How to learn Python
I have shared how to learn data analysis and how to learn sql before, and today I will share how to learn python (maybe not very professional). Thinking about learning something during the summer vacation, I heard from my school teacher that python is very important and I can learn it first, so I started my python learning path
Step 1: Download the software first
I downloaded Anaconda. I think it is very friendly to newcomers. You don’t need to know the python environment or anything (you can find the installation tutorial on csdn)
Step 2: Read books or videos , and follow the code. Because I only took C++ for one semester when I was an undergraduate, I didn't have much contact with the code, so follow the code to help understand the code. Three big blocks: numpy.pandas and matplotlib.
- First look at "python programming: from entry to practice" to learn the basics of python programming
- Look at "Data Analysis Using Python Second Edition", this book is also very detailed, if I encounter basic problems that I don't know, I will check this book
If you don't like reading books, you can also watch videos. There are free introductory videos at station b.
Step 3: Learn machine learning knowledge and implement it with python
The importance of machine learning knowledge to us who study statistics is self-evident. After learning a machine learning model, use python to show it. For beginners, we only need to understand the code written by others. After we understand it, save it and run it for debugging. When reading other people’s code, we can use it in the code Add a note later for easy viewing later.
- First read the book "Statistical Learning Methods", which is very friendly to beginners, and someone on GitHub has reproduced the python code of this book, so after we have learned the theory, we can follow the practical operation to deepen model understanding and code learning
- Then you can read the book "Machine Learning", also called the Watermelon Book. The author uses watermelon as an example to introduce the model, which is quite interesting.
- I also read - the book is called "Machine Learning in Action", and this book also comes with python code, you can also read it if you are free.
In addition, there is a machine learning theoretical knowledge explained by a teacher from Zhejiang University on station b, which can be used to assist books to learn together
Step 4: Make projects on kaggle and play competitions . This step is to apply theory to practice. In real scenarios, how do we use python to build machine learning models for data mining and analysis?
Step 5: If you have a friend who focuses more on algorithms, you can practice python algorithms on Niuke or Likou
Step 6: Learning the Deep Learning Model
I am not very interested in deep learning, so I didn’t do deep learning. If you are more interested in neural network, graph neural network, etc., you can read the book to learn the theory, and then find the code on csdn or github to debug it yourself.
Regarding the theoretical part of this piece, our teacher bought us the book "Neural Network and Deep Learning" as an introductory book.
Finally: Friends, when there is a bug in the code, don’t panic, copy the cause of the error and ask Du Niang, you can usually find the solution, and then just follow the debugging
Technical reserves about Python
If you are ready to learn Python or are currently learning, the following should be useful to you:
① Learning roadmap for all directions of Python, clear what to learn in each direction
② More than 100 Python course videos, covering essential foundations, crawlers and data analysis
③ More than 100 practical cases of Python, learning is no longer just theory
④ Huawei Exclusive Python manga tutorials are produced, and
mobile phones can also learn
There is a way to get it at the end of the article
1. Learning routes in all directions of Python
The route of all directions in Python is to organize the commonly used technical points of Python 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 learn more comprehensively.
2. Python course video
When we watch videos and learn, we can’t just move our eyes and brain without using our hands. A more scientific learning method is to use them after understanding. At this time, the hands-on project is very suitable.
Due to limited space, only part of the information is shown, you need to click the link below to get it
Three, Python actual combat case
Optical theory is useless, you have to learn to follow along, and you have to do it yourself, so that you can apply what you have learned to practice. At this time, you can learn from some actual combat cases.
4. Python comic tutorial
Use easy-to-understand comics to teach you Python, making it easier for you to remember and not boring.
5. Internet enterprise interview questions
We must learn Python to find high-paying jobs. The following interview questions are the latest interview materials from first-line Internet companies such as Ali, Tencent, and Byte, and Ali bosses have given authoritative answers. After finishing this set The interview materials believe that everyone can find a satisfactory job.
This complete set of learning materials for Python has been uploaded to CSDN. If you need it, you can also scan the official QR code of CSDN below or click the WeChat card at the bottom of the home page and article to get the collection method. [Guaranteed 100% free]
Due to limited space, only part of the information is shown, you need to click the link below to get it