I just create an easy to follow ChatGPT API tutorial. In this one I go over how to work the ChatGPT Playground to create an address cleaner and produce clean JSON payloads.

https://youtu.be/3wivIUrC7_Q

Feedback always welcome!

The complete guide: https://medium.com/towards-artificial-intelligence/start-machine-learning-in-2020-become-an-expert-from-nothing-for-free-f31587630cf7

Here is a GitHub repository with all the useful resources linked if you prefer it this way:
https://github.com/louisfb01/start-machine-learning-in-2020

When I first started in tech, I thought the people who stood out had 10+ years of experience.
But over time, I noticed something different: the people who grow the fastest aren’t the ones who know every new tool they’re the ones who never skipped the fundamentals.

The truth is, most beginners rush past the basics. They chase frameworks, languages, and “hot skills,” but can’t explain how files move, how code is tracked, or how networks actually work. That gap shows up quickly in real projects and interviews.

If you want to level up your career faster, focus here first:

• Command Line Basics → navigating, managing files, running scripts. It makes you way faster than click-hunting through GUIs.

• Git & Version Control → not optional. Every serious project lives on GitHub. Your repos are proof you can build.

• Networking 101 → IPs, DNS, ports, firewalls. Whether it’s AWS, Python, or DevOps, everything depends on it.

• Databases → CRUD, joins, indexes. Even a little SQL knowledge puts you ahead of “tutorial coders.”

• APIs → apps talk to each other through APIs. Learn how to send/receive data. It unlocks everything from web apps to automation.

• Cloud Essentials → EC2, S3, IAM, VPC. Even beginner-level cloud knowledge gives you an edge.

• Problem-Solving Mindset → syntax is easy. What makes you valuable is breaking down problems and figuring things out.

Frameworks and tools will keep changing. But fundamentals? They compound forever.

Curious which of these you’ve been focusing on lately?

Hello guys, in this blog post I have organized 40 Python projects that you might be interested in trying out ranging from web scraping to natural language processing.

All the projects listed here are the ones I have built personally as I learn python and I thought it might be useful sharing them with other people learning python so as to help them solidify python skills as they build them.

Here a link to the full article https://kalebujordan.dev/40-python-projects-ideas/

The Python Cheatsheet is designed in an easy to understand flow with examples to learn the basics of Python. It can also be considered as a guide to Getting Started with Python.

In this post, we will discuss the best 25 Python projects ever with source code that are perfect for beginners. For beginners, Python is a great language to learn because it is relatively easy to understand and read. Additionally, there are many online resources and community support groups available to help beginners get started with Python. You can start here and move up to the next level.

1. Python Program to Display Calendar

2. Create To-Do List Using Python

3. Create a Digital Clock in Python

4. Create a Password Generator Tool

5. Taking Multiple User input using python

6. Fing Greater Number using If Function

7. Python Program to Check Prime Number

8. Find Duplicate Value using Python

9. Find LCM of Two numbers using python

10. Find mean, median, and mode using python without libraries

11. Python Program to Print all Prime Numbers in an Interval

12. Python Program to Find Sum of Natural Numbers

13. Python Program to Find the Factorial of a Number

14. Calculate the Area of the Triangle

15. Python Program to Create a Countdown Timer

16. Add Two Matrics, Transpose, and Multiplication using python

17. Library Management System

18. Vehicle Inventory System

19. Billing System Projects

20. Contact Management Projects

21. Health And Gym Management Project In Python

22. School Management System Project In Python

23. Binary Converter In Python with Source Code

24. Simple Chatbot In Python with source code

25. Simple Quiz Project In Python With Source Code

Python is a great language for beginners users. It is relatively easy to understand and read. Additionally, there are many online resources and community support groups available to help beginners get started with Python. In this post, we have discussed the best 25 Python projects ever with source code that are best for beginners.

I curated a list of 49 most popular Python modules with their pip commands:

https://geekyhumans.com/python-libraries/

Please let me know if I missed any important module.

Thanks

Hey, today I want to share a short tutorial on how to create a DeepFake video using the "First Order Motion Model for Image Animation” in Google Colab. Thanks to Colab, you don’t need a powerful GPU to generate deep fakes, because the model runs in the cloud. https://youtu.be/eq55Qy4RPiA

Hi! I have been working on this project for almost 5 years, and decided a few months ago to start over again.

Since the project was re-launched almost two months ago, many things have changed: The contents were revised, the examples updated, and new sections were added. The website was also completely redesigned with support for offline navigation, a dark mode, instant search, and a few other improvements.

I wanted to hear back from the community and find people who would be willing to help contribute.

Python Cheatsheet - GitHub

At work I'm generally tasked with optimizing code from data scientists. This often means to rewrite code in c++ and incorporate it into their projects with pybind11. In doing this, I noticed something interesting is going on with numpy's sort operation. It's just insanely fast at sorting simply arrays of float64s -- much better than c++.

I have two separate benchmarks I'm running - one using Python (with Numpy), and the other is plain C++.

Python:

n = 1_000_000
data = (np.random.rand(n) * 10)

t1 = time.perf_counter()
temp = data.copy()
temp = np.sort(temp)
t2 = time.perf_counter()

print ((t2-t1) * 1_000, "ms")

C++

int main() {
    size_t N = 1000000;

    std::random_device rd;
    std::mt19937_64 gen(rd());
    std::uniform_real_distribution<double> dis(0.0, 10.0);
    
    std::vector<double> data;
    data.reserve(N);
    for (size_t i = 0; i < N; ++i) {
        data.push_back(dis(gen));
    }
    
    auto start = std::chrono::high_resolution_clock::now();
    std::sort(data.begin(), data.end());
    auto end = std::chrono::high_resolution_clock::now();
    
    auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end - start);
    
    std::cout << "Sort time: " << duration.count() << " ms\n";
}

In python, we can sort this list in 7ms on my machine. In c++, this is about 45ms. Even when I use the boost library for faster sorts, I can't really get this to go much better than 20ms. How can it be that this runs so much faster in numpy? All my googling simply indicates that I must not be compiling with optimizations (I am, I assure you).

The best I can do is if I'm sorting ints/longs, I can sort in around the same time as numpy. I suppose I can just multiply my doubles by 10^9, cast to int/longlong, sort, then divide by 10^9. This loses precision and is clearly not what np is doing, but I'm at a loss at this point.

Any pointers would be greatly appreciated, or else I'm going to have to start declaring Python supremacy.

When learning Python, even a small feature can sometimes create a "wow" effect. For me: a,b=b,a

was to change two variables without using temporary variables.

What surprised you while you were learning ?

Example:

numbers = [(i, bin(i), hex(i)) for i in range(15)]
print(*numbers, sep='\n')

Output:

(0, '0b0', '0x0')
(1, '0b1', '0x1')
(2, '0b10', '0x2')
(3, '0b11', '0x3')
(4, '0b100', '0x4')
(5, '0b101', '0x5')
(6, '0b110', '0x6')
(7, '0b111', '0x7')
(8, '0b1000', '0x8')
(9, '0b1001', '0x9')
(10, '0b1010', '0xa')
(11, '0b1011', '0xb')
(12, '0b1100', '0xc')
(13, '0b1101', '0xd')
(14, '0b1110', '0xe')

I knew about argument unpacking (*args), I just never connected it with using print() this way.

When I started learning Python, I kept bouncing between tutorials and still felt like I wasn’t actually learning.

I could write code when following along, but the second i tried to build something on my own… blank screen.

What finally helped was working on small, real projects. Nothing too complex. Just practical enough to build confidence and show me how Python works in real life.

Here are five that really helped me level up:

1. File sorter Organizes files in your Downloads folder by type. Taught me how to work with directories and conditionals.

2. Personal expense tracker Logs your spending and saves it to a CSV. Simple but great for learning input handling and working with files.

3. Website uptime checker Pings a URL every few minutes and alerts you if it goes down. Helped me learn about requests, loops, and scheduling.

4. PDF merger Combines multiple PDF files into one. Surprisingly useful and introduced me to working with external libraries.

5. Weather app Pulls live weather data from an API. This was my first experience using APIs and handling JSON.

While i was working on these, i created a system in Notion to trck what I was learning, keep project ideas organized, and make sure I was building skills that actually mattered.

If you’ve got any other project ideas that helped you learn, I’d love to hear them. I’m always looking for new things to try.

I made further improvements to my Python performance testing library. Now it is possible to generate heatmaps more information can be found in my repository on GitHub:

https://github.com/JoeyHendricks/QuickPotato

https://towardsdatascience.com/7-cool-python-tricks-that-you-probably-didnt-know-634ae56112be?source=friends_link&sk=b847bec39ab19d45862b1c7c91f5ed2f

Had a noteworthy event today I thought many people in here would get a kick out of. I work as the IT tutor at my school and has part of that I handle any Intro to Programming appointments that we get. In the Intro classes, they use Python. So I had a student come in today, basically at her wit's end, border-line in tears. Short of the long: 5 weeks into class and she was not understanding the concepts and it was really frustrating her. Okay, so I hunker down and start working with her. In this case, focusing on loops (as that's chapter 6 in the book). He seems to kind of, sort of be getting it, if I hold her hand and lead her through it.

30 minutes in and we haven't really made any progress. I took a step back and started working with her on comparison operations. But she wasn't really getting that either. she is getting more frustrated. So I try to get her mind off the material for a moment. Her phone flashes a reminder about "D&D party" tonight. So I ask if she plays D&D. She does, and we chat briefly about the campaign she's in (she plays a rogue, go figure). Then I hit on an idea.

"You know a lot of early programmers were RPG players and because of that a lot of what developed into modern programming was influenced and based on RPG mechanics?"

She looked at me like I'm making a really bad setup for an even worse joke.

"I'm serious. How do you determine if you pass or fail a stat-test?" I ask.

"Well, you determine what you need to roll over to pass, roll a dice, add any modifiers you have and compare that to the first number." She explains, more energetically.

"So you perform a comparison operation?" I asked leadingly.

"Well... no. I mean... kind of... wait..." She stammers out, her response interspersed as she thinks and talks at the same time.

I go to write on the White board:

if Dice_roll+modifiers >= needed_roll:  
    you pass  
 else:  
     you fail

She looked at it for a long moment, you could see the gears turning in her head.

"So the variable, Dice_Roll is added to the variable modifiers.. and that's compared to the needed roll. If the needed_roll is higher... you fail... otherwise... you pass." She parses out, once more thinking and speaking at the same time.

"Yeah... and what about this one?" I ask as I write on the board:

 while enemy_attack == True:            
      player.cast(shield)

She looks at it for a minute.

"So long as the enemy is attacking, cast the shield spell." she says.

"Nice, how about this one?"

 for each enemy in hoard:  
      If player.hp > enemy.defense:  
           Target_enemy = True   
           while Target_enemy == True:  
                player.attack(sword,enemy)

"For each enemy in the hoard... check to see if the player Hit Points are greater then the that enemy's defense. If it is, target that enemy. As long as that enemy is targeted, attack them."

"And you just worked through a while loop nested inside an if control statement that was inside a while loop." I explain.

"What?" She says aghast. She quickly turns back to the board and starts studying the examples intently. I can see her eyes traveling across what I wrote.

"So this on is just a comparison operator?" She askes pointing to the dice roll I wrote out before.

"Yup." I respond.

"And this is just a while loop that would tell the player character to cast the shield spell." She askes sounding more confident.

"Yup." I respond.

Her entire face lights up in that unmistakable look of 'the light bulb turning on'. We start hammering through more examples of loops and control statements, fluctuating between the requirements of her assignments and using RPG examples.

So I guess it all depends on what material you use to explain it, right

My first encounter with Python happened because of a small lie.

During my application to an internship that required me to build the control system of robots using Python, I lied to my recruiter about already possessing beginner-level programming skills in python.

Thankfully, he didn't ask me anything else and accepted me for the internship program. But since that day, I have religiously practiced Python every day in my life, so that I wouldn't have to lie to anyone else!

In my beginner days, I did the whole dance to learn Python.

I did a lot of free courses on the internet.

I solved every problem I could find on any Python topic again and again. And then again!

Initially, I only did it to do excel at my thesis. But eventually, I fell in love with this simple language.

So much so that I started applying for Python programming jobs even before my Masters in Transportation Engineering got over and happily accepted the job offer when I got one.

Websites I followed for learning I would like to provide the list of websites, that were of great help when I began programming.

2015 was not the year when there tons of Medium or Dev posts on learning Python. So, finding something useful was actually tricky.

I was a student and therefore, could not afford any paid courses. So I had to stick to the free websites that were available at that time.

1. Stack Overflow

2. Python for everybody course on Coursera

Shout out to Charles Severance for helping me land my first job.

3. Learn Python the Hard Way

For those of you confused about your career decisions, I would definitely recommend that Python is a great programming language to learn. It provides a lot of job opportunities and doesn't have a huge learning curve. Check out my blog on 5 career driven decisions to learn Python.

https://youtu.be/F4oLVoRK9hY

Instead of focusing on best practices and stuff, let's look, for a change, at the things that you should never do.

Here's a list of 18 python anti-patterns and worst practices that you should avoid in order to have a clean, readable, robust, and efficient code.

This list is not exhaustive: it's rather a summary of multiple discussions I had with developers (in real life and on Reddit) and principles coming from the Zen of Python

https://towardsdatascience.com/18-common-python-anti-patterns-i-wish-i-had-known-before-44d983805f0f

GNews is a Happy and lightweight Python Package that searches Google News and returns a usable JSON response. you can fetch/scrape complete articles just by using any keyword. GNews reached 100 stars milestone on GitHub

GitHub Url: https://github.com/ranahaani/GNews

Looking to build a career in Python? Want to improve your resume with multiple personal projects on it? Then this blog of Python projects with source code is for you. You earlier read about the top 5 data science projects; now, we bring you 5 projects implementing data science with Python. In this blog, you’ll find the entire code to all the projects. Read on to give your data science/ Python career a head-start.

List of amazing Python Projects with source code –

• Detecting Fake News Python Project with source code

• Speech Emotion Recognition Python Project with source code

• Detecting Parkinson’s Disease Python Project with source code

• Age and Gender Detection Python Project with source code

• Breast Cancer Classification Python Project with source code

Why do Projects in Python?

In an interview, a resume with projects shows interest and sincerity. Spending time on personal projects ultimately proves helpful for your career. In this blog of python projects, we try our best to include different data science and machine learning libraries of Python to give you a better experience.

Top Python Projects with Source Code

Let’s start discussing python projects with source code –

1. Detecting Fake News with Python

📷

Fake news can be dangerous. This is a type of yellow journalism and spreads fake information as ‘news’ using social media and other online media. This is a common way to achieve a certain political agenda. Fake news may contain false and/or exaggerated claims. Social media algorithms often viralize these and create a filter bubble. In this, we will train on a news.csv dataset of shape 7796×4. We’ll mainly use two things- a Tfidf Vectorizer and a Passive Aggressive Classifier. A Tfidf Vectorizer turns a collection of raw documents into a matrix of TF-IDF features. And a Passive Aggressive Classifier is an online learning algorithm that stays passive for a correct classification and becomes aggressive when there’s a miscalculation. Read More

Keep failing and you will get better my friends.

The zip() is one of the most helpful built-in magic function in Python.

7 Levels of Using the Zip Function in Python