Created by American computer scientist Arthur Samuel in 1959, the term machine learning is defined as a “computer’s ability to learn without being explicitly programmed.”
At its most basic, machine learning uses programmed algorithms that receive and analyze input data to predict output values within an acceptable range. As new data is added to these algorithms, they learn and optimize their operations to improve performance and develop intelligence over time.
There are four types of machine learning algorithms: supervised, semi-supervised, unsupervised and reinforced.
In supervised learning, the machine is taught by example. The operator provides the machine learning algorithm with a known dataset that includes desired inputs and outputs, and the algorithm must find a method to determine how to arrive at these inputs and outputs. While the operator knows the correct answers to the problem, the algorithm identifies patterns in data, learns from observations and makes predictions. The algorithm makes predictions and is corrected by the operator – and this process continues until the algorithm achieves a high degree of accuracy / performance.
Under the umbrella of supervised learning falls: classification, regression and forecasts.
- Classification: In classification tasks, the machine learning program must draw a conclusion from observed values and determine to
which category new observations belong to. For example, when filtering emails as spam or not as spam, the program looks at existing observational data and filters emails accordingly.
- regression: In regression tasks, the machine learning program must estimate – and understand – the relationship between variables. Regression analysis focuses on a dependent variable and a number of other changing variables – making it particularly useful for prediction and prediction.
- forecasting: Forecasting is the process of making predictions about the future based on past and present data and is often used to analyze trends.
Semi-supervised learning is similar to supervised learning, but instead uses both labeled and unlabeled data. Labeled data is essentially information that has meaningful tags so that the algorithm can understand the data, while unlabeled data lacks that information. By combining these techniques, machine learning algorithms can learn to tag unlabeled data.
Here, the machine learning algorithm studies data to identify patterns. There is no answer key or human operator to provide instruction. Instead, the machine determines correlations and relationships by analyzing available data. In an unsupervised learning process, the machine’s learning algorithm is left to interpret large datasets and address these data accordingly. The algorithm tries to organize this data in some way to describe their structure. This can mean grouping the data into clusters or arranging them in a way that looks more organized.
As it assesses more data, its ability to make decisions about that data gradually improves and becomes more refined.
Unsupervised learning techniques include:
- clustering: Clustering involves grouping sets of similar data (based on defined criteria). It is useful to segment data into multiple groups and perform analysis on each dataset to find patterns.
- Dimension reduction: Dimension Reduction reduces the number of variables considered to find the exact information required.
Reinforcement learning focuses on regimented learning processes, where a machine learning algorithm is provided with a set of actions, parameters and final values. By defining the rules, the machine learning algorithm then tries to explore different options and possibilities, monitoring and evaluating each result to determine which is optimal. Reinforcement learning teaches the machine trial and error. It learns from past experiences and begins to adapt its approach in response to the situation to achieve the best possible result.
Deciding which machine learning algorithms to use
Choosing the right machine learning algorithm depends on several factors, including but not limited to: data size, quality, and diversity, as well as the answers companies want to derive from this data. Additional considerations include accuracy, training time, parameters, data points and more. Therefore, choosing the right algorithm is both a combination of business needs, specification, experimentation and available time.
Even the most experienced data scientists cannot tell you which algorithm will perform best before experimenting with others. However, we have prepared one machine learning algorithm cheats sheets, which helps you find the one that best suits your specific challenges.