What is Machine learning (ML)?

Machine learning (ML) is now part of our everyday lives – from personalised recommendations on streaming platforms and the intelligent features of digital assistants to automated image recognition on social media. But what exactly is machine learning, and how does this key artificial intelligence (AI) technology work in practice?

In this guide, you'll not only get a definition of machine learning, but we'll also explain the different types of ML in simple terms and show real-world applications, such as in marketing or IT security. You'll learn about the challenges of using ML and what the future holds for this fascinating technology.

• Machine learning is a branch of AI where machines learn from data and independently identify patterns without being explicitly programmed

• There are four main types of ML algorithms: supervised, unsupervised, semi-supervised, and reinforcement learning

• Machine learning is used in many areas, such as IT security, marketing, digital assistants, and logistics

• The future of machine learning offers diverse solutions for complex problems but also requires consideration of ethical challenges and data quality

Definition: What is machine learning?

To explain machine learning (ML) in simple terms, let's go back to 1959. This was when the term was first coined by computer scientist Arthur Samuel, who worked on an early learning algorithm to teach machines how to play chess. The core idea was that machines could learn from experience rather than following rigidly programmed rules – a concept that defines machine learning today.

Machine learning is a branch of artificial intelligence that enables machines to learn from data and identify patterns without being explicitly programmed. Instead of following a fixed set of instructions, these systems use algorithms to acquire knowledge from sample data and make predictions or decisions based on it.

How does machine learning work?

Machine learning works by training a system with large datasets to identify patterns and make independent predictions. Using algorithms, it processes data and continuously refines its model through repeated learning cycles to improve accuracy. The goal is to learn from sample data so that the model can be applied to new, unseen data.

To better understand how machine learning works, let's go through the entire process step by step.

How does the machine learning process work in detail?

The machine learning process follows several structured steps:

1. Data collection

The first step is gathering large amounts of training data. This data can come from various sources, such as images, text, or numerical data. The quality and quantity of the data are crucial to the model's success.

2. Data preparation

Next, the collected data is prepared for use. This involves cleaning errors, removing irrelevant information, and structuring datasets into a suitable format for the learning algorithm. Often, data is also normalised to ensure consistency.

3. Feature extraction

At this stage, relevant patterns or features are extracted from the prepared datasets. These features are key pieces of information the algorithm needs to recognise patterns effectively, helping to simplify the data while highlighting essential details for the learning process.

4. Model selection

A suitable machine learning algorithm is then chosen. Different algorithms are used depending on the application and data type. Examples include decision trees, neural networks, and support vector machines.

5. Model training

Once an algorithm is selected, the model is trained using the dataset. The system analyses the data, identifies patterns, and adjusts its internal parameters to make accurate predictions. This step often requires multiple iterations to optimise the model.

6. Model evaluation

The trained model is then tested using a separate set of test data that it hasn't seen before. This step checks how well the model performs on new, unseen data.

7. Model fine-tuning

Based on the evaluation results, the model may be further optimised. This could involve adjusting algorithm parameters or gathering more training data to improve performance.

8. Model deployment

Once the model delivers satisfactory results, it can be deployed in real-world applications. It is then used to analyse new data and make predictions or decisions based on previously learned patterns.

Types of machine learning algorithms

There are four main types of machine learning algorithms:

• Supervised learning: Algorithms learn using labelled data

• Unsupervised learning: Algorithms identify patterns in unlabelled data

• Semi-supervised learning: A mix of labelled and unlabelled data

• Reinforcement learning: Algorithms learn through rewards and penalties

Each method takes a different approach to enable machines to learn from data. Let's take a closer look at each type.

Supervised learning

In supervised learning, the algorithm is trained using a dataset that includes both input data and the correct output values. Each training example is linked to the correct answer, allowing the algorithm to learn how to make predictions based on input data. The goal is to generalise from this training data so that it can accurately predict outcomes for new, unseen data.

Common applications include classification and regression models.

Unsupervised learning

Unlike supervised learning, unsupervised learning works with unlabelled data. This means the algorithm has no predefined outputs and must independently identify patterns and structures in the data.

A common example is clustering, where the algorithm groups data points based on similarities. Unsupervised learning is widely used to uncover hidden relationships in large, complex datasets.

Semi-supervised learning

Semi-supervised learning combines elements of supervised and unsupervised learning. It uses a mix of labelled and unlabelled data, where only a small portion of the dataset is labelled.

The algorithm uses the labelled data as a guide and then applies its learning to the unlabelled data to improve predictions. This method is useful when labelling data is costly or time-consuming.

Reinforcement learning

In reinforcement learning, an algorithm learns through a reward system. It makes decisions that lead to specific actions and receives feedback in the form of rewards or penalties.

The goal is to learn through trial and error which actions lead to the best results. This approach is widely used in areas such as robotics and gaming, where decisions must be made in dynamic environments.

Examples of machine learning applications

Machine learning is used in many industries, from customer relationship management to IT security. Here are some key application areas:

• Customer relationship management (CRM): Optimising customer interactions

• Personalisation in marketing: Tailored content and recommendations

• IT security: Detecting threats and attacks

• Digital assistants: Speech processing and automation

• Mobility and logistics: Improving efficiency and route optimisation

To better understand the versatility of this technology, let's take a closer look at some key machine learning applications and their practical benefits.

Machine learning in customer relationship management (CRM)

Machine learning plays a crucial role in CRM, helping businesses better understand their customers and deliver personalised experiences. ML algorithms analyse vast amounts of customer data to identify behavioural patterns and preferences. Based on this, companies can predict future customer needs and enhance customer retention with tailored offers and optimised communication.

Additionally, learning algorithms can be used to automatically classify and prioritise customer inquiries, improving customer service and identifying sales opportunities more efficiently.

Machine learning for personalisation in marketing

In marketing, machine learning is used to customise content and advertisements based on individual customer interests and preferences. By analysing large datasets, such as browsing and purchasing behaviour, companies can use ML to deliver personalised recommendations and targeted ads.

This not only increases conversion rates but also enhances the customer experience by ensuring that relevant content is shown at the right time. Deep learning models can even predict which products or services a customer is likely to need next.

IT security and machine learning

To detect and prevent threats in real time, machine learning is an indispensable tool in IT security. ML algorithms continuously monitor networks and automatically detect patterns that indicate potential security threats, such as hacking attempts or data breaches.

By learning from new data, these systems can respond more quickly to emerging threats and offer an extra layer of protection against cyberattacks. Additionally, machine learning can detect anomalies, helping to identify unusual activities before they escalate into major security incidents.

Machine learning in digital assistants

Digital assistants such as Siri, Alexa, and Google Assistant rely heavily on machine learning, particularly in the areas of speech recognition and automation. ML enables these assistants to understand speech, analyse user queries, and provide relevant responses.

Through deep learning techniques, these systems continuously improve by adapting to individual user preferences and enhancing response accuracy over time. Thanks to machine learning, digital assistants are constantly learning, making them increasingly intelligent and efficient.

Machine learning in mobility and logistics

In mobility and logistics, machine learning significantly improves operational efficiency, from route planning and vehicle maintenance to warehouse management. Algorithms analyse vast amounts of data to predict traffic patterns, optimise delivery routes, and schedule maintenance in advance.

By using ML, businesses can reduce delivery times, lower costs, and enhance customer satisfaction. In logistics, machine learning helps monitor supply chains in real time and identify bottlenecks before they cause disruptions.

Challenges in using machine learning

Despite its many benefits, machine learning comes with several challenges. One of the biggest hurdles is data quality. ML models require large datasets to produce accurate results, but incomplete or faulty data can lead to incorrect predictions. This means that the success of machine learning heavily depends on data quality.

Another challenge is the explainability of complex models such as deep learning, which often function as black boxes, making it difficult for humans to understand how decisions are made.

Furthermore, implementing and running ML models can be expensive and resource-intensive. Finally, ethical considerations, particularly regarding data privacy and AI bias, remain critical concerns in the responsible use of machine learning.

Conclusion: What does machine learning mean for the future?

Now that we've explained machine learning in simple terms and how it works, the question remains: What role will ML play in the future?

Machine learning has the potential to drive innovation across nearly every industry, from personalised medicine to smart mobility. As the technology becomes more powerful, and as more data becomes available, ML models will become even more precise and efficient.

In areas such as climate protection, education, and healthcare, ML could help solve complex problems faster, automate processes, and create new opportunities. However, as the technology continues to advance, it's also essential to consider ethical challenges and potential risks.

Machine learning will undoubtedly be one of the key technologies of the coming decades.

Frequently asked questions about machine learning

Here are some of the most common questions about machine learning, along with their answers.

What is the difference between AI and machine learning?

Artificial intelligence (AI) is a broad term for machines that can perform intelligence-based tasks similar to humans, such as learning and problem-solving. Machine learning (ML) is a specific subcategory of AI where machines learn from data without being explicitly programmed.

While AI includes many different techniques, machine learning is one of the key methods used to achieve AI.

Is ChatGPT AI or machine learning?

ChatGPT is an AI model based on machine learning, specifically deep learning. It processes huge amounts of data to generate text by recognising patterns and structures in language.

Machine learning is both an example of AI and a core technology behind ChatGPT. While ChatGPT belongs to the AI category because it simulates human-like interactions, its underlying technology relies on machine learning to generate accurate and coherent responses from training data.

It uses neural networks to continuously improve its performance and deliver better responses over time.

What is the difference between deep learning and machine learning?

Deep learning is a specialised form of machine learning that uses artificial neural networks. These networks consist of multiple layers and can process vast amounts of data.

Machine learning covers a range of methods where machines learn from data, but not all ML models use neural networks.

• Deep learning is used for highly complex tasks, such as image recognition and speech processing

• Traditional machine learning includes simpler algorithms like decision trees and linear regression

• Deep learning models require more computing power and larger datasets to function effectively

While machine learning focuses on structured learning from data, deep learning enables systems to autonomously develop better solutions for highly advanced tasks.

Further topics on artificial intelligence

If you want to dive deeper into artificial intelligence, the Bitpanda Academy offers a range of informative guides.

Learn more about topics such as Natural Language Processing (NLP) or the role of robo-advisors in finance. You'll also find in-depth articles on the future of digital investing, including AI trading, investment risks, and cryptocurrency trading.