Machine Learning algorithms are being used more often than we can imagine and there is a good reason for that. Let's see what kind of different Machine Learning algorithms exist and how they can help us in solving everyday life problems.
Many different Machine Learning algorithms are widely used in many areas of our life and they help us to solve some everyday problems. Algorithms can help us not only to recognize images, videos, and texts, but are also used to fortify cybersecurity, improve medical solutions, customer service, and marketing.
Basically, there are few different types of Machine Learning algorithms. There is a major distinction between supervised learning and unsupervised learning techniques. Let’s see what are the main differences between them and how specifically they can help us.
To put it simply, we train an algorithm and at the end pick the model that best predicts some well-defined output based on the input data.
Supervised techniques adapt the model to reproduce outputs known from a training set (e.g. recognize car types on photos). In the beginning, the system receives input data as well as output data. Its task is to create appropriate rules that map the input to the output. The training process should continue until the level of performance is high enough. After training, the system should be able to assign an output objects which it has not seen during the training phase. In most cases, this process is really fast and accurate.
There are two types of Supervised Learning techniques: Regression and Classification. Classification separates the data, Regression fits the data.
Regression is a technique that aims to reproduce the output value. We can use it, for example, to predict the price of some product, like a price of a house in a specific city or the value of a stock. There is a huge number of things we can predict if we wish.
Classification is a technique that aims to reproduce class assignments. It can predict the response value and the data is separated into “classes”. Examples? Recognition of a type of car in a photo, is this mail spam or a message from a friend, or what the weather will be today.
In this Machine Learning technique, we do not have any outcome variables to predict. The computer is trained with unlabeled data. Unsupervised techniques aim to uncover hidden structures, like find groups of photos with similar cars, but it's a bit difficult to implement and is not used as widely as supervised learning.
Unsupervised techniques may be used as a preliminary step before applying supervised ones. The internal structure of the data may provide information on how to better reproduce outputs.
In unsupervised techniques, we have clustering and dimensionality reduction.
Clustering is used to find similarities and differences. It groups similar things together. Here we don’t provide any labels, but the system can understand data itself and cluster it well. Unlike classification, the final output labels are not known beforehand.
This kind of algorithm can help us solve many obstacles, like create clusters of similar tweets based on their content, find groups of photos with similar cars, or identify different types of news.
Dimensionality reduction is used to find a better (less complex) representation of the data. After applying such a process, the data set should have a reduced amount of redundant information while the important parts may be emphasized. In practice, this could be realized as a removing a column from a database from further analysis.
In the previous two types of Machine Learning techniques, there are no labels or labels are present for all the observations. Sometimes, we need something between these two. In such situations we can use Semi-Supervised Learning, which refers to a learning process in which lots of output values (the ones we want to predict) are missing. It requires applying both supervised and unsupervised methods in order to obtain useful results. This is often the case within medical applications, in which medical doctors are unable to manually classify/mark all types of illness due to the overwhelming amounts of data.
Sometimes, the required value of the output is not known explicitly, but the system provides feedback on the provided output. Learning based on such feedback is called Reinforcement Learning. This is used, for example, for training the gaming AI in the game NERO. Another example can be found in schools. Students learn about a specific topic (reinforcement learning), then they sit an exam, and the teacher gives them grades without specifying which answers were correct and which were not.
Machine Learning can identify patterns that we are unable to see or find in huge amounts of data. There are different Machine Learning algorithms which are well suited for many different types of situations, such as Supervised and Unsupervised Learning, as well as Semi-Supervised and Reinforcement learning, which are somewhere between the former two. All together, they can help all of us solve many problems and make new discoveries.