Machine learning services
THE BENEFITS OF MACHINE LEARNING
Simply put, these are methods applied to computers to make them have human-like experiences.
We cannot say that ML is a modern invention because it was created in the 1960s, after programmers began to teach computers to perform the required tasks without a programming procedure. Specialists were trying to see if computers could self-learn based on information.
OPERATING PRINCIPLE
MACHINE LEARNING
ML works as follows:
The term "machine learning" itself was introduced by scientist Arthur Samuel. He described the process by which a machine learns without requiring programming. The program he developed could do this by learning to play checkers by itself. Famous universities also have their own opinion on the term, viz:
- Stanford. "It's a science that explains how computers work without outright programming."
- Washington. "These are algorithms that are able to recognize how to perform tasks by applying generalization of examples."
- Carnegie Mellon University. Carnegie Mellon."It is a discipline that seeks to explain how to build computer systems that improve with experience, what are the basic laws that apply to learning."
New technologies and large amounts of information have made it possible to give a fresh impetus to ML with the help of current developments. Compared to the last century, it has changed dramatically.
TERMINOLOGY
When the machine learning models are being created, It is important to use an iterative approach because they are able to self-adapt under the influence of new data. The model is tested on earlier calculations to obtain reliable repeatable solutions.
The goals of ML are to predict results based on incoming information, automate solutions to supercomplex problems, and improve the accuracy of results. In order for the above to be successfully achieved, the following components are required:
- Information. It is used as examples of solutions, statistics, various indicators, events from history, calculations, and so on. The variety of information will affect the ease of identifying patterns by the machine. IT corporations have been collecting data for years and combining it into datasets.
- Signs. These are characteristics or properties that the machine takes into account during training. To the extent that there are fewer of them, the testing will be easier. But for complex tasks, an order of magnitude more parameters will need to be taken into account, which should define the principle of transformation of inputs into outputs.
- Algorithms. This is the name of the ways in which the problem will be solved. You should use the most effective of them.
THE METHODS USED IN THE
MACHINE LEARNING
The three most common methods of using ML are:
- uncontrollable;
- controlled;
- semi-automatic.
In the latter two cases, a "teacher" is used, which is an engineer who points out the correct answers on certain objects or a training sample.
The above methods are classic. They are used in most online services. Surely you have already been able to take advantage of their activities, so it is worth considering the classics more carefully.
CONTROLLED
In this case, the teacher sets the "situation - required solution" pair for each of the precedents. The model is tested on output and input information. Regression and classification are used here to predict meaning and category.
Suppose we have data containing data such as bitcoin and ether prices. The first is the input component, the second is the output component. Applying artificial intelligence and machine learning you can build a model and predict prices.
UNCONTROLLED
Here the teacher sets up a "situation" and then the model comes up with a solution by itself, without using examples or searching for dependencies in the input information. This method uses clustering to divide by similarity, associative rules to find consistency, and dimensional reduction to find dependencies.
For example, you have data with the price of latcoin for the past year. You build a model and set it the task of forecasting prices for the current year, but only the dates are given. Then the system independently discovers patterns based on seasonality, weekends or business days, and so on.
SEMI-AUTOMATIC
In this method, the system is divided into two parts, each of which operates on the example of the previous two.
If there is information with two variables, where news about dochecoin is taken as inputs and prices are outputs, and only 50% of news and values are related to each other, it means that the partitioning is half done. Due to the appearance of certain news, the price changes accordingly. And the second part of the work is for the model to determine which of the remaining news events had an impact on the price of dogecoin.
MODERN TECHNIQUES ML
ML is not limited to classical methods, so there are also alternative methods, which have appeared quite recently:
- With reinforcement. Used in a supervised method, only in this case the engineer is replaced by an environment, such as a neural network, with which the model interacts.
- Transductive. A method based on semi-automatic. In this method, a finite training sample of precedents is set by the teacher, and the model makes predictions based on this data in relation to other data contained in the text sample.
- Active. The model independently chooses the next precedent for which a solution will be prepared.
- Dynamic. Here the teacher may or may not be used. Information comes in streams and sequences, and the model instantly makes decisions on precedents, updates predictions for the next, and learns from patterns derived from new precedents.
Machine learning engineers often use more than one method at a time, which produces predictions with higher efficiency and corrects for the errors of each individual method.
ML algorithms allow you to identify the required patterns in information, to apply them in the right way. They detect natural patterns that help to make error-free decisions and make the most accurate predictions.
Right now, the experts on machine learning more and more often turn to creating neural networks and conducting deep learning. It should be clarified that a neural network is a model with a set of artificial interconnected neurons, which resembles human nerve cells. But neurons in this case act as a functional with multiple inputs and a single output, and the links are the channels through which neurons transmit data.
THE SCOPE OF MACHINE LEARNING
It is noticeable that ML is developing at a shocking rate and its adoption is happening everywhere. Almost all IT companies have launched their own custom machine learning platforms. The rest of the corporations are applying their solutions to their services, resulting in tremendous benefits for users.
This is how mailers fight spam, and on social networks ML automatically recognizes faces and selects relevant tags. Search engines learn through machine learning about your interests and preferences, producing the right data.
With the increase in the volume of data ML has become widespread and in demand in many areas, which will be discussed below.
FINANCES
In this area, machine learning is required to analyze market data. With the help of models, companies and clients find investment prospects, identify trading trends, and select the right moments to make trades.
Banking institutions use technology to identify customers with high-risk profiles and in cyber surveillance to thwart fraud attempts.
SOCIAL MEDIA
Few people know, but it is in this area that AI is used to select the most attractive content for the user based on his or her preferences, which are determined based on the list of groups, likes, reposts made. This makes it possible to create an individual circle of interests of the user.
ELECTRONIC COMMERCE
Among the brightest examples of ML applications is the improvement of search engine capabilities. The application of technology in the field of e-commerce will enable online stores to produce relevant results for visitor queries. A self-trained search engine easily recognizes what the user requires and generates the desired results. Such optimized search allows you to increase the ability to convert to 300-400%, which will increase the volume of sales of the store.
RETAILING
Here, ML systems allow you to analyze the purchase history of products and recommend a related product. Models still work on collecting and analyzing data, using it later for:
- personalization of advertising campaigns;
- optimization value;
- product supply planning.
HEALTH CARE
In this industry, machine learning has become in demand in sensors and devices that use data to assess a patient's condition at a particular point in time. Technology helps medical experts analyze information to identify trends or red flags to optimize diagnosis and treatment.
TRANSPORT
In this industry, ML makes it possible to increase the level of efficiency of routes and predict the occurrence of possible potential problems in trucking and in the use of public transport.
MACHINE BENEFIT
BUSINESS TRAINING
With ML, huge amounts of information are analyzed and forecasting accuracy is increased. But sometimes models require additional resources and time losses.
But if you combine ML and cognitive technology, it has a significant impact on its effectiveness for the better. That is why the use of neural networks has made machine learning even more useful.
INCREASING THE ACCURACY OF INPUT
When performing information analysis, a lot of "junk" data is regularly identified, and modern technology allows us to get rid of it by successfully cleaning it.
Businesses want to automate internal processes, but the constant occurrence of inaccuracies and duplications creates a lot of difficulties. And this is where ML comes in handy, optimizing data and significantly reducing the number of duplicates and inaccuracies.
But data entry is only the tip. A system has already been created that processes natural language and can analyze test data, perceiving its content and using this information to prepare reports.
IMPROVING THE QUALITY OF RECOMMENDATIONS
ML has already been implemented in services such as Amazon and Netflix. After users buy certain products or services, they subsequently receive suggestions for a number of other interesting products. As the number of purchases increases, the system's understanding of their preferences increases, and it begins to put forward more appropriate propositions that are comparable to advice from friends.
You don't have to have a company as big as Netflix to achieve these results with algorithms. All the same, ML will contribute to the delivery of information about your products to potential customers.
You can name a couple of ways to improve the quality of recommendations:
- Offer services that should interest the existing clientele. Machines will learn about their interests and adjust to the consumer.
Analyze and segment the audience through specific models, which will then pass the filtered data to other models or chatbots, which will send promotional offers to prospective customers.
INCREASED CUSTOMER SERVICE
To achieve the required business results, companies try to meet the growing expectations of the customer. Customers regularly have questions about the products they are willing to pay for. Of course, they expect a quick answer, which non-ML companies can't always cope with.
To process initial messages, support services use chat robots with AI, which makes it appear as if a live employee is responding to the customer. This technology is also actively used in social networks.
PERSONALIZATION AT A PARTICULAR MOMENT
It is known that the effectiveness of marketing campaigns depends on how relevant and timely they are. If you send clients with ML messages at the right time, they will be able to move through the sales funnel, and the company will receive data on their preferences and characteristics.
By observing consumer behavior, it is possible to identify topics in which their interest is particularly high. As a result, companies are able to offer the customer a personalized service.
IDENTIFYING AUDIENCES AND TARGET MARKETS
The use of machine learning will assist in determining the target market and linking user profiles to the products they are interested in. It will also be possible to select clients for targeting in advertising campaigns.
APPLICATION OF MACHINE LEARNING
FOR PROFIT
All of the uses of ML that have been listed above are freely available not only to large-scale IT corporations, but also to other companies that are able to benefit from this AI. They will be able to experience this as a result of improved decision efficiency, performance results. They will then provide top-tier services to the consumer.
THE COST OF MACHINE LEARNING SERVICES FOR BUSINESS
| DEVELOPMENT | CONTACT |
|---|---|
| Machine learning services based on AI | from 1000 rubles/hour |
* The indicated prices are not a public offer and are subject to change. The cost is measured based on the time spent and the functionality that is required to be implemented in the system, on average, our work costs the customer from 1000 rubles/hour.
What do our clients think of our work?
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Advantages of our company
We thoroughly study the problems, consider them from all sides and quickly collect the necessary information. Then we discuss the specifics of each problem with clients and together with them make a work plan, taking into account all the wishes of customers.
There are more than 20 specialists in our team. Most of the team members have higher technical education and have been working in IT for more than three years. And we'll always answer if you have questions or need help.
We help to solve any problem related to IT: we develop programs and applications, issue tokens and develop blockchain networks. Our clients are companies, offices, small and medium-sized businesses, and financial organizations.
We develop programs in popular programming languages: Python, C++, JavaScript. That's why we can create universal solutions for any of your tasks: whether it's a program for the office, a database, an application or a blockchain network.
We are engaged in comprehensive support of ready-made products. We can extend the functionality of ready-made programs and applications, if necessary. We can also help if you need to add something to your ready-made program.
After developing a program or creating a blockchain network, we help you understand how to effectively manage the product. We support customers and help them achieve their goals with our applications.
We take a professional approach to problem solving. We create tokens and help clients bring them to market, help you create your own blockchain for your tokens. We can help if you need to configure the management of tokens you already have.
We turn any of your IT ideas into a finished working project. We have released a large number of programs and applications, provided support to a wide variety of clients and earned their trust. They turn to us for problem solving.
We develop our own blockchain networks for companies. Our programmers have excellent knowledge of Python, C++, JavaScript and can work with any storage systems. We can create a secure network or application to store any data.
We thoroughly study the problems, consider them from all sides and quickly collect the necessary information. Then we discuss the specifics of each problem with our clients, taking into account all the wishes of customers.
There are more than 20 specialists in our team. Most of the team members have higher technical education and have been working in IT for more than three years. And we'll always answer if you have questions or need help.
We develop programs in popular programming languages: Python, C++, JavaScript. That's why we can create universal solutions for any of your tasks: whether it's a program for the office, a database, an application or a blockchain network.
We help to solve any problem related to IT: we develop programs and applications, issue tokens and develop blockchain networks. Our clients are companies, offices, small and medium-sized businesses, and financial organizations.
We are engaged in comprehensive support of ready-made products. We can extend the functionality of ready-made programs and applications, if necessary. We can also help if you need to add something to your ready-made program.
After developing a program or creating a blockchain network, we help you understand how to effectively manage the product. We support customers and help them achieve their goals with our applications.
We develop our own blockchain networks for companies. Our programmers have excellent knowledge of Python, C++, JavaScript and can work with any storage systems. We can create a secure network or storage application.
We turn any of your IT ideas into a finished working project. We have released a large number of programs and applications, provided support to a wide variety of clients and earned their trust. They turn to us for problem solving.
We thoroughly study the problems, consider them from all sides and quickly collect the necessary information. Then we discuss the specifics of each problem with clients and together with them make a work plan, taking into account all the wishes of customers.
There are more than 20 specialists in our team. Most of the team members have higher technical education and have been working in IT for more than three years. And we'll always answer if you have questions or need help.
We help to solve any problem related to IT: we develop programs and applications, issue tokens and develop blockchain networks. Our clients are companies, offices, small and medium-sized businesses, and financial organizations.
After developing a program or creating a blockchain network, we help you understand how to effectively manage the product. We support customers and help them achieve their goals with our applications.
We develop programs in popular programming languages: Python, C++, JavaScript. That's why we can create universal solutions for any of your tasks: whether it's a program for the office, a database, an application or a blockchain network.
We take a professional approach to problem solving. We create tokens and help clients bring them to market, help you create your own blockchain for your tokens. We can help if you need to configure the management of tokens you already have.
We are engaged in comprehensive support of ready-made products. We can extend the functionality of ready-made programs and applications, if necessary. We can also help if you need to add something to your ready-made program.
We develop our own blockchain networks for companies. Our programmers have excellent knowledge of Python, C++, JavaScript and can work with any storage systems. We can create a secure network or application to store any data.
We turn any of your IT ideas into a finished working project. We have released a large number of programs and applications, provided support to a wide variety of clients and earned their trust. They turn to us for problem solving.