Machine learning is responsible for most of the advances in applications with artificial intelligence. Many popular services use it for their operations, 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 genres you like, most frequently visited pages, your reactions. 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 via experience rather than code. Put simply, it is methods applied to computers so they gain their own experience as people do. In technical terms, it is methods of analysing input data that automate the development of an 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 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 coined 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 concerned his first invented program that learned to play draughts on its own. Here is how popular research centres define this term:
Thanks to new technology and big data, current developments in machine learning have given it new momentum. Now it drastically differs from what it was in the previous century.
Machine learning is done 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 obtain reliable, repeated solutions.
ML goals are to teach a machine to predict results according to input data, retrieve solutions to complex problems, and increase the accuracy of results. To reach these goals, the following components are required:
Three ML methods are commonly used: supervised, unsupervised, and semi-supervised. A supervisor can either be a learning sample or the engineer that indicates correct answers in defined objects.
These methods are considered to be standard. They are applied in learning systems in most online services. Most likely, you have used the results of their work, so let us review them in detail.
In each instance, the supervisor indicates a training set of input objects and corresponding output, that is, a situation and its solution, and the model learns from this known data, i.e. examples. Here classification (to predict a category) and regression (to predict a value) are used.
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 use machine learning to make price predictions based on the correlation between these cryptocurrencies.
The following algorithms are used as part of this method: a decision tree, linear regression, logistic regression, support-vector machines, a naive Bayes classifier.
In each instance, the supervisor provides 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 on 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 upward or downward trends.
The following algorithms are used as part of this method: latent semantic analysis, principal component analysis, k-means clustering, mean shift, singular value decomposition, Apriori, DBSCAN.
In one part of the instances, the supervisor provides the pair ‘situation—required solution,’ and in 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 occurred — the price 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 standard 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 identify certain patterns in data and put them to good use. They find natural patterns that better ensure error-free decisions and increase the accuracy of predictions.
Moreover, machine learning teams today often create neural networks and conduct deep learning. A neural network is a model with a set of artificial neurons and connections among them that is built similarly to a network of nerve cells in a living organism. Here, neurons are functions with multiple inputs and one output, while connections are channels for neurons to transmit data to each other.
ML is developing at an incredible pace and is being adopted everywhere. All IT corporations have launched their platforms for custom machine learning. Other companies are incorporating these solutions into their services to provide users with unprecedented advantages.
Mail services use ML to fight spam. Social networks use it for automatic facial recognition and the identification of relevant tags. Search engines use it to learn your preferences to show personalised search results.
With the growth of big data, the development of machine learning solutions has found demand in finance, insurance, commerce, healthcare, transportation, and other fields.
Financial and investment companies need machine learning to analyse market data. Models help them and their investing clients to look for investment opportunities, identify market trends, and choose better moments to buy and sell assets.
Banks apply intelligent data analysis to identify clients with high-risk profiles and also for cyber surveillance of fraud detection.
Insurance companies apply complex statistical models that use various data on clients to predict insurance events. By turning to the development of machine learning systems, these companies get rid of complex solutions to regular problems.
Some car insurance companies count driving habits as one of the main factors when determining insurance cost. They ask policyholders to install monitoring devices inside their cars for 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 capabilities 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 better understands what a user has in mind and will generate relevant results rather than focusing only on what is typed.
Such an improved search increases the probability of conversion by 3-4 times, thus increasing the shop’s sales.
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. Finally — 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 analyse data to identify trends and red flags that can lead to improved diagnosis and treatment.
Data analysis to identify patterns and trends is used in the transportation industry, in which an increase in route efficiency and predictions of potential issues are required. 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 improving accuracy 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 can make it more efficient when processing large amounts of data. Access to big data and neural networks have facilitated machine learning becoming even more beneficial.
When analysing data, the axiom ‘garbage in, garbage out’ still holds. However, modern ML algorithms are not only able to analyse incoming data but also to filter and improve results.
Duplications and inaccuracies in data are problems for a business that is striving to automate internal processes. The development of a machine learning model will improve the efficiency and quality of data input, consequently reducing the number of inaccuracies and duplications.
Data input is only a beginning. Today, NLP technology that is able to analyse text, understand its contents, and use this information to prepare reports has already been developed.
ML already works within services like Amazon and Netflix. Their users buy goods and services and then get suggestions 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 is responding 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 progress through the purchase funnel. ML helps to quickly get information about the characteristics and preferences of customers.
Observing the digital behaviour of shoppers helps to identify topics that interest them. As a result, companies can provide customers with personalised services.
Combining use of big data 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 decision-making, 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 practice, we know many more applications of 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 out the request form, and our managers will contact you.