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Today, let's talk about how to learn Python data analysis and machine learning before the wave of artificial intelligence. Maybe many people don't know much about it. In order to make you understand better, the editor summarizes the following contents for you. I hope you can get something from this article.

Like the wave of Internet development, AI is creating a whole new world.

In the face of the new wave of the development of AI, more and more people begin to set foot in the field of AI, study the knowledge of AI and enter the gate of AI. Python,Python, as the most popular artificial intelligence programming language in 2018, can be said to be the top language in the AI era, and it is a stepping stone to enter the AI field. Today we will talk to you about the market use of Python and why Python is so popular. How to learn Python well?

Python is surging in the wind.

According to the programming language popularity Index (PYPL) ranking in February 2019, Java, the king of many years, has finally fallen to the altar, while Python has entered the No.1.

The ranking of PYPL is based on the frequency of relevant searches on Google by programming languages, and the original data comes from the search trend of Google.

In the latest issue of the list, Python's share is as high as 26.42%, an increase of 5.2% over the same period last year, which is the strongest, while the other best ones are only 0.3% higher than those in minority languages.

Why is Python so popular?

Whether the Python language is popular or not focuses on the market demand and market adaptability. For machine learning algorithms, the important thing is that the algorithm can be quickly constructed, good code readability, simple maintenance and easy to use, and Python meets the needs of these market development very well. For example, the most popular machine learning and artificial intelligence technology stacks Scikit-learn, TensorFlow and PyTorch, using them is a Python programming development job. The specific manifestations are as follows:

1. Plays a leading role in data science and AI.

two。 High-quality documentation and rich libraries are useful for a wide range of programming tasks for scientific purposes.

3. The design is very good, fast, sturdy, portable and scalable.

4. Open source, and have a healthy, active, highly supported community.

5. There are some great corporate sponsors, YouTube, Google, Yahoo!, and NASA all use Python a lot internally, especially Google; Facebook's open source PyTorch is also more conducive to the promotion of Python.

How should I catch Python?

With the great demand of data analysts, 90% of job skills need to master Python as a data analysis tool. Here are some suggestions for Python practitioners:

1. Have a clear goal

Those who made great achievements in ancient times not only had extraordinary talents, but also had perseverance.

2. System planning with learning Python

Here, I would like to provide you with a simple learning plan:

The first phase: Overview and fundamentals of Python

It is mainly about the foundation and introduction of Python learning.

The second stage: Python data cleaning

It mainly includes Numpy array and vector computation and Pandas basic & advanced.

Phase 3: Python crawler

Mainly learn the knowledge and practice of Python crawler.

The fourth stage: Python data visualization

Mainly learn to use Matplotlib, Seaborn and other packages for exploratory analysis and visualization of data.

The fifth stage: Python machine learning

It is mainly about some classical algorithms and cases of Python machine learning.

3. Learning means, tools and materials

The means of learning are based on your own learning requirements, which will be strictly implemented in both class hours and planning.

4. Learning mentality

The first is persistence; the second is to do more code; and the third is to make mistakes.

After reading the above, how do you learn about Python data analysis and machine learning before the wave of artificial intelligence? Do you have any further understanding? If you want to know more knowledge or related content, please follow the industry information channel, thank you for your support.

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