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Machine Learning for Business
We develop and implement in the information systems machine learning modules for solving business tasks.
The algorithms of machine learning are based on dynamic mathematical models, repeatedly apply the model to time-varying real input data and thus adapt it. Therefore, the calculations are based on the most up-to-date information - and, therefore, the tasks are more effectively solved.
The problems of classification, clustering, approximation, order relations.
- Feature Engineering
Natural Language Processing
Techniques for automatic word processing.
- Apache OpenNLP
- Elastic Search
- IBM Watson
- Google Cloud Natural Language
Image recognition algorithms depend on the task.
- Neural Networks
- Image Net
Create chat bots both from scratch and using tools.
- Microsoft Bot Framework
- Facebook Bot Platform
What is Machine Learning
The very concept, went from the principle of neural networks - artificial intelligence. Currently, a number of giants already use neural networks in their work, some of us are known for daily use, and some are still beyond the bounds of uncertainty.
- Neuron (biology) is a nerve cell that is capable of receiving, storing and giving information by electrical and chemical signals.
- Neuron (Neuronet) is a node or "combat" unit, which is capable of taking, storing, and giving information. There is a neuron bundle in Neuronet, which can receive information from multiple channels, and be transmitted from one channel.
The interconnection of neurons, this mechanism works dependently and independently of the entire network. If the connection is dependent, then the neurons in the neural network receive signals (information) and try to correlate it with each other, giving out one answer in the end - this is called machine learning algorithm.
Imagine a meeting at work. The cabinet includes five people with the task of discussing new projects. Each of them is an integral being (essence) having experience, views, beliefs, ideology. Everyone pre-pondered the issue of future discussion and came up with some amount of information. We have several sources of information at the entrance - several channels. Discussion (brainstorming, for example) creates a response vector that converts to one conclusion. Those. after discussion, one decision is made, which is read out to the project manager as a recommendation to action.
In the end, we can imagine that all five of our example, during the discussion, having taken into account all the information channels, and at least five of them, outputs one answer based on these five channels.
Machine learning, in this example can be called, the ability of these employees to compare different information, which is owned by all in different aspects and to make one decision.
Machine Learning, Marketing and Business
For the first time about machine learning program began talking about 50 years ago, when in the course of research on the nerve networks of a living organism, the idea came to imitate this process, which seems quite logical.
We use a database that we carefully collect throughout our life - our experience, to work with this database we use the usual behavioral algorithms inherent in us since childhood. The process itself is not complicated in understanding and is familiar to us in full measure (in any case, we want to believe this). We can simulate this process by working with databases, which are increasing every day.
What tasks can machine learning solve in business?
In my opinion, you can solve almost any problem with the help of this technology. The focus is precisely to provide a solution for those zones that require the greatest effort and time, this is all that is connected with the analysis. But not always at first glance you can understand that it is in the analysis of data.
Not only the form of feedback, but also the display of content on the landing page, which fully corresponds to the user's request. In order not to load attention with constant flickering forms of feedback and not give the user an infinite amount of content, we can use machine learning results to show only those content blocks that are most relevant to the query. So the machine algorithm can learn to recognize areas of interest and give only answers to a question that relates directly to the interest, but to all available information. That will significantly increase the conversion of the advertising company.
We can train the mechanism to understand the user, see the sequence of his actions and respond based on the analysis of this sequence. In this way, machine learning approach will not only understand the client’s preferences, but also will guess his expectations. Here in the analysis gets not only the history of behavior in the product itself, but also beyond. For example, you can analyze social signals (posts, mentions, etc.).
If we know who our client is, we have collected enough information, analyzed the behavior and understood our own shortcomings (which the client lacks), in the end we have tactical data that, with the help of the learning algorithm, will give us a strategy of behavior in the niche, aimed at reaching out and interacting with the end user.
Attention to Quality
We can "give" the desired product to the client, we will use the result described above and concentrate not on selling, but on the client's self-perception at the moment of finding the goods. Imagine that you are looking for an ancient artifact (which is how sometimes my search for banal goods looks like). The artifact is so rare that the search queries for results have not given up, your emotional sweats in Facebook, too. And you almost resigned to the failure of the search. Until you "knock on the door" this very artifact itself, sincerely and broadly smiling. As you understand, the task here is not in principle a sale, but to find this very door in a multi-storey building. Instead of tapping to all neighbors, we can choose the door that will open for us exactly.
Where is it Already Used?
- Google Translate - uses machine learning since 2011, whose task is to find not only accurate translations of words, but also the analysis of word combinations. If you try to remember the quality of translations five years ago and compare it with today's results, you will see a difference and it is in my opinion essential.
- Prisma - application for processing photos in the style of this or that artist. Uses this tech, which affects the quality of the result of processing. If you use this application, you can track the learning stages yourself.
- Google uses this technology not only in Translate, but also in search algorithms that perform an online query analysis.
- Yandex - also uses this tech to rank data in search results.
Machine learning is more a set of algorithms that are able to build a relationship between the facts and derive some sort of output based on the input data. Those. these are the steps - data collection, data analysis, output. In other words, we need information channels that are available for most of the operating businesses.
How Machine Learning Can Affect Business
The question of how this advanced technology can change a business is very abstract, but at the same time, we can be guided by a database of the whole world and pay attention to what we are certain of and this is one simple truth:
What was still 10 - 20 years ago on the verge of fiction, today it is quite fit in everyday life. Look at mobile phones, Siri or the same Google Translate. Today it is difficult for me to imagine a person without a mobile phone or studying a foreign language and not a translator from Google.
Anyway, in the future, we will be able to see a lot of things that seem unreal today and it seems to me that in a short time machine learning will have its place in business as something permanent and familiar, otherwise we will not move along with the flow of time.
Getting Information about the Niche
You can get complete information about the niche, about products and the audience. Form a strategy and decide on which niche zones to produce specific proposals. This can be done with the help of machine learning, not only minimizing the risks, but also much faster and more efficiently.
We are constantly risking something, somehow forced to make a decision and very often these decisions are influenced by emotions, personal interest, etc. Machine learning can make decisions by analyzing and minimizing possible risks.
Efficiency of work is the key to the success of any business. Who sets the tasks for your employees, who allocates labor potential, who makes your time effective? Assign tasks and allocate responsibilities will become much easier, and implementation will rise to a new level, for a number of reasons:
- The system does not have emotions
- The calculation is based on a mathematical formula
- All the possibilities are translated into numbers
- Responsibilities and tasks are distributed based on the analysis of the employee's potential and minimization of risks
- You do not need to believe in a word, you can start a process that clears the most effective ways