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Does artificial intelligence need to learn python?
Python has many excellent deep learning libraries available, such as matplotlib, Numpy, sklearn, keras and many other libraries, such as pandas, sklearn, matplotlib and so on, which are all used for data processing, data analysis, data modeling and drawing. Basically, data grabbing, data processing and analysis (Panda), data drawing (matplotlib) and data modeling (sklearn) in machine learning can all be processed in Python.

Python and artificial intelligence

Now most deep learning frameworks support Python, so Python is a common language for artificial intelligence (AI) and data analysis. Not only that, Python also contains high-quality documents, rich AI libraries, machine learning libraries, natural languages and text processing libraries.

In particular, machine learning in Python meets a large number of demands in the field of artificial intelligence.

So what do you need to learn to learn Python+ artificial intelligence?

The first stage: Python basics. Able to skillfully use Python technology to complete the programming of small problems.

The second stage: Web basic development. Can use object-oriented programming method to develop multi-task network program development based on Linux operating system.

The third stage: Web-Django framework and project.

1, able to develop mainstream websites and master common technical points;

2. Design the corresponding database table according to the actual problems.

The fourth stage: Web-Flask framework and project.

1, High Concurrent Full-function Website Development;

2. Improve the response speed of data processing and use the cache flexibly.

The fifth stage: artificial intelligence machine learning programming.

1. Use the learned scientific computing library to basically process the collected data to make it conform to the machine learning algorithm model;

2. Solve some practical problems with the learned machine learning algorithm.

The sixth stage of artificial intelligence machine learning programming.

1, which can realize the algorithm application of recommendation system in different scenarios;

2. Be able to complete the recommendation business development according to the recommendation scenario business process.