Data Analytics: Machine Learning

Machine learning is a collection of mathematical techniques that can provide solutions to specific problems using general algorithms

Just a few years ago, the ideas of big data and cloud services revolutionized the IT business and then the real companies in the industry. Executives and businesspeople from a wide variety of industries quickly realized the opportunities offered by big data analytics, the value of their knowledge, and how the cloud infrastructure can make the most of this information. Machine learning technology is a logical development of these concepts.

Machine learning is a set of mathematical methods that can solve specific problems using general algorithms that are not written specifically for the given problem.

Simply put, machine learning is one way to implement big data analytics. Using this technology, the computer can learn to identify certain patterns, calculate how it will perform certain actions - buy or sell securities, segment potentially high-yield customers, or identify faulty products on a conveyor belt.

The important thing is that machine learning algorithms are completely universal and are completely not tied to any company or industry. Track customers and determine their tendency to leave, analyze traffic in the city and the likely places of congestion, the likelihood of a stove failure in the steel industry, etc. - all these problems can be solved using the same mathematical apparatus.

Machine learning focuses on developing computer programs and algorithms that learn to grow and adapt as new data becomes available. This process is not like a data mining process. Both systems check the data provided to them or collect data in search of patterns. However, in mining applications, the data is human-readable, while machine learning algorithms use this data to find patterns and change programming actions accordingly.

As a rule, data analysis works with information tables, independently performing a whole set of operations: data collection, preparation of data for analysis (sampling, cleaning, sorting), search for patterns in sets of information, data visualization to quickly understand current results and future trends, formulate hypotheses to improve specific business metrics by changing other metrics.

All the tasks presented are needed to achieve the main goal of data analytics - extracting valuable business information from information tables for making optimal management decisions.

In some companies, the data analyst is also responsible for data modeling, that is, developing and testing machine learning models. However, in most cases, the data scientist is responsible for machine learning. In a more detailed division of labor, machine learning is handled by a separate specialist.

Machine learning methods:

  • Supervised learning. The machine receives inputs and their preferred outputs, objects called a "teacher," and the goal is to learn a general rule that matches inputs to outputs. These algorithms apply whatever they have learned previously to any new data.

  • Learning without a teacher. Labels / tags or explanations are not given to the learning algorithm regarding the input data, and it only remains to find the structure there. Used to discover patterns hidden in data. These algorithms can extract their own inferences or inferences from the data in the dataset.

  • Learn in action. The software interacts with a changing environment in which it must perform a specific task (such as driving a vehicle) without telling whether it is approaching its destination or learning to play a game by playing against someone.

  • Semi-Guided Machine Learning. The subject "teacher" gives these machines with some malfunctions, there are no exits.

Machine learning software is widely available and there are many options for organizations looking to build capacity in this area. Consider the following requirements when evaluating machine learning: speed; evaluation time; model accuracy; easy integration; flexible deployment; ease of use; visualization.

Machine learning primarily uses a range or spectrum-based method to optimize many parameters. It is impractical for humans to manually select such optimal settings. For example, speaker recognition by tone, tone, and amplitude. There is no guarantee that machine learning will work in all cases. Sometimes machine learning fails, which requires understanding the problem to apply the correct algorithm.

These training algorithms require a lot of training data. It would be very difficult to work with such large amounts of data or collect such data. Nevertheless, with the passage of time, more and more diverse and accessible algorithms for working with data appear, which makes it possible to obtain accurate and fast results. Thus, machine learning is rapidly becoming a very important and widely adopted part of our daily life.

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