Machine learning is a part of artificial intelligence, a science that studies the ability of a computer to learn, without being specifically programmed. Machine learning algorithms are focused on the improvement of computer programs, that are able to teach themselves and that are able to grow and change automatically.
Machine learning algorithms have much to do with the fact of building an analytical model that automates the process of analysing data.
These algorithms, automatically and quickly, produce models that are able to analyse a complex and a larger quantity of data, obtaining faster and accurate results. These findings are valuable and strong predictions that can guide any business towards better decisions and smart actions in real time, with no human intervention
We can find behavioural patterns hidden in this amazing quantity of data, even that one that is not visible to analysts, discovering new customer segments, identifying buyer behaviour, predicting faults … That is the role of machine learning algorithms.
Machine learnings algorithms goals
The primary goal of machine learning algorithms is to develop general algorithms, taking into consideration two essential variables, that are time and space efficiency.
The impact of using machine learning algorithms is usually more exact and precise than direct human programming. The reason is that machine learning algorithms are able to analyse considerable volumes of data, a quantity of information that humans could never analyse without machines. Even an expert analyst could probably make mistakes and be guided by incorrect impressions; while observing only a relatively small amount of data.
The process that involves the implementation of machine learning algorithms is comparable to the Data Mining procedure. Both systems are looking for patterns by observing data. However, machine learning algorithms use that data to detect patterns but also to adjust the computer programs and operations. Machine learning algorithms are often classified by being supervised or unsupervised.
Supervised machine learning algorithms can perform with new data that has been learned in the past. Unsupervised machine learning algorithms is able to draw a conclusion from datasets.
As we were specifying, human beings have classified machine learning algorithms into 3 categories: supervised machine learning algorithms, unsupervised machine learning algorithms and reinforcement machine learning algorithms.
Supervised Machine learning algorithms
In supervised machine learning algorithms, the output datasets are used to prepare the machine and to obtain the desired outputs. These Supervised Machine Learning algorithms can be subdivided into:
When data or values are being used to anticipate a category or a pattern, these supervised learning algorithms are called classification algorithms.
When data or values are already being predicted, these supervised machine learning algorithms are called regression algorithms. This procedure includes many techniques that have to do with modelling and analysing several variables.
- Anomaly detection
The goal of machine learning algorithms can also be related to determining data points that seem to be unusual. The anomaly detection algorithms method tries to find out what a normal activity looks like thanks to historical non-fraudulent actions. This process, at the same time, defines actions that seem to be different than the usual ones.
Unsupervised Machine Learning algorithms
In unsupervised machine learning algorithms, there is no datasets provided, instead the data is concentrated in different locations. Nowadays, there are many types of machine learning algorithms that try to discover correlations without any external inputs, only using the unique raw data available.
Reinforcement Machine learning algorithms
It allows machine and software agents to automatically define the ideal behaviour within a specific context, taking in account the feedback from the environment.
To learn about behaviour, the machine only needs a simple feedback; this is known as the reinforcement signal. This behaviour can be learnt just one single time, while it keeps adjusting and improving as time goes by.
List of Common Machine Learning Algorithms
These are some of the most commonly used machine learning algorithms, that can be applied to almost any data problem:
- Linear Regression
- Logistic Regression
- Decision Tree
- SVM (Support Vector Machine)
- Naive Bayes
- KNN (K- Nearest Neighbors)
- Random Forest
- Dimensionality Reduction Algorithms
- Gradient Boost & Adaboost