There is currently great interest in artificial intelligence and machine learning ( Machine Learning ), as these technologies allow the automation of processes and analyses that were not previously possible.
However, it can be challenging to understand the various terms used to describe the different forms of machine learning and the types of problems that can be solved. So in this article, we’re going to look at some essential Machine Learning (ML) terms, the various forms of machine learning, and the problems we can solve using this technology.
The most common way to perform machine learning is to collect examples (historical data) and then train a model to infer the data.
An ML model is a representation of how to make a decision. It is possible to make a model with an image as input, which decides whether the image contains a person or not, for example. There are different types of ML models such as Decision Trees, Neural Networks, and Support Vector Machines. Unlike a traditional computer program, where people create decision rules manually, ML models learn the rules by analyzing sample data. The model type defines which rules can be known.
Training is how an ML model learns how to decide on interest by analyzing historical data. During training, an optimization algorithm changes the parameter values of the model’s decision rules to make good decisions for the historical data.
After the model is trained on historical data, it can be used to make decisions (make inferences) on new data, which it did not see during training. The model may be suitable for making decisions on historical data but bad for never-before-seen data. In this case, we say that Overfitting has occurred and that the model does not generalize to new data. Achieving generalization is one of the significant challenges of ML, especially when there is little example data. If the model cannot make good decisions even for the training data, we say that Underfitting has occurred, and it may be necessary to use a more complex model. The term prediction can be used synonymously with inference.
Although the most common is to train machine learning models through examples (historical data), some algorithms perform the training by trial and error. These algorithms are often used when the AI model needs to interact with an external environment, such as playing chess against a human opponent. Depending on the case, it is possible to mix the concepts, carry out an initial training through examples, and then incorporate trial and error.
Techniques to train the model with manually annotated data (i.e., an expert says the expected output for each historical data used in training). Used to identify specific patterns (e.g., which object is present in an image). These are the most commonly used techniques.
Techniques for training models with not annotated data (i.e., the expected output for each historical data used in training is not known). It can be used to identify correlations and make groupings (e.g., recognizing which images have similar objects but not recognizing which object is contained in the picture).
Techniques that use trial and error to discover optimal decisions on interacting with the environment or other agents. It is based on the use of positive or negative reinforcements for optimization. Such support may come after a long sequence of actions (i.e., it may not be obvious which actions contributed to the positive outcome). These are the least used techniques.
To choose a suitable solution or technique for your problem, it is necessary to understand what types of issues are usually solved through machine learning and what the terminology is.
Also Read: How To Protect Machine Learning Algorithms
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