12 machine learning algorithms that data scientists should master

Algorithms have become an important part of our daily lives, and they almost appear in any area of business. Gartner, the research firm, says the phenomenon is "algorithmic commerce", where algorithmic commerce is changing the way we operate and manage companies. Now you can buy these various algorithms for each business area on the "algorithmic market". The algorithmic market provides developers with more than 800 algorithms, including sound and visual processing, machine learning, and computer vision, which help developers save valuable time and money.

However, algorithms available on the algorithmic market may not meet your specific needs. After all, you need different algorithms to cope with different situations, and the same algorithm can produce different results in different environments. In fact, the types of algorithms available and how they are executed are determined by a number of different variables. These variables include the size and category of data, the industry in which the algorithm is applied, the functions used to perform, and more.

Therefore, sometimes buying a ready-made algorithm and slightly modifying it may not be the best choice. Data scientists should still learn the most important algorithms, how to develop them, and how to choose the most appropriate algorithms based on their intentions? "think Big data" 's infographic shows 12 of the most important algorithms for different applications, and presumably this is something that every data scientist would have loved.

Note: The Chinese translation in the Chinese information map is a reference to the Internet and the translation of the noun in Zhou Zhihua's "machine learning" book. In order not to affect the reader's reading, the Chinese and English version of the infographic are attached to the text.

Chinese Version infographic:

Original English information map

12 machine learning algorithms that data scientists should master