Machine Learning with Python

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A cheat sheet about functions and techniques in Python: shows useful built-in functions, working with iterators, strings, and collections, as well as popular tricks with unpacking, zip, enumerate, map, filter, and dictionaries @DataScience4

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This GitHub repository is not a dump of tutorials. Inside, there are 28 production-ready AI projects that can be used. What's there: Machine learning projects → Airbnb price forecasting → Air ticket cost calculator → Student performance tracker AI for medicine → Chest disease detection → Heart disease prediction → Diabetes risk analysis Generative AI applications → Live chatbot on Gemini → Medical assistant tool → Document analysis tool Computer vision projects → Hand tracking system → Drug recognition app → OpenCV implementations Data analysis dashboards → E-commerce analytics → Restaurant analytics → Cricket statistics tracker And 10 more advanced projects coming soon: → Deepfake detection → Brain tumor classification → Driver drowsiness alert system This is not just a collection of code files. These are end-to-end working applications. View the repository 😲 https://github.com/KalyanM45/AI-Project-Gallery 👉 @codeprogrammer

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Advice on clean code in Python Don't use "naive" datetime without time zones. Store and process time in UTC, and display it to the user in his local time zone

import datetime
from zoneinfo import ZoneInfo

# BAD
now = datetime.datetime.now()

print(now.isoformat())
# 2025-10-21T15:03:07.332217

# GOOD
now = datetime.datetime.now(tz=ZoneInfo("UTC"))
print(now.isoformat())
# 2025-10-21T12:04:22.573590+00:00

print(now.astimezone().isoformat())
# 2025-10-21T15:04:22.573590+03:00

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In scientific work, the most time is spent on reading articles, data, and reports. On GitHub, there is a collection called Awesome AI for Science -»»» a catalog of AI tools for all stages of research. Inside: -» working with literature -» data analysis -» turning articles into posters -» automating experiments -» tools for biology, chemistry, physics, and other fields GitHub: http://github.com/ai-boost/awesome-ai-for-science The list includes Paper2Poster, MinerU, The AI Scientist, as well as articles, datasets, and frameworks. In fact, this is a complete set of tools for AI support in scientific research. 👉 https://t.me/CodeProgrammer

Automatic translator in Python! We translate a text in a few lines using deep-translator. It supports dozens of languages: from English and Russian to Japanese and Arabic. Install the library:

pip install deep-translator

Example of use:

from deep_translator import GoogleTranslator

text = "Hello, how are you?"
result = GoogleTranslator(source="ru", target="en").translate(text)

print("Original:", text)
print("Translation:", result)

Mass translation of a list:

texts = ["Hello", "What's your name?", "See you later"]
for t in texts:
    print("→", GoogleTranslator(source="ru", target="es").translate(t))

🔥 We get a mini-Google Translate right in Python: you can embed it in a chatbot, use it in notes, or automate work with the API. 🚪 @DataScience4

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🚀Stanford just completed a must-watch for anyone serious about AI: 🎓 “𝗖𝗠𝗘 𝟮𝟵𝟱: 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 & 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀” is now live entirely on YouTube and it’s pure gold. If you’re building your AI career, stop scrolling. This isn’t another surface-level overview. It’s the clearest, most structured intro to LLMs you could follow, straight from the Stanford Autumn 2025 curriculum. 📚 𝗧𝗼𝗽𝗶𝗰𝘀 𝗰𝗼𝘃𝗲𝗿𝗲𝗱 𝗶𝗻𝗰𝗹𝘂𝗱𝗲: • How Transformers actually work (tokenization, attention, embeddings) • Decoding strategies & MoEs • LLM finetuning (LoRA, RLHF, supervised) • Evaluation techniques (LLM-as-a-judge) • Optimization tricks (RoPE, quantization, approximations) • Reasoning & scaling • Agentic workflows (RAG, tool calling) 🧠 My workflow: I usually take the transcripts, feed them into NotebookLM, and once I’ve done the lectures, I replay them during walks or commutes. That combo works wonders for retention. 🎥 Watch these now: - Lecture 1: https://lnkd.in/dDER-qyp - Lecture 2: https://lnkd.in/dk-tGUDm - Lecture 3: https://lnkd.in/drAPdjJY - Lecture 4: https://lnkd.in/e_RSgMz7 - Lecture 5: https://lnkd.in/eivMA9pe - Lecture 6: https://lnkd.in/eYwwwMXn - Lecture 7: https://lnkd.in/eKwkEDXV - Lecture 8: https://lnkd.in/eEWvyfyK - Lecture 9: https://lnkd.in/euiKRGaQ 🗓 Do yourself a favor for this 2026: block 2-3 hours per week / llectue and go through them. If you’re in AI — whether building infra, agents, or apps — this is the foundational course you don’t want to miss. Let’s level up. https://t.me/CodeProgrammer 😅