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Machine Learning and Data Science in Data Processing

As previous generations mark, today’s people’s life is in their gadgets. We all got used to the fast rhythm of life. And Google searcher is a part of our daily routine.

One-click solutions are our convenient and simplified way of performing tasks and problem-solving. We can buy a product in one click; find the direction if we get lost; etc.)

No wonder it is simple only on the surface. There is a much more complex structure hidden under the simplicity of trivial applications. To uncover this basis, we need to look behind the scene. And, of course, we will need some guides. These are data science and machine learning techniques which work in cooperation.

What is Machine Learning?

This system is taught by people. As a result, the system can differentiate the data by itself. It can perform definite algorithms that the machine has learned.

-algorithms = result of machine training = made to achieve a certain aim= to deal with data without human help.

Machine learning consists of algorithms that comprise the model for data science. These algorithms are necessary for Machine learning modeling. The model includes data source, data collection and management, and preparation for further training.

1) People perform a unique role in Machine learning. They provide Machines with new data. And this data works as a source of the basis for Machine learning ( like water for a watering can).

2) Machine learning training ceases the complex nature to destroy complexes of Data Science. There are specific algorithms that a machine has to perform to be able to read the data behavior and automatically learn from it. Then there comes testing for prediction.

What is Data Science?

Data science has to deal with data processing. Which involves all data handling forms: evaluating, collecting, etc. It aims to retrieve necessary information from this data.

Data Science uses the data source as a tool for influencing companies (business). There is a certain principle.

Attending multiple websites on the internet (searching for something), people supply the database with some information. As a rule, stepping into the environment of the Internet users leaves many tracks in view of specific data (like the information about requirements). It helps in the selection of the necessary information.

The Data Science formula= applications of machine learning+ tools (algorithms) + techniques of data processing.

So the first thing that we should state is how Machine Learning is applied in Data Science.

There are several forms of machine learning applications. The most famous form is following people’s interests and preferences to suggest the products they want and need (this is how search engines function). This form of application is called prediction. Machine learning’s distinctive feature is foreseeing people’s choices and focusing on personal preferences. (Today, one person may think of going to a hairdresser, and tomorrow this person will see the hairstyles/hairdressers in the recommendations on social networks).

Thus, some more applied forms of Machine Learning are:

  1. Recognition:

⁃ Face recognition (face ID; smartphone apps: photo/video editors; age checkers (checking your age by photo))

⁃ Photo/ image recognition (smartphone apps; searching for similar items by photo in internet shops)

⁃ Voice/ speech (language learning apps; search engines usage)

  1. Navigation

⁃ Maps

⁃ traffic jams viewing

⁃ Apps for viewing public transport paths

The data is a powerful unit that is mandatory in creating something new on the internet (for example: making an offer to potential customers). That is why this peculiarity also helps modern companies to get to your mind and what is in there. Moreover, such characteristic enables them to understand which sort of apps you would like to use and in what field.

However, there are still some issues in Machine learning functioning, which make Machine learning imperfect.

Challenges of ML

Dealing with Machine Learning, we often face some challenges (in gathering training data). There is a list of the main problematic issues:

  1. Accessibility of data. It seems to be difficult to get access to the huge amount of data and so much knowledge for Machine training. Furthermore, it is time-consuming and not cheap. (the helper-a tool that helps to deal with unlabeled data and process it in a faster way (Transfer Learning+ Self-Supervised Learning))
  2. Non-flexible nature. According to the differences in geographical aspect: difference in seasons; the countries’ differences in general (available in one place and non-available in other places)

The only practical way out here is constantly updating.

  1. Large scale but low speed. The huge size of the whole model affects the time needed for implementation. (An obvious solution is a technique that reduces the general size and makes the processing unit faster)

Conclusion

To complete a summary of this article, we can address the previous example with a watering can.

Machine learning, following its functions, can perform the role of a watering can for Data science.

Data science can be imagined as a plant that constantly grows (since it is trained). Despite this constant growth (more steps-more data), there is one more specific feature. The root system of the so-called plant becomes bigger either (more applications and requirements).

These roots involve the complexity of dealing with data because of challenges. Nevertheless, we can count on the state of constant development and be sure that these problems will get managed over the course o time.

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