After going through the basics of python, you would be interested to know more about further and bit more advance topics of the Python3 programming language.
This article covers them.
Please remember that Python completely works on indentation and it is advised to practice it a bit by running some programs. Use the tab key to provide indentation to your code.

This article is divided in following five sections:

1. Object-Oriented Programming (OOP): Python is an object-oriented programming language, which means it supports OOP concepts like encapsulation, inheritance, and polymorphism. OOP allows you to organize your code into classes and objects, making your code more modular and reusable.

2. Decorators: Decorators are a powerful feature in Python that allow you to modify the behavior of functions or classes without changing their source code. Decorators are defined using the "@" symbol and can be used to add functionality like logging, timing, or authorization checks to your code.

3. Generators: Generators are a type of iterator in Python that allow you to iterate over large datasets without storing them in memory. They are defined using the "yield" keyword and can be used to create lazy evaluation functions that only compute the values when needed.

4. Regular Expressions: Regular expressions are a powerful tool in Python for working with strings. They allow you to search, match, and replace text patterns in a flexible and efficient way. Python has a built-in "re" module that provides support for regular expressions.

5. Error Handling: Error handling is an important part of writing robust and reliable code. Python provides a comprehensive set of tools for handling errors, including try/except blocks, raising exceptions, and logging errors.

6. File I/O: Python provides a simple and flexible way to work with files. You can use the built-in "open" function to read from and write to files, and the "os" module to manipulate file paths and directories.

7. Multithreading and Multiprocessing: Python provides support for multithreading and multiprocessing, which allows you to take advantage of multiple CPU cores to speed up your code. Multithreading is used for IO-bound tasks, while multiprocessing is used for CPU-bound tasks.

8. Web Development: Python is a popular language for web development, thanks to its simplicity and flexibility. Popular web frameworks like Flask and Django provide powerful tools for building web applications, APIs, and web services.

9. Data Science: Python has become the go-to language for data science and machine learning. Popular libraries like NumPy, Pandas, Matplotlib, and Scikit-learn provide powerful tools for working with data, analyzing data, and building machine learning models.

10. Networking: Python provides support for networking and can be used to build networked applications like servers, clients, and chatbots. The built-in "socket" module provides a low-level interface to network protocols like TCP and UDP. Popular third-party libraries like requests and urllib provide higher-level interfaces for working with HTTP and other protocols.