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.
