A Guide to the Types of Machine Learning Algorithms and Their Applications
This free guide to the types of machine learning contains what you need to know and the different ways to maximise AI system performance.
There is little doubt that today's companies can gain a competitive boost from deploying artificial intelligence (AI) in back-office administration and customer support roles. Fundamental to this automation is machine learning, of which there are various types. To discover how many different kinds of machine learning there are, please read on.
Types of machine learning
In this time-saving guide to machine learning techniques, we discuss the different varieties of machine learning (ML). Then, you will find examples of their applications in customer service and problem-solving. Also, towards the end of this article, you will find an overview of specific algorithms and their suitability for different tasks. Firstly, the main types are:
We can also include a hybrid training model that combines supervised and unsupervised methods elements: semi-supervised learning. Additionally, the multi-inference method combines various techniques from all three above.
Supervised learning is the simplest type of ML to implement and understand. For instance, let us compare this method to teaching a young child new language vocabulary with picture-and-word cards. Similarly, algorithms that examine labelled examples like this will eventually learn to predict the correct label for newly-introduced examples. A vital part of this training process is to provide feedback on whether predictions are accurate or not. Supervised learning teaches the model to tag new data using available imported data and established responses. In this way, algorithms can observe previously unseen instances and label them with reasonable to excellent levels of accuracy. This method focuses on single tasks and is, therefore, task-oriented. Its most common applications include measuring the popularity of advertisements through clicks received. In addition, spam classification filters that detect malicious email content and mass mailings do so by analysing previously labelled examples. Recently, such systems have become increasingly able to learn from user preferences, thus providing inbox customisation and increased efficiency in the workplace. Facial recognition systems such as in social media – Facebook is a typical example – can photo detect faces and suggest names. Then, users either click to confirm, select someone else, or add a new name. As a result, accuracy improves steadily.
Next, unsupervised learning does not use labels. Instead, the algorithm employs tools to understand data properties. Then, often with several iterations, it organises the inputs so that humans and other intelligent algorithms can make sense of the newly-organised groups or clusters. The attraction of unsupervised learning is that it can process unlabelled datasets. As untagged data is the most commonly available type, this ability is a clear advantage. The most powerful algorithms can decipher reams of previously unprocessed information and transform it into valuable business insights, thus delivering significant competitive advantage. In the future, we are likely to see such AI machines correlating available knowledge and boosting productivity in various industries. Let us consider an algorithm that scans a database of every research paper ever published. This ML model would eventually sort and group database entries into relevant, recognisable groups using unsupervised learning. Thus, the database manager and users would be able to consult the state of research and progress to date within the domain of their choice. Furthermore, when a researcher, scientist, or laboratory technician connected to this example network and wrote up notes, the intelligent algorithm would comb through existing information. It might suggest related search, citations and useful findings to facilitate further research and make significant breakthroughs. Essentially, unsupervised learning is data-driven. Typical applications include page ranking, sales recommendations based on previous buying habits and home entertainment based on viewing history. Such systems consider previous choices or purchases in conjunction with customer segmentation techniques. In customer support departments, companies can analyse their journal logs. AI can identify recurrent problems and frequently asked questions by clustering similar incidences. In unsupervised learning, labels and the associated data preparation are not necessary. Thus, algorithms can work with much larger data sets. However, the ML model might have hidden structures because there are no labels. While this ability is sometimes a welcome versatility, it can sometimes produce unusual results. In one notable example, an algorithm to analyse chest X-ray images learnt to associate tuberculosis with older scanner machines by checking the scanner model or date of installation. As it happened, these older radiography units were in third-world countries where tuberculosis diagnoses were prevalent. So although – fortunately – most of the ML model diagnoses were accurate, the underlying logic was nonetheless flawed.
Reinforcement learning is different from the previous two methods, which are straightforward to distinguish by the absence of labels. In short, the reinforcement method works through trial and error, with feedback cues provided to the AI model during training. Positive reinforcement indicates a correct decision, while negative queues indicate error conditions. Over time, the algorithm learns to make fewer mistakes. This type of learning is potent in video games. However, it also has important uses in industry, where simulation techniques teach machines to perform optimally, waste less and use resources to maximum effect. This optimal management can balance loads and minimise costs, ideal for energy-intensive applications such as server farm management or industrial processes. Nonetheless, it is possible to combine these different methods, in which case the distinctions between them blur somewhat. For instance, we might have an AI system trained using semi-supervised and reinforcement learning, such as advanced robots.
Finally, multi-instance learning features human involvement, though less than supervised regimes. Under this method, data preparation staff usually label bags or groups of samples instead of tagging individual examples or data items. Given cases may occur more than once. By determining that some or all of the instances match a target label, the algorithm learns to predict labels for new bags after analysing the composition of their multiple unlabelled examples. In other words, class labels apply to each bag of data; the machine learns to infer class from the instances that make up the data bag. After assigning class labels, such projects often start training the new ML model with standard supervised algorithms.
Selecting the right type of machine learning
Suppose you are wondering how to choose the best type of machine learning algorithm. In that case, there are various factors that will influence the decision. The most important are:
Data size and availability.
Data quality and diversity.
The answers or business solutions sought.
The level of accuracy required.
The availability of financial resources and working time for data preparation and machine training.
Deciding on the correct method requires an appreciation of business needs, precise task specifications and shrewd evaluation. Additionally, a good appreciation of statistics is helpful in data preparation and ML configuration tasks. To a large extent, evaluating which algorithm will perform any given task most accurately requires professional judgement. Also, it is essential to be prepared to experiment with more than one solution if necessary. Expertise in dimensionality reduction is also valuable. This technique simplifies data handling and speeds up predictions while maintaining accuracy. In the field of ML, large datasets are increasingly common. However, the volume of raw information means that often, they are not easy to interpret. Principal component analysis addresses this potential problem. It reduces the dimensionality of large datasets to make them easier to analyse. Yet, at the same time, it minimises information loss.
Discover how training data can make or break your AI projects, and how to implement the Data Centric AI philosophy in your ML projects.
Popular machine learning algorithms
Below, we introduce some of the most common algorithms in machine learning. As you will see, the different methods use various statistical inference methods to meet a range of business or project requirements.
Naive Bayes Classifier
Used in supervised learning to classify values independently, the Naive Bayes algorithm uses probability to predict class or category based on given features. Although relatively simple, this algorithm performs surprisingly well.
This algorithm is suitable for unsupervised learning. It categorises unlabelled data by finding groups within the results. Under this method, the value of the variable K represents the number of groups. The algorithm reiterates several times to assign each data point to one of the K groups, in line with the data characteristics.
Support Vector Machine Algorithms
These supervised learning models analyse data used for classification and regression analysis. Essentially, Support Vector Machine (SVM) algorithms categorise data after training with a set of examples, marked as belonging to one of the two categories.
Suitable for supervised learning, ML algorithms that use linear iteration are the most straightforward within the regression category. They enable us to understand the relationship or correlation between two continuous variables.
Logistic regression estimates the likelihood of an event based on an existing dataset. Thus, its outputs are Boolean, i.e. a binary 0 or 1. This type of algorithm is popular in supervised learning.
Artificial Neural Networks
In reinforcement learning, an artificial neural network (ANN) contains nodes or neurons arranged in a series of layers. Each node connects to layers on either side and upwards and downwards. This efficient structure is similar to the natural transmission of impulses in the brain. Working in unison, nodes in ANNs learn to solve specific problems. By example and through experience, they model non-linear relationships in multi-dimensional data. Their power is their ability to spot patterns among the input variables, which initially might be challenging to understand. Additionally, unsupervised learning models can use autoencoders. These ANNs can deduce a lower-dimensional representation – or encoding – for given datasets. The technique trains ML models to capture the essential parts of the input image and simplify higher-dimensional data.
A decision tree is a flow-chart-like structure that uses a branching method to illustrate every possible decision outcome. Each node represents a test on the input within these supervised learning structures. In contrast, each branch is the outcome of that decision. This procedure is suitable for classification and regression.
Random Decision Forests
Random forest algorithms are used in supervised learning to combine various classifiers to produce accurate results. These regression algorithms employ decision trees as a form of logic. As input data passes through the tree-like graph or model, the items split into progressively smaller sets according to the conditions set during design and training.
This supervised learning algorithm estimates how likely a data point is to be a member of one group or another. In essence, the nearest neighbours method compares each data item's relative departure from the centre point of established groups. These hierarchical clustering methods fall into two categories. The agglomerative type makes predictions and decisions by analysing the proximity of data items. In contrast, the divisive analysis approach focuses on their separation or distance apart.
Expert ML support
When choosing machine learning algorithms for your business analytics, there are various points to consider. However, you don't need to be a data scientist or a statistics expert to use the most appropriate model for your systems and projects. Kili is the complete solution to iterate smoothly, train your AI with ease and deliver successful projects that are ready to produce results. With Kili, you can better manage and optimise training data, whether images, video, text, PDF files or voice inputs. The innovative solution enables fast annotation, easy collaboration and total quality control. Available either as a cloud service or local installation to match all business requirements, Kili allows you to make the most of the advantages of machine learning techniques. It bridges the gap between businesses and ML experts. If you are a technical CXO, CTO or data lab decision-maker, we invite you to contact our approachable experts for further information or to discuss your requirements.
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