predicting what movie you¡¯ll enjoy next to helping companies hire the right people. It¡¯s changing the way businesses and people make decisions.
In this article, we¡¯ll break down what machine learning is, how it works, the different types of machine learning and how it¡¯s used in real life.
Defining machine learning
Machine learning is an offshoot of artificial intelligence that allows computers to learn from data without being programmed for every single task. Instead of telling a computer exactly what to do, we give it data, let it figure out patterns and make predictions based on what it¡¯s learned.
For instance, machine learning can analyse past workforce data to predict which employees might be at risk of leaving. This helps HR teams take proactive steps to improve retention.
This ability to learn and improve with more data is what makes machine learning so powerful.
What¡¯s the difference between AI and machine learning?
Artificial intelligence (AI) is when computers are designed to think like humans. This includes doing things like solving problems, understanding language or making decisions.
Machine learning is a part of AI. It¡¯s how computers learn from data to get better at a task without being told exactly what to do every time. Think of AI as the umbrella concept and machine learning as one of the methods used to achieve intelligent behaviour in machines.
How machine learning works
Machine learning starts with data. A model is trained using a large set of examples. The model uses algorithms to find patterns in that data and then applies what it¡¯s learned to new information. The more data it processes, the more accurate and useful the predictions become.
When the model makes predictions, those predictions can be compared to real-world outcomes. If the model gets it wrong, the algorithm adjusts to improve future performance. This continuous learning is what makes machine learning so powerful ¨C? it evolves and improves based on experience, just like humans do.
A machine learning system used in recruitment might learn from previous hiring outcomes to better identify candidates likely to succeed in certain roles. If some predictions turn out to be inaccurate, the system can be fine-tuned to improve its selection process over time.
Types of machine learning
There are three main types of machine learning:
- Supervised learning: Involves training the model on a labelled dataset. This means the data includes both the input (like a CV) and the correct output (such as whether the applicant was hired). The algorithm learns the relationship between the two and applies this to future data.
- Unsupervised learning: Used when the data doesn¡¯t come with predefined labels. The algorithm explores the data to find hidden patterns or groupings. For instance, it might cluster employees based on engagement levels or work habits, helping HR teams identify trends.
- Reinforcement learning: Learns through trial and error. The model receives feedback in the form of rewards or penalties based on its actions. This type is often used in areas like robotics or gaming, but also in dynamic environments like personalised marketing or workforce scheduling.
What are the benefits of machine learning??
Machine learning offers a range of benefits for organisations. One of the biggest is improved accuracy. Whether identifying customer preferences or predicting employee turnover, machine learning helps businesses make more informed decisions based on real data.
Machine learning frees up time for teams to focus on more strategic work by automating routine tasks like screening resumes or processing customer support tickets. This can save money and lead to efficient delivery of services over time.
In HR and workforce management, machine learning can help organisations better understand their people. For example, it can identify gaps in skills development, giving leaders a clearer picture of team needs.
What are the challenges of machine learning??
Despite its advantages, machine learning isn¡¯t without challenges. One major concern is data privacy. Models are only as good as the data they are trained on, and that often includes sensitive personal or organisational information. Ensuring data is collected, stored and used responsibly is essential.
Another challenge is integration. Many organisations struggle to embed machine learning into their existing systems and processes. It can be complex and resource-intensive to get up and running, especially without the right tools or expertise.
Machine learning also requires ongoing data management. The quality of predictions depends on access to fresh, accurate data. Outdated or biased data can lead to flawed outcomes. Ethical issues are also a concern, especially when models unintentionally reflect human biases. For example, if past hiring data is biased, the machine learning model may repeat those patterns, leading to unfair outcomes.
To make the most of machine learning, businesses need to combine strong technical tools with thoughtful governance and routine monitoring.
What are some use cases of machine learning?
Machine learning is already used in HR to improve how organisations hire, support and retain their people. One common use case is recruitment. Machine learning models can scan thousands of resumes and applications, identifying candidates who closely match the requirements of a job. These models also use past hiring data to help HR teams make faster and more informed decisions about who to shortlist.
Another powerful use case is employee sentiment analysis. Machine learning can process feedback from surveys, performance reviews and internal communication channels to identify patterns in employee engagement or well-being. This helps HR teams act early to address concerns, reduce turnover and improve workplace culture.
Outside of HR, machine learning is also transforming operations. Many organisations use machine learning to forecast demand and manage inventory. By analysing past sales trends, seasonal patterns and real-time data, machine learning models help businesses ensure they have the right products available at the right time, reducing waste, improving customer satisfaction and increasing efficiency.
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