1. Introduction
Machine learning is a sub-field of artificial intelligence (Al) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
According to Arthur Samuel, Machine Learning algorithms enable computers to learn from data, and even improve themselves, without being explicitly programmed.
Machine learning (ML) is a category of algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output while updating outputs as new data becomes available.
The goal of machine learning generally is to understand the structure of data and fit that data into models that can be understood and utilized by people.
For the process of learning (model fitting) we need to have available some observations or data (also known as samples or examples) in order to explore potential underlying patterns, hidden in our data. These learned patterns are nothing more than some functions or decision boundaries.
Machine learning is a continuously developing field. Because of this, there are some considerations to keep in mind as you work with machine learning methodologies or analyze the impact of machine learning processes.
These patterns are learned by the systems (computer systems) automatically without human intervention or input.
There are much more examples of ML in use.
- Prediction — Machine learning can also be used in the prediction systems. Considering the loan example, to compute the probability of a fault, the system will need to classify the available data in groups.
- Image recognition — Machine learning can be used for face detection in an image as well. There is a separate category for each person in a database of several people.
- Speech Recognition — It is the translation of spoken words into the text. It is used in voice searches and more. Voice user interfaces include voice dialing, call routing, and appliance control. It can also be used a simple data entry and the preparation of structured documents.
- Medical diagnoses — ML is trained to recognize cancerous tissues.
- The financial industry and trading — companies use ML in fraud investigations and credit checks.
2. The main category of Machine Learning
Machine learning algorithms are usually categorized as supervised or unsupervised.
2.1 Supervised Machine Learning Algorithms/Methods
For this family of models, the research needs to have at hand a dataset with some observations and the labels/classes of the observations. For example, the observations could be images of animals and the labels the name of the animal (e.g. cat, dog etc).
These models learn from the labeled dataset and then are used to predict future events. For the training procedure, the input is a known training data set with its corresponding labels, and the learning algorithm produces an inferred function to finally make predictions about some new unseen observations that one can give to the model. The model is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct intended output (ground truth label) and find errors in order to modify itself accordingly. (e.g. via back-propagation).
Supervised models can be further grouped into regression and classification cases:
- Classification: A classification problem is when the output variable is a category e.. “disease”/”no disease”
- Regression: A regression problem is when the output variable is a real continuous value eg. stock price prediction
Some examples of models that belong to this family are the following SVC, LDA, SVR regression, random forests, etc.
2.2 Unsupervised machine learning algorithm/methods
For this family of models, the research needs to have at hand a dataset with some observations without the need of having also the labels/classes of the observations.
Unsupervised learning studies how the system can infer a function to describe a hidden structure from unlabeled data. The system doesn’t predict the right output, but instead, it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
Unsupervised models can be further grouped into clustering and association cases:
Clustering: A clustering problem is where you want to unveil the inherent groupings in the data, such as grouping animals based on some characteristics Textures eg number of legs.
Association Asociation rule learning is where you want to discover association rules such as people that buy also send to buy X also tend to buy Y.
Some examples of models that belong to the family are the following PCA, Kmean, DECCAN, mixture models, etc.
2.3 Semi-supervised machine learning algorithms/methods
This family is between the supervised and unsupervised learning families. The semi-supervised models use both labeled and unlabeled data for training.
2.4 Reinforcement machine learning algorithms/methods
This family of models consists of algorithms that use the estimated eros as rewards or penalties. If the error is big, then the penalty is high and the reward low. If the error is small, then the penalty is low and the reward high.
Trial or search and delayed reward are the most relevant characteristics of reinforcement leaming This family of models allows the automatic deterministic of the ideal behavior within a specific context in order to minimize the desired performance.
Reward feedback is required for the model to learn which action is best and this is known as” the reinforcement signal.”
3. Summary
Supervised: All the observations in the dataset are labeled and the algorithms learn to predict the output from the input data.
Unsupervised: All the observations in the dataset are unlabeled and the algorithms learn to inherent structure from the input data.
Semi-supervised: Some of the observations of the dataset are labeled but most of them are usually unlabeled, So, a mixture of supervised and unsupervised methods is usually used.
