Machine Learning Algorithms: How This Technology Can Be Used

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To create the machine learning methods, the elements of mathematical statistics, optimization methods, numerical methods, as well as probability theory, graph theory, and genetic algorithms are used. It is because of the need for deep mathematical knowledge that the technology of machine learning is considered difficult for developers. However, at the same time, this technology is one of the most popular areas of Computer Science. First of all, thanks to a very wide scope of technology and truly unique opportunities.

machine learning methods

Machine learning in applications: how to implement and what benefits it provides

Today, machine learning is a very serious area of computer technology development, which is considered one of the steps to creating an artificial intelligence. Large companies (Google, for one) have already began implementing the elements of this technology into their products, setting a new trend in the IT business.

Although creating a machine learning product is a difficult task, a ready-made automated data processing algorithm (which in fact is a self-learning technology) can greatly facilitate the work on the project. After all, in the future developers instead of writing code just need to enter new data into the algorithm, which will independently build logical chains to solve certain tasks.

Building a Taxi Booking App like Uber, Gett, or Lyft

Another problem is the search for the best language for machine learning software. Disputes about which language is better for the creation of machine learning algorithms are one of the most heated in the developer community. In general, the controversy has divided developers into two camps:

– Some argue that only “scientific” languages (used mainly for creating applications and products for science) like R and MATLAB/Octave are able to cope with the task:

– Others believe that simpler and more common Python and Java contain a sufficient number of additional libraries suitable for the creation of simple machine learning algorithm.

ML methods

The possibilities of implementing self-learning algorithms in applications are almost unlimited in terms of the scope. However, there are two main areas to which the technology is suited best: multimedia/image recognition and big data processing. Anyway, regardless of the direction, applications with machine learning are able to perform unique and complex intellectual tasks.


Learn more about the top machine learning as a service companies to understand a competitive edge in being the first to adopt ML solutions.

Machine learning apps and the scope of their application

By applications, we usually mean simple and useful programs, popular because they are easily applicable to everyday life. Such are entertainment/game apps or products that help ordinary people and companies quickly solve daily routine issues. Is it possible to create functional machine learning applications, useful in everyday life? The answer is – yes, definitely. Not only are the machine learning technologies helpful but also find a lot of use in applications lately.

– Image and video recognition. Such functions are already widely used in various applications: from programs that help the police identify speed limit violators to chat applications with face recognition routines. There are also apps that recognize the sex and age of a person from a photograph, which might be applied to many areas, including medicine and security. Intelligent fingerprint or retina recognition can be used in security systems.

ML apps scope– Text recognition. Most of the documents and data nowadays are stored, sent and processed in digital form. Thus, text recognition greatly simplifies the life of the user and saves their time. Why use the machine learning and applications to solve such a mundane, at first glance, task? Simple: any text has a number of unique characteristics, including font, size and letter spacing. Only self-learning algorithms can perform this task as qualitatively as humans do.

– Voice recognition. Voice recognition is already used by many applications, including Google Speech Recognition (the infamous “Ok, Google”). Such tools today are far from ideal, but the implementation of machine learning algorithms makes it possible to create applications that will improve the efficiency of their work in the process. In future, this would result in applications that can accurately translate live speech into text or commands, which will certainly prove useful in many spheres of human activity.

Not to mention that such self-learned algorithms help to identify the new and previously uninvented solutions to known problems, thus advancing the science. We say machine learning meaning the cutting edge of the innovative technologies.

– Sensor data analysis. Most frequently, this technology is employed by fitness tracker apps. Measuring the distance traveled or monitoring the heart rate during training is just the beginning.

Using self-learning algorithms in conjunction with sensors, one can create medicine applications for diagnostics and monitoring of the specific diseases patients. Technologies that can monitor the blood sugar level in diabetic patients or predict epilepsy seizures beforehand will be in great demand in nearest future.

machine learning algorithm future– Navigation and driving control systems. Navigation apps can significantly expand their capabilities upon introduction of automated learning algorithms. Image recognition not only can help to navigate unfamiliar areas and cities, but also provide security, for example, when combining the car navigator with the DVR camera. Alternatively, imagine parktronic and cruise control systems that adapt to the owner driving style.

– Identifying trends based on the Big Data analysis. This capability of the machine learning technology applies to many areas that require analytics: from business to sports betting. A rapid intelligent analysis of extreme disparate data volumes followed by the structuring and calculation of further development probabilities can turn the tides in world of trade completely. Applications that analyze the customer behavior in the store or track the world market processes are the typical instances.

– Customization. One of the rules of trade says that the more personal is the appeal to a potential client, the higher the probability of a sale. Today, there are a lot of programs that when studying user requests on various sites (whether online stores or sites for movie lovers), try to offer each product that may interest them. The implementation of machine learning into this field of business allows for analysis that is more thorough and, as a result, a more personal approach to each client, which can significantly increase the number of sales.

Where Can the Machine Learning be Applied? Machine learning algorithms today is a rapidly developing field of computer science, which in the future promises to become very common. The possibilities of technology are truly extensive with a self-conscious artificial intelligence being one of the opportune prospects. Self-learning smart algorithms can already be incorporated into existing software: medical institutions, trade companies, security services (including public ones), and many others. In addition, the machine learning can be used in such popular spheres as the Internet of things and smart homes. Although intelligent technologies are difficult to develop, they can, nevertheless, simplify the lives of both ordinary people and users with special requirements, be it company owners or handicapped persons. Is it worth using the functional machine learning in applications? Sure enough, if you strive to make your application successful. One does not need to be a professional analyst to understand that the future belongs to machine learning technologies.  

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