Python and Artificial Intelligence

At present, mentioning artificial intelligence and data science will almost certainly mention Python. Many courses related to artificial intelligence are actually talking about Python in large lengths. Some beginners even think that artificial intelligence and Python are equal. With the fierceness of the concept of artificial intelligence, there has been a wave of learning Python for all. As a computer programming language, what is the charm of Python, and why it is closed with artificial intelligence.

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Abundant class library

Since its introduction, Python has gradually developed a large and active scientific computing and data analysis community, becoming one of the important languages ​​in data science, machine learning, academic and industrial general software development. In particular, Python’ s support for various libraries makes it a popular choice for data analysis tasks. For example, Numpy (Numerical Python) is the cornerstone of Python’ s numerical calculation. It provides a variety of data structures, algorithms, and most interfaces required for numerical calculation. Pandas provides advanced data structures and functions. It combines the flexible data manipulation capabilities of tables and relational databases (such as SQL) with Numpy’s array calculations to provide a wealth of basic functions that can effectively simplify the work of cleaning and preprocessing data. Matplotlib is currently the most popular Python library for charting and 2D data visualization, which can help data analysts visually observe the data distribution. Scikit-learn is a module dedicated to machine learning. It was invented in 2010 and has become the preferred machine learning toolkit for programmers. SKlearn has 1500 code contributors worldwide, including sub-modules such as classification, regression, clustering, dimensionality reduction, and model selection. Abundant class library makes Python an efficient data science programming language, and engineers can manipulate data flexibly and build their own models like building blocks.


Easy to use

Python is a programming language that represents minimalism. Reading a beautifully typed Python code is like reading an English paragraph, which is very close to the human language. Why Python is so important for programmers? Once it is simple, one thing becomes very pure. When we develop Python programs, we can focus on solving the problem itself, rather than understanding the language itself. It can be understood in one sentence: “Python is the most language in the world that does not need to write comments.” In addition, users use Python to develop or publish their own programs without paying any fees or worrying about copyright issues. Even for commercial purposes, Python is free.

The Python language is simple and easy to learn, and the support library is abundant and powerful. These two reasons have basically established Python’ s position in artificial intelligence.

However, Python does not mean AI. In other words, it is not that you can become an AI expert by mastering Python. Although Python is very powerful, it is just a computer programming language, and what is the core of AI is simply an algorithm, a variety of algorithms, behind these algorithms are statistics, calculus, probability theory and mathematical theory. Without these algorithms, AI cannot be realized.

In order to implement these algorithms, programmers must compile programs. These algorithms are very complex, and if you write everything by yourself, it will be a lot of work. And there are ready-made in Python, so everyone uses Python. And as long as these libraries are supported, other languages, such as R and SAS, can be easily modeled. In addition, programmers who only know Python can also build models, but if there is no mathematical theory behind them, it is difficult to build a good model without knowing how to reasonably process data, choose models, choose parameters, do evaluations.

There is a figurative metaphor. Functions in Python are like a pile of modular building materials. To build a building, it is not enough to have only these raw materials, but also to understand the building structure, materials, machinery, fluid mechanics and other theories to build a building that is suitable for the needs of residents and safe. Otherwise, it can only be called building a house. As for whether the built house can live or not, it often depends on luck. So the core of AI is mathematics, and Python is a powerful tool to implement these cores.

So, are ordinary people without the knowledge of statistics and algorithms unable to use AI technology to solve problems? Nor is it, the automatic modeling technology that has emerged in recent years can solve this problem. Automatic modeling technology is to integrate the data processing experience and theory of statisticians and mathematicians into the software, so that the software can intelligently complete data preprocessing, model building, parameter selection, and evaluation. For users, they only need to put the data into the automatic modeling tool, and configure the target, the tool can automatically build a high-quality model. Therefore, both business personnel and ordinary IT programmers can do data mining business through automatic modeling technology, and even the simple Python can enjoy the benefits of AI technology without having to learn again.

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