Machine learning (ML) is a leading form of artificial intelligence used by modern businesses. If a company is not yet using ML, it will probably assess its potential soon, and AI will become the main engine of the IT strategy of many enterprises. After all, artificial intelligence is already playing a massive role in transforming the development of the IT industry: customers are paying more attention to intelligent applications to develop their business using AI.
Table of Content
- Machine Learning: Principles and Objectives
- How It Works: Types of Machine Learning
- Machine Learning Tools
- Benefits of Machine Learning for Industry
- Conclusion
Machine Learning: Principles and Objectives
There are three equally important components at the heart of machine learning:
- Data: Collected in all sorts of ways. The more data, the more effective machine learning and the more accurate the future result.
- Signs: Determine what parameters machine learning is based on.
- Algorithm: Choosing a machine learning method (assuming you have good data) will affect the finished model's accuracy, speed, and size.
The existence and development of machine learning were based on three main principles:
- Innovativeness: ML opportunities open up new prospects for the development and growth of almost all sectors of the economy.
- Specificity: machine learning is used to implement and develop new products exclusively by people who understand IT technologies.
- Simplicity: products sold using machine learning technologies become understandable even for schoolchildren and elderly people.
The problems that machine learning can solve directly determine the benefits for business and the possibilities for solving social problems by states of different countries. The main tasks include:
- Regression: Provides a forecast based on a selection of objects with different characteristics. Based on the data analysis result, the output is a number or a numeric vector.
- Classification: Identifies categories of objects based on the available parameters. Continues the tradition of machine vision, so you can often find the term "pattern recognition" for example, identifying wanted people from a photo or based on a verbal description of their appearance. <
- Clustering: Divides data into similar categories based on a unifying feature. For example, space objects are clustered by distance, size, type, and other characteristics.
- Identification: Separates the data with the specified parameters from the rest of the data array.
- Forecasting: It works with volumes of data for a certain period and predicts, based on the analysis, their value after a given time. An example is a weather forecast.
- Extraction of Knowledge: Explores the relationship between several indicators of the same phenomenon or event. For example, it finds patterns in the interaction of exchange indicators.
As you can see, the range of machine learning tasks is vast, which confirms its promise for use by both commercial enterprises and social projects.
How It Works: Types of Machine Learning
For ease of perception, types of machine learning are commonly subcategorized into 3:
Supervised Learning
This type is as similar as possible to the process of cognition of the surrounding world by a child, only an algorithm acts in the role of a toddler. The data prepared for analysis initially contains the correct answer, so the purpose of the algorithm is not to answer but to understand "Why exactly?" by identifying relationships. The result is the ability to build correct forecasts and models.
Learning Without A Teacher
For this type of learning, the key concept is a pattern - when processing large amounts of data, the algorithm must first independently identify patterns. At the next stage, based on the identified patterns, the machine interprets and systematizes the data.
Reinforced Training
The principles of reinforcement learning are borrowed from psychological experiments: the machine tries to find the optimal actions to take, being in a set of different scenarios. These actions can have short-term and long-term consequences, and the algorithm must detect these connections.
Machine Learning Tools
Machine learning tools are used in the following stages:
- Collection and preparation of data
- Building a model
- Training and deployment of applications
For each of these stages, specialized platforms are used. They differ in the programming language (Python, Cython, C, C ++, CUDA, Java), operating systems (Linux, Mac OS, Windows) and what tasks can be solved with their help.
There are several dozen Machine Learning tools on the market today:
- TensorFlow
- Shogun
- Keras.io
- Rapid Miner
- Google Cloud ML Engine
- Amazon Machine Learning (AML)
- Accord.NET
- Apache Mahout
- Microsoft Azure ML
Benefits of Machine Learning for Industries
1) Errors Are Decreased
From a mistake made, machine learning records the variables and, in the future, allows it not to be repeated. In addition, you can implement preventive actions. The robustness of the system depends mainly on how long it has been integrated into the process.
2) Security Against Fraud and Cyber Attacks
This is one of the fundamental aspects of machine learning. Artificial intelligence may be able to detect illegitimate transactions based on a pattern of how these movements work. In addition, you will be prepared for a cyber attack based on previous experience.
3) Automatic Learning
The machine will know what processes should and can be carried out without human intervention, from which it will perfect them and even expand the number of tasks to be carried out.
4) Personalized Marketing
This system is capable of analyzing customer preferences and, in the future offer them customized products automatically. With this, customer service is improved and loyalty is promoted.
5) Productivity Planning
This works by collecting data at different production levels, analyzing it, and determining improvement and optimization strategies. For example, it will be possible to know if the machinery or equipment will accept the workload with machine learning. Even the possible delays that could be due to any failure.
6) Collection of Information and Analysis In Real-time
This system allows evaluating the quality of the product in each of the phases so that, if necessary, prudent changes can be implemented.
Conclusion
We have mentioned some of the essential concepts of ML. The potentials of ML are applicable in almost all industries and they are very helpful in product planning and personalized marketing. Contact us when you finally decide to develop an ML solution for your establishment.
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