Definition and importance of machine learning vs a

Machine learning vs. artificial intelligence: definition and importance

, sometimes called computational intelligence, has broken through some technical barriers in recent years, and has made significant progress in the fields of robotics, machine translation, social networking, e-commerce, and even medicine and health care. It is a field of artificial intelligence. Its goal is to develop learning computing technology and build a system that can automatically acquire knowledge

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learning system is a computer program that makes decisions through the experience accumulated by successfully solving past problems. Although the application time is not long, there are many different learning algorithms. This field is one of the hottest fields in the computing field, and some new technologies and algorithms are published regularly

vs. artificial intelligence

many people believe that machine learning and artificial intelligence have the same meaning, but this is not very accurate. There are several definitions of artificial intelligence, including the broad concept of machine learning. A widely accepted definition is that AI consists of computer systems that rely on human behavior to solve problems. In other words, technology enables computers to "think" like humans to perform tasks

human beings can analyze data, find patterns or trends, analyze them more wisely, and then use conclusions to make decisions. In a sense, AI follows the same principle. Usually, the more tasks people complete, the more skilled they are. This is the result of learning ability. It is a kind of training for people to often repeat or implement relevant procedures. A similar thing will happen in AI systems: data publicly obtained or recorded on a dedicated platform is used as training for AI algorithms

how is the training completed? There are several algorithms for this purpose. It all depends on the applications and the organization or people behind them. Here, the most important thing is to know that machine learning is meaningful at this point

what is a machine learning metering pump that ensures very accurate extrusion of melt into the machine head at constant pressure

machine learning is also a concept with many definitions, but at its core, machine learning is a system that can modify its behavior independently according to its own experience, with little human interference. This behavior modification basically includes establishing logical rules to improve the performance of tasks or make decisions that are most suitable for the scenario according to the application. These rules are generated from the pattern recognition in the analysis data after 5 (7) experiments of each material

for example, if a person types the word "brave" in a search engine, the service needs to analyze a series of parameters to decide whether to display results similar to anger or courage, which may have two meanings. Among the many parameters available are user search history: for example, if you are looking for "brave" a few minutes before, the second meaning is most likely to appear. This is a very simple example, but it illustrates some important aspects of machine learning

importantly, the system must analyze according to the experience of a lot of data into the mold teacher Fu. This is a standard that searchers must give up, because they have received millions of visits, so this is a training standard

another aspect is continuous data input, which is conducive to identifying new standards. Assuming that the word "brave" becomes slang related to cultural movements, through machine learning, search engines will be able to identify patterns pointing to the new meaning of the term, and after a period of time, they will be able to consider it in search results

there are several methods of machine learning. A well-known method is called "" in which a large amount of data comes from multilayer artificial neural networks. These algorithms are inspired by the brain neuron structure to solve complex problems, such as object recognition in images

examples of machine learning

the use of machine learning is evolving into a variety of applications, and many of the technical resources people have today are based on artificial intelligence and machine learning

· autonomous database -

with machine learning, autonomous database processes several tasks previously performed by managers (DBAs), allowing these professionals to handle other activities, thereby reducing the risk of unavailability of applications due to human errors

· crack down on fraud in the payment system - various credit card frauds and attempts at other payment methods will occur every second. Machine learning allows anti fraud systems to identify most of them before they succeed

· text translation - translation must consider scenes, regional expressions, and other parameters. Due to machine learning, automatic translation is becoming more and more accurate

· content recommendation - video and audio streaming platforms use machine learning to analyze the history of content viewed or rejected by users in order to provide them with suggestions that meet their wishes

· marketing and sales - analyze the purchase history based on the previous use of stations to purchase recommended products and services, and promote other projects that customers may be interested in. This ability to capture data, analyze data, and use it to customize the shopping experience or implement marketing activities is the future of retail

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· transportation - analyzing data to identify patterns and trends is crucial for the transportation industry, which depends on developing more effective routes and predicting potential problems to improve reliability and profitability. Data modeling and analysis is an important tool for transportation manufacturers, public transport and other organizations in the industry

· oil and natural gas - help to discover new energy, analyze minerals in soil, predict sensor failures in refineries, accelerate oil distribution, and make it more efficient and economical. In this industry, the number of machine learning applications is huge and continues to grow

· health care -

due to the emergence of wearable devices and sensors, health care professionals can access patient data in real time. Therefore, machine learning is a continuous development trend in the field of health care. The technology can also help medical experts analyze data to identify trends or alerts, thereby improving diagnosis and treatment

methods used in machine learning

the two most widely used machine learning methods are supervised learning and unsupervised learning, but they are not the only methods

train the supervised learning algorithm by marking examples as the input of the known required output. For example, a device may have data points marked "F" (failed) or "e" (executed). The learning algorithm receives a set of inputs and the corresponding correct output, and learns to find errors by comparing the actual output with the correct output. Then it modifies the settlement model. Supervised learning uses criteria to predict tag values in additional unlabeled data through methods such as classification, regression, and gradient enhancement. Supervised learning is usually used in the application of historical data to predict possible future events. For example, it can predict when credit card transactions may be fraudulent, or which policyholders tend to require their policies

unsupervised learning is used for data without historical labels. The "correct answer" is not reported to the system. The algorithm must find out what is displayed. The goal is to explore the data and find some structure in it. Unsupervised learning applies to transaction data. For example, it can identify customer groups with similar attributes, and then deal with them similarly in marketing activities; Or it can find key attributes that separate different customer groups. Common techniques include self-organizing mapping, neighborhood mapping, K-means grouping and decomposition into singular values. These algorithms are also used to segment text topics, recommend items, and identify differences in data

semi supervised learning is used for the same application as supervised learning, but it is trained to deal with labeled and unlabeled data - usually a small amount of data marked with a large amount of unlabeled data (because unlabeled data is cheaper and requires less effort to obtain). This kind of learning can be used in classification, regression, prediction and other methods. Semi supervised learning is useful when the cost associated with labeling is too high to achieve a fully labeled training process. Typical examples include face recognition on a webcam

reinforcement learning is usually used for robots, games and navigation. With it, the algorithm finds which behaviors will bring greater returns through trial and error. This type of learning has three main components: agents (learners or decision makers), environment (Everything agents interact with), and action (what agents can do). The goal is to let agents choose actions that maximize the expected return in a given period of time. If agents follow a good policy, they can achieve their goals faster. Therefore, the focus of reinforcement learning is to find the best strategy

, machine learning and

although all these methods have the same goal of extracting insights, patterns and relationships that can be used for decision-making, they have different methods and functions

can be seen as a superset of many different ways to extract insight from data. It may involve traditional statistical methods and machine learning. Methods from multiple regions are applied to identify previously unknown patterns in the data. This may include statistical algorithms, machine learning, text analysis, time series analysis, and other areas of analysis. Data mining also includes the research and practice of data storage and operation

through machine learning, the purpose is to understand the structure of data. Therefore, there is a theory behind the statistical model that has been mathematically proved, but it requires that the data also meet some assumptions. Machine learning evolved from the ability to use computers to check data structures, even though people don't know what such structures look like. The test of machine learning model is a validation error in new data, not a theoretical test to prove null hypothesis. Because machine learning usually uses iterative methods to learn from data, it can easily learn automatically. These steps are performed through data until a reliable standard is found

combines advances in computing power with special types of neural networks to learn complex patterns in large amounts of data. Technology is the most advanced technology today, which is used to recognize objects in pictures and words in speech. Researchers are trying to apply the success of pattern recognition to more complex tasks, such as machine translation, medical diagnosis and many other social and enterprise problems

although the concepts of artificial intelligence and machine learning have long appeared, they have begun to become part of mainstream applications. However, it is still in its infancy. If AI and machine learning are useful and impressive, their implementation will be more effective when they are better trained and improved