Machine learning is widely used in the fields of finance, diagnosis and treatment, marketing and road transportation. Among other daily tasks, it is also used to analyze and process a lot of data, to make predictions, and to automate technical management processes.

In this paper, the five important roles of learning and training machine learning make it a powerful and special tool for dealing with a series of problems, from image and speech recognition technology to recommendation algorithm and natural language understanding.

What is machine learning?

Machine learning is a sub-industry of artificial intelligence applications (AI), which involves the development of algorithms and statistical models that allow computers to learn from data without a clear programming. The purpose of machine learning is to build system software that can continuously improve its characteristics in giving daily tasks based on the experience gained from the data it has been exposed to. This is also accomplished by giving extensive learning and training for the algorithm in a large number of data sets, which enables the algorithm to find ways and connections in the data.

• Supervised learning and training: this involves tagging the dataset in the training model, in which the appropriate output is given for each type. The algorithm uses this information content to learn the relationship between input and output, and then predicts a new, invisible data.
• Unsupervised learning: this involves not providing proper output of unidentified data sets in the training model. The algorithm must find the structure in the data itself, and is generally used in clustering algorithm, reduction and anomaly detection.
• Enhanced learning: this involves agent admissions making decisions in an environment where feedback is received through reward or punishment. The algorithm uses this feedback to learn the best strategy to maximize returns over time.

Related to:The root causes of DEFI: artificial intelligence technology, big data, cloud computing technology and distributed system ledger technology

Five core features of machine learning

In recent years, machine learning has become one of the most important scientific and technological advances, and has a far-reaching impact on the general field and application. Its main features are:

• Predictive analysis model: machine learning algorithms use data to build models that predict future events. This entity model can be used to clarify the risk of loan default or the probability of consumer choice.
• Automation technology: machine learning algorithms automate the process of discovering patterns in the process of data, which requires less human participation in order to achieve more accurate and efficient interpretation.
• Scalability: machine learning technology is particularly suitable for solving big data, because it needs to solve a large amount of data. As a result, companies can also make decisions based on the information collected from some data.
• Broad: machine learning algorithms can find common ways in the process of data, which can be used to analyze fresh and unexplored data. Although the data used to train the model may not be immediately applicable to the daily task at hand, they are useful for predicting the future.
• Adaptability: with the birth of new data, machine learning algorithms are created for continuous improvement and integration. As a result, over time, they will be able to improve their main performance, and as they provide each other with better data, they will become more accurate and efficient.

The combination of Machine Learning and Block chain Technology

The combination of machine learning and blockchain technology has a broad market prospect. Machine learning algorithms can be used to evaluate data and form predictive analysis based on data using decentralized and secure service platforms such as digital currency.

One possible application area of this kind of integration is commercial banks. The blockchain features of blockchain technology and the ability to strictly prohibit unauthorized browsing of more sensitive data can help machine learning algorithms to more effectively detect fraud and money laundering.

Machine learning and blockchain technology can also achieve important functions in the supply chain. Although blockchain technology can be used to give openness and accountability in the supply chain, machine learning algorithms can be used to optimize supply chain operation forecasting and analysis requirements.

Block chain technology can achieve safe and subjective sharing of medical records, while machine learning algorithms can be used to predict and analyze disease outbreaks and improve the prognosis of patients.

The Future Development of Machine Learning

It is estimated that the future development of machine learning will be characterized by the continuous improvement of algorithms, computing level and data ease of use. As machine learning is more and more widely used and integrated into various industries, life may endanger social development to a great extent in many ways.

Some inevitable trends and developments in machine learning in the future include:

• Improve the level of automation: with the development of machine learning algorithms, they will be able to automate a wide range of technical work, must have lower human resources investment, and improve production efficiency.
• More personalized experience: machine learning algorithms are about to usher in capability evaluation and the use of large amounts of data to provide relatively highly personalized feelings, such as humanized proposals and advertising.
• Improve judgment: as machine learning algorithms become better and better at making complex decisions and predictive analysis, many companies will benefit from more accurate and effective management decisions.
• The development of artificial intelligence technology in ethics: as machine learning becomes more widespread, people will pay more and more attention to ensuring that machine learning is developed and applied in an ethical and responsible way. the key is to protect personal privacy and eliminate stereotypes in management decisions.
• Interdisciplinary cooperation: machine learning will increasingly be used to cooperate with other fields such as cognitive science and molecular biology to promote discovery and growth in these areas.

All in all, the market for the future development of machine learning is promising, and it is expected to change the general areas from financial services to medical services again in the next few years.