Machine learning models train in a variety of different ways. What type of training depends on the problem, available data, and the learning algorithm selected.

Some common types of training are:

Supervised Learning:

• The learning algorithm trains using a labelled dataset, where the input pairs with an output.
• The algorithm learns to map the input to an output based on what it learns from the training data.
• Tasks such as classification (commonly used during image recognition and spam) and regressions (used to predict house prices and stock market trends) require supervised learning as they are dynamic.

Unsupervised Learning:

• It involves the use of algorithms and techniques which enable the ability to identify and learn patterns, relationships, and groupings without needing any labelled data or explicit guidance.
• It allows AI systems to explore data without guidance and discover hidden patterns, relationships, and groupings.
• It does so using the following techniques:
• Clustering algorithms group similar data points based on their properties. Its main goal is to identify groups of data points which share common characteristics or exhibit similar behaviour. Examples of clustering algorithms are k-means, hierarchical clustering, and density-based clustering.
• Dimensionality reduction reduces the number of variables or data points available while preserving essential information. Principal Component Analysis (PCA), t-SNE (t-Distributed Stochastic Neighbor Embedding) and Autoencoders are commonly used techniques.
• Anomaly detection identifies rare and unusual patterns present in data that are inconsistent with the others. It learns the behaviour of data and then detects instances where the behaviour is different. Features and services such as fraud detection, network intrusion detection or equipment failure protection use anomaly detection to function.
• Association rule learning discovers interesting relationships or associations between variables in a dataset. It then identifies patterns of co-occurrence among those variables. The Apriori algorithm is a well-known example of association rule learning.
• Generative modelling understands the distribution of data. Models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) can generate new data samples that resemble the training data. Use cases such as data generation, image synthesis (artificially generating images computationally), or anomaly detection.

Semi-supervised learning:

• This learning uses a combination of both labelled and unlabelled data to improve a model’s performance.
• By integrating additional information into labelled data, the AI model can generalize better and make more accurate predictions.
• This type of training is handy when obtaining labelled data is expensive or time-consuming by using a small amount of labelled data with a large amount of unlabelled data.

Reinforcement learning (RL):

• It is a subfield of artificial intelligence which focuses on training an agent to make decisions in a given environment, learn from feedback, and improve its decision-making over time.
• It focuses on learning through trial-and-error interactions with an environment.
• Reinforcement learning involves the following:
• Defining a problem: A task or problem is determined, which the AI model needs to learn how to solve. It can range from playing a game to controlling a robot or optimizing a complex computing system.
• Defining the environment: An environment is created which can mimic the task or problem. It can provide the model with states, allowing it to take action, and the environment generates rewards according to those actions.
• Defining the state space and action space involves specifying the possible states the model will observe and the series of actions it can follow in each state.
• Defining the reward structure: A reward function provides feedback to the AI model based on its actions. It guides the model towards achieving the desired solution to a problem and how to achieve that solution.
• Defining the learning algorithm: A reinforcement learning algorithm to train the model is selected. Popular reinforcement learning algorithms are Q-learning, policy gradient methods, and actor-critic methods, each having advantages and disadvantages.
• Training loop: Interaction happens between the model and environment where the model observes a state, takes actions, receives feedback, and updates depending on the reinforcement learning algorithm selected.
• Exploration and Exploitation: A balance between Exploration and Exploitation should be present to ensure the model explores different actions to determine the optimal approach while exploiting the knowledge learnt to maximize reward.
• Model Optimization: Refining of the AI model takes place to improve its decision-making ability. It involves updating parameters such as weight adjustment in the neural network.
• Evaluation: Assessing the performance of the trained AI models takes place to measure its ability to solve a task or problem. Refinement of the training process takes place to improve performance if necessary.

Transfer learning:

• It involves training an AI model on one task and using its training and learnt knowledge to improve performance in a different yet related task.
• Rather than training a model from scratch, we use a pre-trained model from a large dataset as a starting point.
• Its training and learning are transferred and fine-tuned for a new task having less labelled data.

Online learning:

• It is also known as incremental learning or streaming learning.
• It is a training approach where we update the model continuously as new data becomes available.
• This approach is beneficial when incoming data is continuous.
• We use it in applications which require real-time or dynamic learning, such as online recommendation systems or fraud detection.