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A useful find on GitHub CheatSheets-for-Developers LINK: https://github.com/crescentpartha/CheatSheets-for-Developers This is a huge collection of cheat sheets for a wide variety of technologies:

JavaScript, Python, Git, Docker, SQL, Linux, Regex, and many others.

Conveniently structured — you can quickly find the topic you need. Save it and use it 🔥 👉 @DATASCIENCEN

📚 Let's add a powerful library to our collection of useful tools: **JaidedAI/EasyOCR — an open-source Python library for Optical Character Recognition (OCR) that's easy to use and supports over **80 languages out of the box. ### 🔍 Key Features: 🔸 Extracts text from images and scanned documents — including handwritten notes and unusual fonts 🔸 Supports a wide range of languages like English, Russian, Chinese, Arabic, and more 🔸 Built on PyTorch — uses modern deep learning models (not the old-school Tesseract) 🔸 Simple to integrate into your Python projects ### ✅ Example Usage:

import easyocr

reader = easyocr.Reader(['en', 'ru'])  # Choose supported languages
result = reader.readtext('image.png')

### 📌 Ideal For: ✅ Text extraction from photos, scans, and documents ✅ Embedding OCR capabilities in apps (e.g. automated data entry) 🔗 GitHub: https://github.com/JaidedAI/EasyOCR 👉 Follow us for more: @DataScienceN #Python #OCR #MachineLearning #ComputerVision #EasyOCR

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Topic: Python Script to Convert a Shared ChatGPT Link to PDF – Step-by-Step Guide --- ### Objective In this lesson, we’ll build a Python script that: • Takes a ChatGPT share link (e.g., https://chat.openai.com/share/abc123) • Downloads the HTML content of the chat • Converts it to a PDF file using pdfkit and wkhtmltopdf This is useful for archiving, sharing, or printing ChatGPT conversations in a clean format. --- ### 1. Prerequisites Before starting, you need the following libraries and tools: #### • Install pdfkit and requests

pip install pdfkit requests

#### • Install wkhtmltopdf Download from: [https://wkhtmltopdf.org/downloads.html](https://wkhtmltopdf.org/downloads.html) Make sure to add the path of the installed binary to your system PATH. --- ### 2. Python Script: Convert Shared ChatGPT URL to PDF

import pdfkit
import requests
import os

# Define output filename
output_file = "chatgpt_conversation.pdf"

# ChatGPT shared URL (user input)
chat_url = input("Enter the ChatGPT share URL: ").strip()

# Verify the URL format
if not chat_url.startswith("https://chat.openai.com/share/"):
    print("Invalid URL. Must start with https://chat.openai.com/share/")
    exit()

try:
    # Download HTML content
    response = requests.get(chat_url)
    if response.status_code != 200:
        raise Exception(f"Failed to load the chat: {response.status_code}")

    html_content = response.text

    # Save HTML to temporary file
    with open("temp_chat.html", "w", encoding="utf-8") as f:
        f.write(html_content)

    # Convert HTML to PDF
    pdfkit.from_file("temp_chat.html", output_file)

    print(f"\n✅ PDF saved as: {output_file}")

    # Optional: remove temp file
    os.remove("temp_chat.html")

except Exception as e:
    print(f"❌ Error: {e}")

--- ### 3. Notes • This approach works only if the shared page is publicly accessible (which ChatGPT share links are). • The PDF output will contain the web page version, including theme and layout. • You can customize the PDF output using pdfkit options (like page size, margins, etc.). --- ### 4. Optional Enhancements • Add GUI with Tkinter • Accept multiple URLs • Add PDF metadata (title, author, etc.) • Add support for offline rendering using BeautifulSoup to clean content --- ### Exercise • Try converting multiple ChatGPT share links to PDF • Customize the styling with your own CSS • Add a timestamp or watermark to the PDF --- #Python #ChatGPT #PDF #WebScraping #Automation #pdfkit #tkinter https://t.me/CodeProgrammer ✅

Found a useful resource for learning Python from scratch This is a free book Think Python. Everything is clearly structured - from basic variables to classes, OOP and recursion Formatted as Jupyter notebooks: you can read the text, run code and complete tasks - all in one place. Directly in the browser, via Colab The notebooks with solutions can be downloaded from this repo on GitHub

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This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

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This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

🔥 The coolest AI bot on Telegram 💢 Completely free and knows everything, from simple questions to complex problems. ☕️ Helps you with anything in the easiest and fastest way possible. ♨️ You can even choose girlfriend or boyfriend mode and chat as if you’re talking to a real person 😋 💵 Includes weekly and monthly airdrops!❗️ 😵‍💫 Bot ID: @chatgpt_officialbot 💎 The best part is, even group admins can use it right inside their groups! ✨ 📺 Try now: • Type FunFact! for a jaw-dropping AI trivia. • Type RecipePlease! for a quick, tasty meal idea. • Type JokeTime! for an instant laugh. Or just say Surprise me! and I'll pick something awesome for you. 🤖✨

. Gemini CLI Google has launched Gemini CLI, a new AI-powered tool that runs directly through the command-line interface (Terminal) 📃. The tool is designed to be a powerful assistant for developers and represents a significant step forward in seamlessly integrating artificial intelligence into development environments🧠.

✅ Key Features of Gemini CLI: Powered by the advanced Gemini 1.5 Pro model🛸. Completely free during the current phase🆓. Generous usage limits: up to 60 requests per minute and 1000 requests per day – more than enough for most personal uses🛸. 👨‍💻Advanced Technical Features: Extended context window of up to 1 million tokens, enabling it to handle long texts and complex projects efficiently💻. Multimodal support: understands and works with code, images, PDFs, and user interface (UI) graphics✨. Built-in Google Search to enhance AI responses and provide accurate answers🔎. Automated pull request (PR) handling in software projects, including detecting and executing PRs automatically✅.

While other AI tools require monthly subscriptions ranging from $10 to $20, Google is offering this tool completely free 🤝. Google really is generous🎓. 🖥 GitHub

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10 GitHub repos to build a career in AI engineering: (100% free step-by-step roadmap) 1️⃣ ML for Beginners by Microsoft A 12-week project-based curriculum that teaches classical ML using Scikit-learn on real-world datasets. Includes quizzes, lessons, and hands-on projects, with some videos. GitHub repo → https://lnkd.in/dCxStbYv 2️⃣ AI for Beginners by Microsoft This repo covers neural networks, NLP, CV, transformers, ethics & more. There are hands-on labs in PyTorch & TensorFlow using Jupyter. Beginner-friendly, project-based, and full of real-world apps. GitHub repo → https://lnkd.in/dwS5Jk9E 3️⃣ Neural Networks: Zero to Hero Now that you’ve grasped the foundations of AI/ML, it’s time to dive deeper. This repo by Andrej Karpathy builds modern deep learning systems from scratch, including GPTs. GitHub repo → https://lnkd.in/dXAQWucq 4️⃣ DL Paper Implementations So far, you have learned the fundamentals of AI, ML, and DL. Now study how the best architectures work. This repo covers well-documented PyTorch implementations of 60+ research papers on Transformers, GANs, Diffusion models, etc. GitHub repo → https://lnkd.in/dTrtDrvs 5️⃣ Made With ML Now it’s time to learn how to go from notebooks to production. Made With ML teaches you how to design, develop, deploy, and iterate on real-world ML systems using MLOps, CI/CD, and best practices. GitHub repo → https://lnkd.in/dYyjjBGb 6️⃣ Hands-on LLMs - You've built neural nets. - You've explored GPTs and LLMs. Now apply them. This is a visually rich repo that covers everything about LLMs, like tokenization, fine-tuning, RAG, etc. GitHub repo → https://lnkd.in/dh2FwYFe 7️⃣ Advanced RAG Techniques Hands-on LLMs will give you a good grasp of RAG systems. Now learn advanced RAG techniques. This repo covers 30+ methods to make RAG systems faster, smarter, and accurate, like HyDE, GraphRAG, etc. GitHub repo → https://lnkd.in/dBKxtX-D 8️⃣ AI Agents for Beginners by Microsoft After diving into LLMs and mastering RAG, learn how to build AI agents. This hands-on course covers building AI agents using frameworks like AutoGen. GitHub repo → https://lnkd.in/dbFeuznE 9️⃣ Agents Towards Production The above course will teach what AI agents are. Next, learn how to ship them. This is a practical playbook for building agents covering memory, orchestration, deployment, security & more. GitHub repo → https://lnkd.in/dcwmamSb 🔟 AI Engg. Hub To truly master LLMs, RAG, and AI agents, you need projects. This covers 70+ real-world examples, tutorials, and agent app you can build, adapt, and ship. GitHub repo → https://lnkd.in/geMYm3b6

#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers

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