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This article mainly shows you "what are the main application directions of Python", the content is simple and easy to understand, and the organization is clear. I hope it can help you solve your doubts. Let Xiaobian lead you to study and learn "what are the main application directions of Python".

web development

Python-based Web frameworks such as Django and Flask have recently become very popular in Web development.

These web frameworks help you create server-side code (back-end code) in Python. This is code that runs on servers, not code on user devices and browsers (front-end code). If you're unfamiliar with the difference between back-end code and front-end code, see my footnote below.

But wait, why do I need a web framework?

This is because web frameworks make it easier to build common backend logic. This includes mapping different urls to Python code blocks, working with databases, and generating HTML files that users see on browsers.

Which Python web framework should I use?

Django and Flask are two of the most popular Python web frameworks. If you are just starting out, I suggest you use one of these.

What is the difference between Django and Flask?

Main comparison:

Flask provides simple, flexible, and fine-grained control. It is unconstrained (it lets you decide how to implement things).

Django provides a comprehensive experience: an admin panel, database interface, ORM[object-relational mapping], and directory structure for your applications and projects.

You should choose:

If you are concerned about experience and learning opportunities, or if you want more control over which components you use (such as which databases you want to use and how you want to interact with them), you can use Flask.

Django, if you focus on the final product. Especially if you're working on a simple app, such as a news site, an e-store, or a blog, and you want to always have a single, obvious way of doing things.

In other words, if you're a beginner, Flask might be a better choice because it has fewer components to deal with. Also, if you want more customization, Flask is a better option.

On the other hand, if you want to build something straight away, Django might let you do it faster.

data science

First, let's review what machine learning is.

I think the best way to explain machine learning is to give you a simple example.

Suppose you want to develop a program that automatically detects content in pictures.

So, based on the following diagram (Figure 1), you want your program to recognize that it is a dog.

Fig. 1

Consider the other one below (Figure 2), which you want your program to recognize as a table.

Fig. 2

You might say, I can write some code to do that. For example, if there are a lot of light brown pixels in the picture, then we can say that it is a dog. Or, you can figure out how to detect edges in pictures. Then, you might say, if there are many straight edges, then it's a table.

However, this approach can quickly become tricky. What if there's a white dog in the picture without brown hair? What if this diagram only shows the circular part of the table?

What is machine learning?

Machine learning typically implements algorithms that automatically detect patterns in a given input.

For example, you can feed a machine learning algorithm 1000 pictures of dogs and 1000 pictures of tables. Then it learns the difference between a dog and a table. When you give it a new picture of a dog or a table, it recognizes which one it is.

I think it's kind of like the way babies learn new things. How does a baby know that one thing looks like a dog and another looks like a table? Probably from a bunch of examples.

You may not explicitly tell the baby,"If something is furry and has light brown fur, then it's probably a dog. "You might say,'That's a dog. "It's also a dog. This is a table. That's also a table. "

The same goes for machine learning algorithms. You can apply the same idea to:

Recommendation systems (think YouTube, Amazon and Netflix)

Face Recognition

speech recognition

in other applications. Popular machine learning algorithms you may have heard of include:

neural network

deep learning

support vector machine

random forest

You can use any of the above algorithms to solve the picture tagging problem I explained earlier.

Python in Machine Learning

Python has many popular machine learning libraries and frameworks. Two of the most popular are scikit-learn and TensorFlow.

Scikit-learn has some of the more popular machine learning algorithms built in. I mentioned some of them above.

TensorFlow is more like a low-level library that allows you to build custom machine learning algorithms.

If you're just starting a machine learning project, I suggest you start with scikit-learn. If you start having efficiency issues, then I'll start working on TensorFlow.

How should I learn machine learning?

To learn the basics of machine learning, I recommend Stanford Enda Wu's Machine Learning course.

https://www.coursera.org/learn/machine-learning

Note that you will need a basic knowledge of calculus and linear algebra to understand some of the material in these courses.

Then, practice what you have learned from these courses at Kaggle. This is a website where people compete to build the best machine learning algorithm for a given problem. They also have great beginner tutorials.

Data analysis/visualization using Python

One of the most popular libraries for data visualization is Matplotlib. This is a good starter library because: It's easy to get started

Some other libraries, such as seaborn, are based on it. Therefore, learning Matplotlib will help you learn these other libraries later.

script

What is the script?

Scripting generally refers to writing Mini programs that automate simple tasks.

So let me give you an example from my personal experience.

I used to work for a small company in Japan that had an email support system. This is a system we use to respond to questions sent to us by customers via email.

When I worked there, my task was to count the number of emails that contained specific keywords so we could analyze incoming emails. We could have done it manually, but I wrote a simple program/simple script to automate this task.

Actually, we were using Ruby, but Python is also a good language for this task. Python is suitable for this type of task primarily because it has relatively simple syntax and is easy to write. It can also quickly write small things with it and test them.

What about games?

You can use PyGame library to develop games, but it is not the most popular game engine. You can use it to build an interest project, but I personally wouldn't choose it if you're serious about game development.

Instead, I suggest you start with Unity using c#, which is one of the most popular game engines. It allows you to create games on many platforms, including Mac, Windows, iOS and Android.

The above is all the content of this article "What are the main application directions of Python", thank you for reading! I believe that everyone has a certain understanding, hope to share the content to help everyone, if you still want to learn more knowledge, welcome to pay attention to the industry information channel!

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