There are so many people on the Internet who advocate learning Python to enter the IT industry. What if they can’t find a job after learning Python?

Mastering a skill costs money. Before making a decision, it is necessary to make a prospect judgment and measure the input-output ratio.

However, once you think about it deeply, you may yourself have doubts about the value of learning Python.

Because most people look at this issue to judge whether they can improve their competitiveness after learning Python.

There is a common proverb that Chinese people often say, there is a saying “one trick is fresh, and it can be eaten all over the world”. That is to say, you have mastered a certain skill that is in short supply, so you can enjoy the benefits and bonuses brought by this skill.

You can pause reading for 20 seconds, and in your mind, match the corresponding skills that meet the above conditions, or the certificates that represent the skills.

After thinking about it, let’s continue.

Such skills do exist. However, to achieve the effect of “eat all over the sky”, you need to measure the supply and demand relationship in the market.

We all know that the demand for Python in the market has been really high in recent years. Many job recruitment conditions include Python.

But what about the other side of the equation, supply?

It is not optimistic.

I am not saying that there is too little supply, but that there is too much.

Don’t forget, the biggest feature of Python is that it is easy to learn.

Therefore, there is no threshold, no moat, and even elementary school children must be taught Python in textbooks.

No matter how big the demand is, if the supply is like this, it will be difficult for the price to go up.

So, if your plan is to use it to realize cash directly after learning Python well, then you must think twice before acting.

Does this mean you shouldn’t learn Python?

Quite the opposite, you really should learn Python.

You may be wondering: Teacher, aren’t you inconsistent?

no.

Python needs to be learned, but this skill is really not applied in this way.

Connection

Python has no threshold, it is so simple, it is not surprising to learn it, so what is the use of learning it?

Useful.

Because it connects you to a huge collaborative network. The spillover effects of this network can be hugely beneficial to you.

for example.

Have you heard of machine learning? It’s been hot lately.

In the past, when people did machine learning, they used a tool called Matlab.

Until 6 or 7 years ago, when Andrew Ng produced what became the classic “Machine Learning” course, the tool used was Matlab.

Of course, because Matlab was very expensive at the time, Andrew Ng encouraged everyone to use Octave (an open source implementation of Matlab) instead.

When I took this course, it was very painful. One of the most important reasons is the use of Matlab/Octave.

This was the 8th homework I did at that time. Let’s see how many files are needed to do a collaborative filtering (Collaborative Filtering).

Just open a code file like this:

As a result, most of the students simply don’t know how to write a program for a collaborative filtering algorithm. Everyone can only be satisfied with the requirements of the course, that is, to fill in the blanks at the position specified in each file.

Therefore, if you plan to use machine learning at that time, you must hold a Matlab book and chew it down. Because only when you understand how to use it, can you really have the courage to try to practice the machine learning skills you have learned from MOOC from beginning to end.

Other Python-based machine learning courses have also sprung up like mushrooms after rain.

For example, in the course of fast.ai, to achieve the same collaborative filtering function, you no longer need to write a bunch of Matlab files and functions.

All you need are these few lines of code:

from fastai.collab import *
path = untar_data(URLs.ML_SAMPLE)
ratings = pd.read_csv(path/'ratings.csv')
ratings. head()
data = CollabDataBunch. from_df(ratings)
learn = collab_learner(data, n_factors=50, y_range=(0.,5.))
learn.fit_one_cycle(5, 5e-3, wd=0.1)

OK, done.

Python has no threshold. But by mastering it, you can learn and master machine learning and even deep learning skills in a shorter time and with higher efficiency.

About Python technology reserves

Learning Python well is good whether it is employment or sideline business to make money, but to learn Python, you still need to have a study plan. Finally, everyone will share a full set of Python learning materials to help those who want to learn Python!

For beginners with 0 basics:

If you are a zero-based novice, you can consider getting started with Python quickly.

On the one hand, the learning time is relatively short, and the learning content is more comprehensive and concentrated.
On the other hand, you can find a learning plan that suits you

Including: Python activation code + installation package, Python web development, Python crawler, Python data analysis, artificial intelligence, machine learning and other tutorials. Take you to learn Python systematically from zero foundation!

Introduction to zero-based Python learning resources

Python learning route summary

The technical points in all directions of Python are sorted out to form a summary of knowledge points in various fields. Its usefulness lies in that you can find corresponding learning resources according to the above knowledge points to ensure that you can learn more comprehensively. (Get the full set of tutorials at the end of the article)

Python essential development tools

Reminder: The space is limited, the folder has been packed, and the way to obtain it is at the end of the article

Python learning video 600 collection

Watching the zero-based learning video is the fastest and most effective way to learn. Following the teacher’s ideas in the video, it is still very easy to get started from the basics to the in-depth.