Machine learning is responsible for most of the accomplishments in applications with artificial intelligence. Many popular services use it for their operation, e.g. systems that recommend content on Netflix, TikTok, and YouTube, search engines Google and Yandex, social networks Facebook and Twitter. There are hundreds of such services — you use them daily without much thought. At the same time, each platform collects as much data on you as possible: what styles you like, most frequently visited pages, your triggers. Then this information is used to train machines so they can predict what you might want to see or hear next.
Machine learning (ML) is a division of artificial intelligence that provides systems with the possibility to learn from data arrays and improve based on experience rather than program code. To put it simply, it is methods applied to computers so they gain their own experience as people do. In science terms, it is methods of analysing input data that automate the development of the algorithmic model. It is based on the idea that machines can be self-sufficient when learning from data, identifying patterns, and then making predictions and coming up with the optimal solutions.
ML is not a recent invention. It dates back to the 1960s and originates from pattern recognition and attempts to teach computers to do certain tasks without programming them. Developers of models wanted to see whether computers would be able to self-learn based on data.
The term ‘machine learning’ was suggested in 1959 by scientist Arthur Samuel. He described it as a process that results in a machine capable of learning without being explicitly programmed to do so. It was relevant to his first invented program that learned to play draughts on its own. Here is how popular research centres define this term:
Stanford University: “It is a discipline that investigates how to make computers work without explicit programming.”
University of Washington: “These are algorithms capable of understanding how to execute important tasks by generalising examples.”
Carnegie Mellon University: “It is a discipline that strives to find answers to such questions as to how to build computer systems that automatically evolve with experience and what are fundamental laws that control learning processes.”
Machine learning is executed in 4 stages:
When developing a machine learning model, it is important to apply an iterative approach because when exposed to new data, it can self-adapt. A model trains itself based on previous calculations to come to reliable repeated solutions.
ML goals are to learn a machine to predict results according to input data, to automate solutions of complex problems, and to increase the accuracy of results. To reach these goals, the following components are required:
Most often, 3 ML methods are used: supervised, unsupervised, and semi-supervised. A supervisor means either a learning sample or an engineer that points out correct answers in defined objects.
For each instance, a supervisor indicates a pair ‘situation—required solution,’ and a model learns from known input and output data, i.e. examples. Here classification (to predict a category) and regression (to predict a value) are applied.
Let’s assume that we have a dataset with two variables: bitcoin prices (input data) and ether prices (output data). We can build a model and using machine learning make price predictions based on the correlation between these cryptocurrencies.
For each instance, the supervisor sets only a ‘situation,’ and the model must independently come up with a solution, that is, without examples, looking for dependencies in the input data. Here clustering (to divide according to similarities), association rules (to determine sequences), and dimensionality reduction (to identify dependencies) are applied.
Let’s assume that we have data with litecoin prices for the past year. We can build a model and assign it a task to predict prices for this year without providing any marks (besides dates). After that, let it find patterns on its own, for example, according to seasons, weekends, and uptrends or downtrends.
For one part of the instances, the supervisor sets the pair ‘situation—required solution,’ and for the other — only ‘situation.’
Let’s assume that we have a dataset with two variables: dogecoin news (input data) and dogecoin prices (output data). Of these, only half of the news and half of the prices are related, that is, we have done half of the markup: such news has passed — the price has changed this way. We will assign the other half of the work to the model and let it determine which of the remaining news influenced the price of this cryptocurrency.
In addition to 3 classic ones, engineers practise 4 more modern ML methods:
Engineers involved in machine learning professionally also apply ensemble methods, i.e. multiple learning algorithms. A group of algorithms may provide more efficient predictions rather than each one individually. In addition, algorithms fix each other’s errors.
ML algorithms determine certain patterns in data and put them to good use. They find natural dependencies that help to come up with error-free solutions and make the most precise predictions.
Moreover, today, machine learning teams create neural networks and provide deep learning more often. A neural network is a model with a set of artificial neurons and connections among them that is built similarly to a network of neuronal cells of a living organism. Here neurons are functions with a multitude of inputs and one output, while connections are channels for neurons to transfer data to each other.
ML develops at an unbelievable pace and is brought in everywhere. All IT corporations have launched their platforms for custom machine learning. Other companies implement these solutions with their services to provide users with unprecedented advantages.
Mail services use ML to protect from spam. Social networks apply it for automated face recognition and identifying relevant tags. Search engines leverage it to find out your preferences to show you personalised search results.
Financial and investment companies need machine learning to analyse market data. Models help them and their investing clients to find investment opportunities, identify market trends, and choose better moments to buy and sell assets.
Insurance companies apply complex statistical models that use various data on clients to predict insurance events. When applying for the development of machine learning systems, these companies get rid of complex solutions to regular problems.
Some car insurance companies take into account driving habits as one of the main factors when determining the insurance cost. They ask policyholders to install special devices inside cars to monitor indicators related to their driving habits. This data is also used when predicting the probability of accidents and creating individual insurance plans.
A striking example of ML is the improved possibilities of search engines. The development of machine learning algorithms and their implementation into search forms on e-commerce websites allows online shops to provide relevant results to user requests. A self-learning search engine will understand better what a user implies and will form a relevant result rather than concentrate only on what a user typed in.
Online shops, as well as offline retailers, engage with machine learning so models can analyse purchase history, and algorithms — recommend accompanying goods. For them, models also collect and analyse data and then use it to personalise marketing campaigns, optimise prices, and plan the supply of goods. At the last — to understand the needs of each customer individually.
ML has found demand in healthcare due to the emergence of devices and sensors that can use data for evaluating the health condition of patients in real time. Models help medical experts to analyse data for detecting trends and red flags that can lead to improvements in diagnostics and treatment.
Data analysis for detecting patterns and trends is used in the transportation field that requires an increase in route efficiency and predictions of potential issues. ML models and algorithms have become useful tools for companies involved in delivery, freight traffic, and public transportation.
Machine learning allows analysing huge chunks of data and increasing the precision of predictions. Although it gives quick and precise results when searching for profitable opportunities or risks, the preparation of proper models might need additional sources and time.
A combination of ML and cognitive computing might make it more efficient when processing big data volumes. Access to big data and neural networks facilitated machine learning in becoming even more beneficial.
When analysing data, a fundamental truth ‘garbage in, garbage out’ is still valid. However, modern ML algorithms are able not only to analyse incoming data but also to clean it to improve results.
Duplicates and inaccuracy of data pose an issue for a business that strives for automating internal processes. The development of a machine learning model will allow increasing efficiency of data input and its quality, that consequently will significantly reduce the number of inaccuracies and duplicates.
ML already works inside services like Amazon and Netflix. Their users buy goods and services and then receive an offer for other interesting products. The more people consume, the better the system understands their preferences and the clearer it makes relevant offers that are as good as the advice of friends. It is a useful tool for improving customer service and loyalty.
You don’t need to trade on a scale comparable to Amazon to use algorithms to achieve similar results. Whether you are a clothing retailer, an insurance agency, or an investment fund, ML can help you spread the word about your products to potential buyers.
There are two ways to improve the quality of recommendations:
Meeting growing customer expectations is an efficient way that companies use to achieve better results. Customers constantly have questions about the goods or services they are paying for. Companies have to respond quickly but it’s hard to do it in real time. Then ML comes to the rescue.
Intelligent chatbots can be used to handle initial support requests. Machine learning makes it appear as if a live employee responds to customers. ML-based chatbots can also be used to respond to requests via social media.
Marketing campaigns depend on timeliness and relevance. Send customers the right messages at the right time so they can move through the purchase funnel. ML helps to quickly get information about the characteristics and preferences of customers.
Using big data combined with ML helps to define the target market and link customer profiles to their preferred products or services. The ability to filter and narrow profiles is useful when targeting in marketing campaigns.
The above-mentioned methods of using machine learning are available not only to IT giants: any company can benefit from this direction in artificial intelligence. Your business can also increase the efficiency of solutions, improve work results, and provide customers with better services if you implement quality machine learning.
We have provided a dozen examples of how ML can automate data input and business processes. Machines help to make this job quicker, cheaper, and more efficient. In practical work, we know many more options of applying ML that can help you achieve performance targets incomparable with manual or semi-automated methods.
If you are interested in applying machine learning, Polygant will help you integrate it into your business. For time frames and the cost of machine learning, please fill in the request form, and our managers will contact you.