Python Projects & Free Books

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❌ YOU CAN'T USE LAMBDA LIKE THIS IN PYTHON The main mistake is turning lambda into a logic dump: adding side effects, print calls, long conditions, and calculations to it. Such lambdas are hard to read, impossible to debug properly, and they violate the very idea of being a short and clean function. Everything complex should be moved into a regular function. Subscribe for more tips every day !

# you can't do this - lambda with state changes
data = [1, 2, 3]
logs = []

# dangerous antipattern
process = lambda x: logs.append(f"processed {x}") or (x * 10)

result = [process(n) for n in data]

print("RESULT:", result)
print("LOGS:", logs)

🔰 Convert decimals to other number system

Sometimes reality outpaces expectations in the most unexpected ways. While global AI development seems increasingly fragmented, Sber just released Europe's largest open-source AI collection—full weights, code, and commercial rights included. ✅ No API paywalls. ✅ No usage restrictions. ✅ Just four complete model families ready to run in your private infrastructure, fine-tuned on your data, serving your specific needs. What makes this release remarkable isn't merely the technical prowess, but the quiet confidence behind sharing it openly when others are building walls. Find out more in the article from the developers. GigaChat Ultra Preview: 702B-parameter MoE model (36B active per token) with 128K context window. Trained from scratch, it outperforms DeepSeek V3.1 on specialized benchmarks while maintaining faster inference than previous flagships. Enterprise-ready with offline fine-tuning for secure environments. GitHub | HuggingFace | GitVerse GigaChat Lightning offers the opposite balance: compact yet powerful MoE architecture running on your laptop. It competes with Qwen3-4B in quality, matches the speed of Qwen3-1.7B, yet is significantly smarter and larger in parameter count. Lightning holds its own against the best open-source models in its class, outperforms comparable models on different tasks, and delivers ultra-fast inference—making it ideal for scenarios where Ultra would be overkill and speed is critical. Plus, it features stable expert routing and a welcome bonus: 256K context support. GitHub | Hugging Face | GitVerse Kandinsky 5.0 brings a significant step forward in open generative models. The flagship Video Pro matches Veo 3 in visual quality and outperforms Wan 2.2-A14B, while Video Lite and Image Lite offer fast, lightweight alternatives for real-time use cases. The suite is powered by K-VAE 1.0, a high-efficiency open-source visual encoder that enables strong compression and serves as a solid base for training generative models. This stack balances performance, scalability, and practicality—whether you're building video pipelines or experimenting with multimodal generation. GitHub | GitVerse | Hugging Face | Technical report Audio gets its upgrade too: GigaAM-v3 delivers speech recognition model with 50% lower WER than Whisper-large-v3, trained on 700k hours of audio with punctuation/normalization for spontaneous speech. GitHub | HuggingFace | GitVerse Every model can be deployed on-premises, fine-tuned on your data, and used commercially. It's not just about catching up – it's about building sovereign AI infrastructure that belongs to everyone who needs it.

Cheatsheet Machine Learning Algorithms🌟

OBJECT-ORIENTED PYTHON 📚 book

Tune in to the 10th AI Journey 2025 international conference: scientists, visionaries, and global AI practitioners will come together on one stage. Here, you will hear the voices of those who don't just believe in the future—they are creating it! Speakers include visionaries Kai-Fu Lee and Chen Qufan, as well as dozens of global AI gurus! Do you agree with their predictions about AI? On November 20, we will focus on the role of AI in business and economic development and present technologies that will help businesses and developers be more effective by unlocking human potential. On November 21, we will talk about how engineers and scientists are making scientific and technological breakthroughs and creating the future today! The day's program includes presentations by scientists from around the world: - Ajit Abraham (Sai University, India) will present on “Generative AI in Healthcare” - Nebojša Bačanin Džakula (Singidunum University, Serbia) will talk about the latest advances in bio-inspired metaheuristics - AIexandre Ferreira Ramos (University of São Paulo, Brazil) will present his work on using thermodynamic models to study the regulatory logic of transcriptional control at the DNA level - Anderson Rocha (University of Campinas, Brazil) will give a presentation entitled “AI in the New Era: From Basics to Trends, Opportunities, and Global Cooperation”. And in the special AIJ Junior track, we will talk about how AI helps us learn, create and ride the wave with AI. The day will conclude with an award ceremony for the winners of the AI Challenge for aspiring data scientists and the AIJ Contest for experienced AI specialists. The results of an open selection of AIJ Science research papers will be announced. Ride the wave with AI into the future! Tune in to the AI Journey webcast on November 19-21.

Eigenvalues & Eigenvectors — Why PCA Actually Works You’ve heard of PCA. But what’s really happening underneath? PCA finds the directions (vectors) where your data varies the most. Those directions are eigenvectors of the covariance matrix and the eigenvalues tell you how much variance each captures. You’re basically rotating your data to find its “natural axes.”

PCA isn’t compression — it’s discovering how your data wants to be seen.

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The program for the 10th AI Journey 2025 international conference has been unveiled: scientists, visionaries, and global AI practitioners will come together on one stage. Here, you will hear the voices of those who don't just believe in the future—they are creating it! Speakers include visionaries Kai-Fu Lee and Chen Qufan, as well as dozens of global AI gurus from around the world! On the first day of the conference, November 19, we will talk about how AI is already being used in various areas of life, helping to unlock human potential for the future and changing creative industries, and what impact it has on humans and on a sustainable future. On November 20, we will focus on the role of AI in business and economic development and present technologies that will help businesses and developers be more effective by unlocking human potential. On November 21, we will talk about how engineers and scientists are making scientific and technological breakthroughs and creating the future today! Ride the wave with AI into the future! Tune in to the AI Journey webcast on November 19-21.

🔰 4 Unique Steps to Become a Python Expert in 2025 1️⃣ Understand Python Internals:

Learn how Python handles memory (GIL), garbage collection, and optimize code performance.

✨ Example: Debugging a slow script by identifying memory leaks. 2️⃣ Leverage Async Programming:

Master async/await to build scalable and faster applications.

✨ Example: Using async to handle thousands of API requests without crashing. 3️⃣ Create & Publish Python Packages:

Build reusable libraries, document them, and share on PyPI.

✨ Example: Publishing your own data-cleaning toolkit for others to use. 4️⃣ Master Python for Emerging Tech:

Dive into areas like quantum computing (Qiskit) or AI (Hugging Face).

✨ Example: Building an AI chatbot with Hugging Face APIs.

Python Commands Cheatsheet ✅

Important Python Functions ✅

🔰 Simplify Your Code with namedtuple in Python

📋 This Python program shows how to use namedtuple to create lightweight, readable data structures instead of regular tuples!

✨ Example Output:

Alice 30 Paris

🐍 Python Roadmap 1️⃣ Basics: 📝📜 Syntax, Variables, Data Types 2️⃣ Control Flow: 🔄🤖 If-Else, Loops, Functions 3️⃣ Data Structures: 🗂️🔢 Lists, Tuples, Dictionaries, Sets 4️⃣ OOP in Python: 📦🎭 Classes, Inheritance, Decorators 5️⃣ File Handling: 📄📂 Read/Write, JSON, CSV 6️⃣ Modules & Libraries: 📦🚀 NumPy, Pandas, Matplotlib 7️⃣ Web Development: 🌍🔧 Flask, Django, FastAPI 8️⃣ Automation & Scripting: 🤖🛠️ Web Scraping, Selenium, Bash Scripting 9️⃣ Machine Learning: 🧠📈 TensorFlow, Scikit-learn, PyTorch 🔟 Projects & Practice: 📂🎯 Create apps, scripts, and contribute to open source React ❤️ for more tech related info #techinfo

Question: What are Python set comprehensions? Answer:Set comprehensions are similar to list comprehensions but create a set instead of a list. The syntax is:

{expression for item in iterable if condition}

For example, to create a set of squares of even numbers:

squares_set = {x**2 for x in range(10) if x % 2 == 0}

This will create a set with the values

{0, 4, 16, 36, 64}

https://t.me/DataScienceQ 🌟

30-day roadmap to learn Python up to an intermediate level Week 1: Python Basics *Day 1-2:* - Learn about Python, its syntax, and how to install Python on your computer. - Write your first "Hello, World!" program. - Understand variables and data types (integers, floats, strings). *Day 3-4:* - Explore basic operations (arithmetic, string concatenation). - Learn about user input and how to use the input() function. - Practice creating and using variables. *Day 5-7:* - Dive into control flow with if statements, else statements, and loops (for and while). - Work on simple programs that involve conditions and loops. Week 2: Functions and Modules *Day 8-9:* - Study functions and how to define your own functions using def. - Learn about function arguments and return values. *Day 10-12:* - Explore built-in functions and libraries (e.g., len(), random, math). - Understand how to import modules and use their functions. *Day 13-14:* - Practice writing functions for common tasks. - Create a small project that utilizes functions and modules. Week 3: Data Structures *Day 15-17:* - Learn about lists and their operations (slicing, appending, removing). - Understand how to work with lists of different data types. *Day 18-19:* - Study dictionaries and their key-value pairs. - Practice manipulating dictionary data. *Day 20-21:* - Explore tuples and sets. - Understand when and how to use each data structure. Week 4: Intermediate Topics *Day 22-23:* - Study file handling and how to read/write files in Python. - Work on projects involving file operations. *Day 24-26:* - Learn about exceptions and error handling. - Explore object-oriented programming (classes and objects). *Day 27-28:* - Dive into more advanced topics like list comprehensions and generators. - Study Python's built-in libraries for web development (e.g., requests). *Day 29-30:* - Explore additional libraries and frameworks relevant to your interests (e.g., NumPy for data analysis, Flask for web development, or Pygame for game development). - Work on a more complex project that combines your knowledge from the past weeks. Throughout the 30 days, practice coding daily, and don't hesitate to explore Python's documentation and online resources for additional help. Learning Python is a dynamic process, so adapt the roadmap based on your progress and interests. Best Programming Resources: https://topmate.io/coding/886839 ENJOY LEARNING 👍👍