Artificial intelligence has set off a new wave of science and technology in the world.
So what exactly does Python have to do with artificial intelligence, and why does artificial intelligence bring Python to light?
Let's give you a brief introduction to the relationship and application of Python and artificial intelligence, and what you need to learn about Python in order to learn AI. Let's take a look at two diagrams of artificial intelligence and Python.
As you can see from the figure above, artificial intelligence contains two very important modules for common machine learning and deep learning. In the following figure, Python has a large number of libraries such as matplotlib, Numpy, sklearn, and keras. The libraries like pandas, sklearn, and matplotlib are It is a database for data processing, data analysis, data modeling and drawing. Basically, the data is scraped, the processing and analysis of data (pandas), the drawing of data (matplotlib) and the data The sklearn can find the corresponding library in Python for processing.
Therefore, if you want to learn AI without knowing Python, it is equivalent to learning English without knowing words.
So if you want to learn artificial intelligence, you want to learn Python, those things to learn, here's a brief introduction for everyone:
First of all, you have to learn how Python crawls data. You need to do data analysis and data modeling. At the very least, you need to have data. These data sources come in a variety of ways, but many come from networks. This is the crawler.
Reptiles: requests, scrapy, selenium, beautifulSoup, these libraries are all used to write web crawlers, have a good grasp of these things, the data will be there.
Then, with data, you can perform data processing and analysis. At this time, you need to use some libraries for data processing.
Data processing: Numpy, scipy, pandas, matplotlib, these libraries can perform matrix calculations, scientific calculations, data processing, drawing and other operations, with these libraries, you can step by step to start processing the data into the format you need.
Then, after the data meets your format, you will need to use this data to model it, and this time you have a lot of libraries.
Modeling: nltk, keras, sklearn, these libraries are mainly used for natural language processing, deep learning and machine learning. Use these and your model is built.
Finally, if your project is based on an online system developed by Python, you can also learn Python's Web development. In this way, you can use the online system directly.
Web development: Django, Flask, Tornado, these libraries make it clear that your Web development will be done.
However, there is a saying "Life is short and I use Python". This is because Python is much more concise than other languages ​​when implementing various functions. Many functions need only one line of code in Python, but In Java you may need to write a lot of code to achieve this.
Take a simple example, reading and writing files:
Python reads and writes files:
//Read the file
With open('readFile', 'r') as inFile:
For line in inFile.readlines():
Print line
...
//Write the file
With open('writeFile', 'w') as outFile:
outFile.write("...")
Java read and write files:
Import java.io.InputStreamReader;
Import java.io.FileInputStream;
Import java.io.FileReader;
Import java.io.FileWriter;
Import java.io.BufferedWriter;
Import java.io.File;
BufferedReader br1 = new BufferedReader(new InputStreamReader(new FileInputStream("read_file1")));
BufferedReader br2 = new BufferedReader(new FileReader("read_file2"));
String line = null;
While((line = br1.readLine())!=null){
System.out.println(line);
...
}
File outfile = new File("write_file");
If (!file.exists()){
file.createNewFile();
}
//true = append file
FileWriter fileWritter = new FileWriter(file.getName(),true);
BufferedWriter bufferWritter = new BufferedWriter(fileWritter);
bufferWritter.write(data);
bufferWritrer.flush();
bufferWritter.close();
You guys who study artificial intelligence, you see it, is a simple read-write file, Java operation is much more complicated than Python!
In real work, what we need to do is to focus much of our attention on data, data analysis and understanding, rather than spend 30%-50% of the time to write code. Python not only provides the machine learning needs All of the tool libraries can also allow you to focus on data processing and analysis. Therefore, to learn and enter the artificial intelligence industry, learn Python well.
Finally, attach a list of languages ​​for this year.

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