What is machine learning and where is it commonly used?
Machine learning is a general data processing technology that contains a large number of learning algorithms, and different algorithms can show different performances and advantages in different industries and applications. Application fields: financial field, Internet field, medical field, automation and robotics field, biological field, game field, news field, criminal investigation field.
The operating environment of this tutorial: Windows 10 system, Dell G3 computer.
Machine learning is an engineering technology. Before learning, readers must understand what problems this technical tool can solve, what related industries it can be applied to, and what existing successful technical applications are, etc., so as to inspire Passion for learning.
Machine learning is a general data processing technology that contains a large number of learning algorithms, and different algorithms can show different performance and advantages in different industries and applications.
Currently, machine learning has been successfully applied in the following fields:
Financial field: detecting credit card fraud, securities market analysis, etc.
Internet field: natural language processing, speech recognition, language translation, search engines, advertising promotion, email anti-spam filtering system, etc.
Medical field: medical diagnosis, etc.
Automation and robotics fields: driverless driving, image processing, signal processing, etc.
Biological field: human gene sequence analysis, protein structure prediction, DNA sequence sequencing, etc.
Game field: game strategic planning, etc.
News field: news recommendation system, etc.
Criminal investigation field: potential crime prediction, etc.
In summary, it can be said that machine learning is becoming an analysis tool frequently used in all walks of life, especially as the amount of data in various fields continues to increase, all companies hope to use data analysis Through this method, we can obtain valuable information from the data, thereby guiding the development of the enterprise and clarifying customer needs.
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