python快速掌握
Python is a high-level interpreted programming language that can be used for a variety of software development tasks including scientific computing, machine learning, data analysis, image analysis and much more. In this post, I will walk through some of the best learning resources for mastering python.
Python是一种高级解释型编程语言，可用于各种软件开发任务，包括科学计算，机器学习，数据分析，图像分析等等。 在本文中，我将逐步介绍一些精通python的最佳学习资源。
Let’s get started!
让我们开始吧！
在线学习 (Online Learning)
Corey Schafer YouTube频道 (Corey Schafer YouTube Channel)
One of the first learning resources I started using for learning python was Corey Schafer’s YouTube Channel. Corey’s channel is great if you are just getting started or if you’re a seasoned python developer that is interested in trying something new with python. The channel has playlists for the following topics:
我开始用于学习python的第一个学习资源之一是Corey Schafer的YouTube频道。 如果您刚刚开始使用Corey，或者如果您是一位经验丰富的python开发人员，并且对尝试使用python进行一些新的开发感兴趣，那么Corey的渠道非常有用。 该频道具有以下主题的播放列表：
Python Programmer Beginner Tutorials
Python程序员初学者教程
This is pretty self explanatory. This playlist contains comprehensive videos for learning the python basics in an accessible manner. Here you’ll learn about types, lists, tuples, dictionaries, conditionals, loops and iteration, functions, the OS module, the python standard library and much more. This is truly a great place to start for those who feel intimidated by Python as it is very accessible for any audience.
这是不言自明的。 该播放列表包含全面的视频，这些视频以易于访问的方式学习python基础知识。 在这里，您将了解类型，列表，元组，字典，条件，循环和迭代，函数，OS模块，python标准库等。 对于那些被Python吓到的人来说，这确实是一个不错的起点，因为它对任何读者都很方便。
2. Pandas Tutorials
2. 熊猫教程
Pandas is a powerful python library for data analysis and data manipulation. Corey’s channel provides a playlist of tutorial videos discussing how to perform various tasks on data. These include reading and writing data, filtering data, selecting data, adding/removing rows, aggregating data and much more!
Pandas是一个功能强大的python库，用于数据分析和数据处理。 Corey的频道提供了一个教程视频的播放列表，讨论了如何对数据执行各种任务。 其中包括读取和写入数据，过滤数据，选择数据，添加/删除行，聚合数据等等！
3. Django Tutorial
3. Django教程
Corey’s channel also contains tutorials on the Django, a python based web framework. If you already have some python development experience in your back pocket and you’re interested in learning how to build highly scalable and secure web applications, I highly recommend you check out Corey’s Django tutorials.
Corey的频道还包含有关Django(基于python的Web框架)的教程。 如果您已经有一些python开发经验，并且对学习如何构建高度可扩展且安全的Web应用程序感兴趣，那么我强烈建议您查看Corey的Django教程。
Sentdex YouTube频道 (Sentdex YouTube Channel)
Sentdex is a YouTube channel run by Harrison Kinsley that provides in-depth machine learning tutorials in python. Sentdex was instrumental in my early learning days as a data scientist and I highly recommend the channel to anyone with an interest in delving into the field. The channel covers many fundamental machine learning algorithms such as linear regression, neural networks, random forests, support vector machines and much more.
Sentdex是哈里森·金斯利(Harrison Kinsley)运营的YouTube频道，它以python提供了深入的机器学习教程。 Sentdex在我作为数据科学家的早期学习中发挥了重要作用，我向有兴趣钻研该领域的任何人强烈推荐该渠道。 该频道涵盖了许多基本的机器学习算法，例如线性回归，神经网络，随机森林，支持向量机等等。
数据营 (DataCamp)
DataCamp is a subscription-based platform that provides high quality video tutorials on python and machine learning. The Data Science with Python Track is particularly great for those who want to get their feet wet using python for machine learning. It is also a great place to sharpen data science and machine learning fundamentals as well as explore some applications to niche industry verticals.
DataCamp是一个基于订阅的平台，可提供有关python和机器学习的高质量视频教程。 对于想要使用python进行机器学习的人来说，带Python跟踪的数据科学特别有用。 它也是加强数据科学和机器学习基础知识以及探索某些在利基行业垂直市场中的应用的好地方。
图书 (Books)
If you learn more efficiently through books, the following texts are great resources for learning python:
如果您通过书本学习更有效，则以下文本是学习python的重要资源：
Learning Python, Mark Lutz & David Ascher
学习Python ，Mark Lutz和David Ascher
Learning Python by Mark Lutz is a great place to get started learning python. It covers many of the fundamental concepts such as types & operators, lists, dictionaries, tuples, conditionals, functions, modules, classes and much more.
Mark Lutz的“ 学习Python”是开始学习python的好地方。 它涵盖了许多基本概念，例如类型和运算符，列表，字典，元组，条件，函数，模块，类等等。
2. The Python Cookbook, Brian Beazley & Brian K. Jones
2. Python 的食谱，布莱恩·比兹利和布赖恩·琼斯K.
If you have some python experience, The Python Cookbook is a great resource to take your skills to the next level. As the name suggests, this book serves as a catalogue of recipes for performing a wide variety of development tasks in an efficient way. This book discusses data structures & algorithms, string & text manipulation using regular expressions, HTML & XML parsing, handling dates & times, working with generators and iterators, network and web programming and much more!
如果您有一些python经验，那么Python Cookbook是将您的技能提升到更高水平的绝佳资源。 顾名思义，这本书是食谱的目录，用于以有效的方式执行各种开发任务。 本书讨论了数据结构和算法，使用正则表达式的字符串和文本操作，HTML和XML解析，处理日期和时间，与生成器和迭代器一起使用，网络和Web编程等等！
3. Effective Python, Brett Slatkin
3. 有效的Python， Brett Slatkin
This is another intermediate text that is great for deepening your python knowledge. It covers how to write pythonic code, that is how to code in a simple and explicit way while maximizing readability. Within the framework of pythonic thinking, it covers some fundamental concepts including functions, classes, metaclassess, concurrency, built-in modules and more. This way of developing in python helps you write clean easy to read code which makes working with the language much more of a pleasure.
这是另一个中间文本，对于加深您的python知识非常有用。 它涵盖了如何编写pythonic代码，即如何在最大程度地提高可读性的同时以简单明了的方式进行编码。 在pythonic思维的框架内，它涵盖了一些基本概念，包括函数，类，元类，并发，内置模块等。 使用python开发的这种方式可以帮助您编写清晰易读的代码，这使使用该语言更加愉快。
4. Hands-on Machine Learning with Scikit-Learn & TensorFlow, Aurelien Geron
4. 使用Scikit-Learn和TensorFlow进行动手机器学习 ，Aurelien Geron
Once you have a solid foundation in python, if you’re interested in getting your feet wet with machine learning, I recommend Hands-on Machine Learning with Scikit-Learn & TensorFlow. This text covers machine learning fundamentals including how to build classification models & regression models, reduce dimensionality, build deep neural networks, build convolutional neural networks, build recurrent neural networks and much more. If you are a python programmer with an interest in getting started with machine learning, this is a great resource.
一旦您在python上有了扎实的基础，如果您有兴趣通过机器学习来学习，我建议使用Scikit-Learn和TensorFlow进行动手机器学习。 本文涵盖了机器学习的基础知识，包括如何建立分类模型和回归模型，减少维数，建立深度神经网络，建立卷积神经网络，建立递归神经网络等等。 如果您是对机器学习入门感兴趣的python程序员，那么这是一个很好的资源。
结论 (CONCLUSION)
In this post, we discussed several resources for mastering the python programming language. For those truly just getting started, I highly recommend Corey Schafer’s channel & Learning Python by Mark Lutz. These resources will sufficiently familiarize you with the language and open the doors for other possible interests you may have. Whether you want to build the next Instagram with Django or the next facial recognition technology using deep learning in Keras, this list is enough to get you started on your Python journey. I hope you found this post useful/interesting. Thank you for reading and good luck!
在这篇文章中，我们讨论了一些掌握python编程语言的资源。 对于那些真正刚刚起步的人，我强烈推荐Mark Lutz撰写的Corey Schafer的频道＆ Learning Python 。 这些资源将使您充分熟悉该语言，并为您可能有的其他兴趣打开大门。 无论您是要使用Django构建下一个Instagram，还是要在Keras中使用深度学习构建下一个面部识别技术，此列表都足以使您开始Python之旅。 我希望您发现这篇文章有用/有趣。 感谢您的阅读和好运！
翻译自: https://towardsdatascience.com/best-resources-for-mastering-python-2356b8be0ece
python快速掌握