Learning How to Use ChatGPT to Learn Python (or anything else)
Let's learn how ChatGPT can help us learn about Python... or really anything at all.
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The verdict is in: ChatGPT isn't just hype, it's a useful tool that everyone can take advantage of in some way.
Note what I didn't say: ChatGPT is not sentient. It is not an artificial general intelligence. ChatGPT is not a panacea that can solve every problem, and will not take everyone's jobs.
Understanding that ChatGPT is a tool at our disposal, let's see how it can help us learn Python. Let's make it a little meta as well: we are going to have ChatGPT provide us with a series of prompts that we can subsequently use to interact with ChatGPT to learn Python.
Here's how we started.
Prompt: I want to learn Python. I am a newcomer to programming. I would like you to be my coach. Write 25 prompts that I could use to interact with you and learn all about Python programming.
And here is the list the ChatGPT provided me with:
- What is Python and why is it popular?
- How do I install Python on my computer?
- What are the basic data types in Python?
- How do I declare variables in Python?
- What are the different operators available in Python?
- How do I use if-else statements in Python?
- What are loops in Python and how do I use them?
- How do I define and call functions in Python?
- What are lists and how do I use them in Python?
- What are tuples and how are they different from lists in Python?
- What are dictionaries and how do I use them in Python?
- What are sets and how do I use them in Python?
- What are the built-in functions in Python and how do I use them?
- How do I work with strings in Python?
- How do I work with files in Python?
- What is exception handling in Python and how do I use it?
- How do I use modules and packages in Python?
- How do I create and use classes and objects in Python?
- What is inheritance and polymorphism in Python?
- What are decorators and how do I use them in Python?
- How do I use regular expressions in Python?
- How do I work with databases in Python?
- How do I use the NumPy library in Python?
- How do I use the Pandas library in Python?
- How do I use the Matplotlib library in Python for data visualization?
This is especially helpful as it provided topics for the prompts in a (somewhat) logical progression as far as learning goes. For instance, you would certainly want to understand how to install Python (prompt #2) before delving into NumPy (prompt #23).
Anyhow, these prompts all seem reasonable. Let's look at the next step: using the prompts. Let's start at the beginning.
Prompt: What is Python and why is it popular?
Python is a high-level, interpreted programming language that was first released in 1991. It is a popular language due to its easy-to-read syntax, dynamic typing, and interpreted nature, which make it a versatile and accessible language for beginners and experts alike. Python is also widely used for various applications including web development, scientific computing, data analysis, artificial intelligence, and more. Additionally, Python has a large, active community that develops and maintains a vast number of useful libraries and modules, which add functionality to the language and further increase its popularity.
OK, that's nice high-level overview. Now let's get some more information.
Prompt: Can you elaborate on this?
Sure! Let me break down a few key points:
High-level language: Python is considered a high-level language because it abstracts away many of the underlying details of the computer, such as memory management, that are handled automatically by the language's interpreter. This makes it easier to write and understand code, as well as faster to develop.
Easy-to-read syntax: Python's syntax is designed to be simple, readable, and intuitive, which makes it a great choice for beginners who are just starting to learn programming. This is because the syntax is closer to natural language and does not require a lot of symbols or special characters like some other programming languages do.
Dynamic typing: Python uses dynamic typing, which means that you don't need to specify the type of a variable before using it. This makes the code more flexible, but also means that the interpreter needs to do more work behind the scenes to keep track of what each variable is.
Interpreted language: Python is an interpreted language, which means that the code is executed line by line by the interpreter, rather than being compiled into machine code beforehand. This allows for faster development, as you can test small portions of code without having to compile the entire program, but it also means that the code will run slower than if it had been compiled.
Versatile: Python is used for a wide range of tasks, from web development and scientific computing, to data analysis and artificial intelligence. This versatility is due in part to its large, active community that has developed many libraries and modules that add functionality to the language.
Large community: As mentioned, the Python community is large and active, which means that there are many resources available for learning the language, getting help with specific problems, and contributing to the development of new libraries and modules. This also helps to ensure that the language continues to evolve and improve over time.
These are just a few of the reasons why Python is so popular and widely used.
That's a little more helpful. Let's dig deeper into a particular point.
Prompt: Explain dynamic typing in more detail.
Dynamic typing is a feature of the Python programming language that allows for the type of a variable to change during the course of the program's execution. This is in contrast to statically-typed languages, such as C or Java, where the type of a variable must be specified when it is declared and cannot change thereafter.
With dynamic typing, you can declare a variable and assign a value to it, without having to specify the type of the value beforehand. For example:
x = 42
In this case, the type of x is automatically determined to be int based on the value assigned to it. Later on in the program, you can change the value of x to be a string, for example:
x = "Hello, World!"
And the type of x will change automatically to str.
Dynamic typing makes the code more flexible and easier to write, as you don't have to keep track of the type of each variable and can just focus on the values and operations. However, it also means that the interpreter has to do more work behind the scenes to keep track of the types, and it can also make it harder to catch certain types of errors, such as type mismatches, until runtime.
Overall, dynamic typing is one of the features that makes Python a popular choice for many tasks, as it allows for faster development and more flexible code.
All of a sudden it seems as though we are getting somewhere, and learning some important points about Python and programming in general.
ChatGPT can be probed further for clarification, rephrasing, and elaboration on points as necessary, which it is always able to do. This isn't just a valid way of learning Python, you can leverage ChatGPT's strengths to help you learn any topic in the same way.
And note what we didn't have to do: figure out what to search for online; search for results online; comb through results looking for useful resources; parse the information in each resource, consume it, and then ultimately realize whether or not it was useful.
And this is the real power of ChatGPT. The flip side is that reliance on only what ChatGPT shares can leave you susceptible to bias, misinformation, and related issues. In this particular case, that may not be as potentially detrimental as in others; however, supplementing this information with some follow up internet searches can help ensure what is being provided by ChatGPT is accurate, unbiased information.
As ChatGPT might tell you (if prompted):
Learning is a great accomplishment and taking the time to expand your knowledge is commendable. Keep up the great work! Every small step you take brings you closer to mastering the new topic. Remember that the journey is just as important as the destination. Don't be discouraged if things seem challenging at times, that's a normal part of the learning process. Keep asking questions and seeking out resources, and you'll find success in no time. Believe in yourself and your abilities, and have fun with the learning experience!
Matthew Mayo (@mattmayo13) is a Data Scientist and the Editor-in-Chief of KDnuggets, the seminal online Data Science and Machine Learning resource. His interests lie in natural language processing, algorithm design and optimization, unsupervised learning, neural networks, and automated approaches to machine learning. Matthew holds a Master's degree in computer science and a graduate diploma in data mining. He can be reached at editor1 at kdnuggets[dot]com.