Methods of Machine Learning
When developing an algorithm for a machine learning model, a programmer can select how to train it using a particular learning method.
Once the machine has developed a learning method, it is ranked on a maturity scale to measure its ability to learn independently.
These methods of machine learning include:
Supervised learning
Supervised learning is the most common and accurate method to train algorithms for machine learning. This is because the learning is guided using existing data to get a prediction or outcome.
Imagine you're back at school where a teacher instructs you to solve a problem in a maths class. This situation is similar to supervised learning for training an algorithm; data is provided so the model can learn from it. Only one answer and one solution are given to help the model learn easily.
Unsupervised learning
Unsupervised learning is the process where the model learns for itself how to categorise and interpret data with no pre-existing knowledge. It receives unseen data without any instructions or knowledge of how to categorise it and will then group data and categorise the results to guess the outcome. This method is commonly used for finding common patterns and groupings within data.
Reinforcement learning
Reinforcement learning is when an algorithm learns to react to an environment on its own. There is always a start and an end for the model; however, there might be different paths to reaching the end - similar to a maze.
Machines, like children, can be at different maturity levels. Just as a very immature child needs a lot of guidance, and one who is very mature needs very little, a mature system runs essentially on autopilot. It can update itself with minimal human intervention. An immature system will need significantly more human intervention.
There are roughly 4 levels of maturity for machine learning:
Blank slate: This stage means starting from the very beginning, building the machine to perform its selected tasks. This first step involves adding all the necessary raw data to the system.
One-off model production: After inputting all the required data, it's time to test the system! It's important in this stage to set up analytics to track the system's performance over time.
Manual model updates: After a system has been deployed, it must learn to adapt to the ever-changing world around us. This means the system needs to undergo frequent updates with more recent data to maintain its performance.
Autopilot: This is the highest maturity level that every programmer aims to get their machine learning system to achieve. The model can update itself, interpret data automatically, and deliver more sophisticated solutions.
(Flender, 2021)
Summary
Supervised learning is when a model learns from given data with guidance.
Unsupervised learning can perform more complex tasks than supervised learning, but its predictions are also less accurate.
Reinforcement learning is specifically used to find various solutions to problems.