Tip 1: Supervised learning needs to master three basic models thoroughly, including linear regression, logical regression and decision tree.

Skill 2: Understand the mathematical meaning of these models, and be able to understand the assumptions and solutions of these models. Write actual code or pseudo code to describe the algorithms of these models and really master these algorithms. It is necessary to study the "K-means algorithm" seriously in order to achieve a truly thorough understanding.

Tip 3: Understand the content that hypothesis testing is easily forgotten by AI engineers. We should be familiar with the basic setting of hypothesis testing and the assumptions behind it, and know under what circumstances these assumptions can be used, and what work needs to be done to make up for them if they are violated.

Skill 4: Have the most basic programming ability, and have a certain grasp of data structures and basic algorithms. Have a basic understanding of building an artificial intelligence system (such as search system, face recognition system, image retrieval system, recommendation system, etc.). ).

If machine learning algorithm is really applied to real products, it must rely on a complete system link, which includes the design of data link, the architecture of the whole system, and even the connection between the front and back ends.

Extended data:

AI engineers will do: design and start analyzing information; Good at some specific development fields, such as network, operating system, database or application; Help maintain the organization's computer network and system; It plays a key role in the design, installation, testing and maintenance of software systems.

Become a professional programmer, able to cooperate with Web developers and software engineers to integrate Java or other programming languages into commercial applications, software and websites; Research software application field and prepare software requirements and specification documents; In order to do this.