Machine learning has made a breakthrough in the last few years, making some routine human duties easier. However, not everyone fully understands what it is and how it works, although they encounter the result of the process every day. So if you’re in university now, but you understand that your future profession doesn’t fit you, try mastering this one. When I was in the same situation, I’ve been asking to help write my essay because I simply had no time to do that by myself. You can It may turn out to be quite promising and interesting, especially if you have the above-mentioned qualities.
The work of some applications and programs in gadgets and devices is established thanks to this technology. Siri and Alexa are prime examples.
In the future, machine learning will only evolve, opening up new opportunities for humanity. This is a huge prospect, and it would be foolish not to jump into this circle by gaining some knowledge. It is possible to master this profession in a few years and start working even earlier. We will tell you below what there is to learn, what tools to use, and where to go to study.
Major representatives of the global IT industry, as well as renowned research companies, interpret the essence of ML as follows:
- “The practical use of algorithms to analyze data, study it, and then predict a phenomenon” (NVIDIA).
- “The science of how to teach computers to function without explicit programming” (Stanford University).
- “Technology based on algorithms capable of learning from embedded data without the aid of programming tools” (McKinsey & Co.).
- “Algorithms capable of independently selecting a method for solving important problems by generalizing from examples embedded in the system” (University of Washington).
- “A field whose function is to find ways to create computer systems capable of self-learning and self-improving as experience is gained, and to find the fundamental patterns by which all learning processes work” (Carnegie Mellon University).
In general, we can say about machine learning that it is part of the science of artificial intelligence, and neural networks are in turn one of the varieties of ML.
Tasks solved by machine learning today
The range of tasks that can be solved using artificial intelligence is very wide: the analysis of information and its memorization, the prediction of processes, the reproduction of ready-made models, and the selection of the most appropriate of them.
The systems carrying out very large volumes of calculations are of special value. This includes, for example, scoring in banks that determine the credit rating of customers. Other areas of application include analytical calculations in marketing and statistics, business planning, research in demography, detection of fraudulent resources, and fake news.
Modern research in the field of artificial intelligence today is aimed at the development of deep learning systems with increased efficiency and without loss of performance. In addition, one of the key objectives is to minimize the amount of input data and the time required to process it. There is a particular demand for such systems in personalized healthcare, robotics, and emotion research.
2 types of machine learning
All types of machine learning can be of two types:
- The Inductive (precedent) type is based on empirical patterns in the raw data.
- Deductive Type takes expert knowledge, formalizes it, and transfers it to a digital database.
The latter type is part of expert systems, so the concept of machine learning most often refers to learning from precedents (a training sample). This sample is a set of matching inputs and outputs. There is no unambiguous pattern between machine learning inputs and their results. As an example, let’s take the weather forecast. What kind of weather should we expect tomorrow if the past week has been frosty, windless, and sunny?
Additional parameters will be needed for forecasting here: geographic coordinates, the terrain of the area, current climatic features, etc. Next, an algorithm is created that provides a sufficiently accurate result, regardless of what is fed to the input.
An estimated quality function is used to adjust the accuracy of the output. The result is formed empirically, taking into account the accumulated experience. In the process of learning, the system must be able to generalize the input data, reacting adequately when this data goes beyond the training sample. In practice, input information is inaccurate, incomplete, or heterogeneous.
For this reason, machine learning systems operate on many different methods. A common approach is to solve problems through analysis by analogy and similar precedents. This technique is called Case-Based Reasoning (CBR).
Next, let’s look at three methods of machine learning: with a teacher, without a teacher, and deep.
Machine Learning Specialists
Data Scientists work with artificial intelligence. In the course of their work, these specialists comprehensively study data, trying to find some dependencies and connections in its interaction that are useful for potential customers
In the current context of the coronavirus pandemic, machine learning and data analysis are used to recognize individuals who violate health and safety regulations. To do this, a camera captures and analyzes the faces of passing people beforehand. The offenders are then identified in this stream. Automatic recognition without the direct involvement of specialists requires training in a video surveillance system with a built-in algorithm.
Here is a simpler example. There is a form on a website where the user needs to specify, among other things, name and gender. The system can be trained to automatically determine gender based on the name that the visitor enters.
For training, for example, a large database of social network users is used and it is determined that the vast majority of users with the name Sergei are men. This pattern is then incorporated into the algorithm.
The most difficult thing for experts in machine learning – the use of logic in reasoning and continuous attention to detail. On the technical side, however, this profession is quite easy to master.
Artificial Intelligence professionals will be aided by their natural meticulousness, assiduity, and constant desire to understand cause and effect. This job will suit people with a technical mindset, who are persistent in their goals and are willing to work hard to find the truth. A bonus will be a strong entrepreneurial mindset.
