Machine learning powers many technologies you use daily, from spam filters and voice assistants to product recommendations and fraud detection systems.
TL;DR
- Machine learning is a type of AI that learns from data instead of fixed rules.
- It helps computers recognise patterns, make predictions, and improve over time.
- There are four main types: supervised, unsupervised, semi-supervised, and reinforcement learning.
- Businesses use ML for automation, forecasting, and data analysis.
- Kleritt helps companies apply machine learning safely, strategically, and in line with EU regulations.
How Machine Learning Works
Machine learning works by feeding large amounts of data into an algorithm, which then identifies patterns and relationships. Over time, it uses those patterns to make predictions or decisions without human intervention.
A typical ML process includes:
- Data collection: gathering structured and unstructured data.
- Training: using data to teach the algorithm what outcomes to expect.
- Testing: evaluating how well the model performs on new, unseen data.
- Deployment: integrating the model into a business workflow.
- Continuous learning: updating the model as new data arrives.
For example, an e-commerce platform might use ML to predict which products a customer is most likely to buy, based on past browsing and purchasing behaviour.
The Four Main Types of Machine Learning
- Supervised Learning: The model is trained on labelled data (where the correct answer is already known). Example: predicting customer churn or classifying emails as spam or not.
- Unsupervised Learning: The model looks for hidden patterns in unlabelled data. Example: grouping customers by behaviour or preferences (clustering).
- Semi-Supervised Learning: Combines both labelled and unlabelled data to improve accuracy when full datasets are unavailable. Example: identifying anomalies in network security or financial transactions.
Reinforcement Learning
The model learns by trial and error, receiving feedback through rewards or penalties.
Example: AI systems that play games, manage logistics, or optimise marketing budgets.
Machine Learning vs Artificial Intelligence
Although the two terms are often used interchangeably, they’re not the same:
- Artificial Intelligence (AI) is the broader field, machines that can act intelligently.
- Machine Learning (ML) is a subset of AI focused on enabling machines to learn from data.
In short: AI is the concept; ML is one of the main methods that makes it work.
Business Applications of Machine Learning
Machine learning is already transforming industries by making operations faster, smarter, and more data-driven.
Examples include:
- Sales & Marketing: lead scoring, customer segmentation, and personalised ads.
- Finance: fraud detection, credit scoring, and risk prediction.
- E-commerce: dynamic pricing, product recommendations, and inventory forecasting.
- Manufacturing: predictive maintenance and quality control.
- Healthcare: disease detection and diagnostics using data analysis.
ML helps companies make smarter decisions, reduce costs, and increase customer satisfaction, all based on real data rather than guesswork.
The Challenges of Machine Learning
Despite its potential, ML also comes with challenges:
- Data quality: poor or biased data leads to inaccurate models.
- Transparency: explaining how models make decisions is often difficult.
- Compliance: GDPR and the upcoming EU AI Act require strict data handling.
- Maintenance: models must be updated regularly as conditions change.
That’s why businesses need a structured approach to developing and managing ML systems, not just a one-time setup.
How Kleritt Helps Businesses Apply Machine Learning
At Kleritt, we help companies use machine learning in practical and compliant ways. Our focus is on making AI accessible, understandable, and valuable for every organisation.
We offer:
- AI & ML Strategy: identifying where ML can deliver measurable ROI.
- Implementation: integrating ML into existing systems and workflows.
- Model training & evaluation: ensuring accuracy, fairness, and scalability.
- Compliance: applying GDPR and AI Act guidelines for data protection and transparency.
With Kleritt, your machine learning initiatives are not just experiments, they become real, reliable business drivers.
