Code With Python
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This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
Admin: @HusseinSheikho || @Hussein_Sheikho
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A Complete Course to Learn Robotics and Perception

Notebook-based book "Introduction to Robotics and Perception" by Frank Dellaert and Seth Hutchinson

github.com/gtbook/robotics

roboticsbook.org/intro.html

#Robotics #Perception #AI #DeepLearning #ComputerVision #RoboticsCourse #MachineLearning #Education #RoboticsResearch #GitHub


⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
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Preparing for Data Science Interviews With LeetCode FAQs.pdf
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🔖 Preparing for Data Science Interviews
📝 With LeetCode FAQs

👨🏻‍💻 If you're aiming for top tech companies like Google or Amazon, it's natural to feel overwhelmed and unsure where to begin. I’ve been there too—facing tons of questions without a clear roadmap of what to study or when.

✏️ That’s why I decided to create a step-by-step plan for myself. In this guide, I’ve compiled the most frequently asked LeetCode questions, complete with solutions, techniques, and patterns to help you master the kinds of problems that big companies love to ask.

Whether you're just starting out or sharpening your interview skills, this resource is designed to give you clarity, focus, and confidence.

📂 Stay tuned — I’ll be sharing the full file soon!

#DataScience #InterviewPrep #LeetCode #CodingInterview #TechInterviews #GoogleInterview #AmazonInterview #Python #MachineLearning #AI #CareerTips


✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk

📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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Python MarkItDown: Convert Documents Into LLM-Ready Markdown

📖 Get started with Python MarkItDown to turn PDFs, Office files, images, and URLs into clean, LLM-ready Markdown in seconds.

🏷️ #intermediate #ai #tools
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Forwarded from Machine Learning
In Python, building AI-powered Telegram bots unlocks massive potential for image generation, processing, and automation—master this to create viral tools and ace full-stack interviews! 🤖

# Basic Bot Setup - The foundation (PTB v20+ Async)
from telegram.ext import Application, CommandHandler, MessageHandler, filters

async def start(update, context):
await update.message.reply_text(
" AI Image Bot Active!\n"
"/generate - Create images from text\n"
"/enhance - Improve photo quality\n"
"/help - Full command list"
)

app = Application.builder().token("YOUR_BOT_TOKEN").build()
app.add_handler(CommandHandler("start", start))
app.run_polling()


# Image Generation - DALL-E Integration (OpenAI)
import openai
from telegram.ext import ContextTypes

openai.api_key = os.getenv("OPENAI_API_KEY")

async def generate(update: Update, context: ContextTypes.DEFAULT_TYPE):
if not context.args:
await update.message.reply_text(" Usage: /generate cute robot astronaut")
return

prompt = " ".join(context.args)
try:
response = openai.Image.create(
prompt=prompt,
n=1,
size="1024x1024"
)
await update.message.reply_photo(
photo=response['data'][0]['url'],
caption=f"🎨 Generated: *{prompt}*",
parse_mode="Markdown"
)
except Exception as e:
await update.message.reply_text(f"🔥 Error: {str(e)}")

app.add_handler(CommandHandler("generate", generate))


Learn more: https://hackmd.io/@husseinsheikho/building-AI-powered-Telegram-bots

#Python #TelegramBot #AI #ImageGeneration #StableDiffusion #OpenAI #MachineLearning #CodingInterview #FullStack #Chatbots #DeepLearning #ComputerVision #Programming #TechJobs #DeveloperTips #CareerGrowth #CloudComputing #Docker #APIs #Python3 #Productivity #TechTips


https://t.me/DataScienceM 🦾
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#YOLOv8 #ComputerVision #FireDetection #Python #AI #Safety

Lesson: Real-Time Fire Detection in an Industrial Facility with YOLOv8 and Alarm System

This tutorial guides you through building a computer vision project from scratch. We will use the YOLOv8 model to detect fire in a video feed from an industrial setting and trigger an alarm sound upon detection.

---

#Step 1: Project Setup and Dependencies

First, we need to install the necessary Python libraries. We'll use ultralytics for the YOLOv8 model, opencv-python for video processing, and playsound to trigger our alarm. Open your terminal or command prompt and run the following command:

pip install ultralytics opencv-python playsound

After installation, create a Python file (e.g., fire_detector.py) and import these libraries.

import cv2
from ultralytics import YOLO
from playsound import playsound
import threading

# Hashtags: #Setup #Python #OpenCV #YOLOv8


---

#Step 2: Load the Model and Prepare the Alarm System

We will load a pre-trained YOLOv8 model. For a real-world application, you must train a custom model on a dataset of fire and smoke images. For this example, we will write the code assuming you have a custom model named fire_model.pt that knows how to detect 'fire'.

You also need an alarm sound file (e.g., alarm.wav) in the same directory as your script.

# Load your custom-trained YOLOv8 model
# IMPORTANT: The standard YOLOv8 models do not detect 'fire'.
# You must train your own model on a fire dataset.
model = YOLO('fire_model.pt') # Replace with your custom model path

# Path to your alarm sound file
ALARM_SOUND_PATH = "alarm.wav"

# A flag to ensure the alarm plays only once per detection event
alarm_on = False

def play_alarm():
"""Plays the alarm sound in a separate thread."""
global alarm_on
print("ALARM: Fire Detected!")
playsound(ALARM_SOUND_PATH)
alarm_on = False # Reset alarm flag after sound finishes

# Hashtags: #AIModel #AlarmSystem #SafetyFirst


---

#Step 3: Main Loop for Video Processing and Detection

This is the core of our application. We will open a video file, read it frame by frame, and pass each frame to our YOLOv8 model for inference. If the model detects 'fire' with a certain confidence, we will draw a bounding box around it and trigger the alarm.

Create a video file named industrial_video.mp4 or use your own video source.
1
Build a Python MCP Client to Test Servers From Your Terminal

📖 Follow this Python project to build an MCP client that discovers MCP server capabilities and feeds an AI-powered chat with tool calls.

🏷️ #intermediate #ai #projects
1
Quiz: Python MarkItDown: Convert Documents Into LLM-Ready Markdown

📖 Practice MarkItDown basics. Convert PDFs, Word documents, Excel documents, and HTML documents to Markdown. Try the quiz.

🏷️ #intermediate #ai #tools
How to Use Google's Gemini CLI for AI Code Assistance

📖 Learn how to use Gemini CLI to bring Google's AI-powered coding assistance directly into your terminal to help you analyze and fix code.

🏷️ #intermediate #ai #tools
Quiz: Build a Python MCP Client to Test Servers From Your Terminal

📖 Learn how to create a Python MCP client, start an AI-powered chat session, and run it from the command line. Check your understanding.

🏷️ #intermediate #ai #projects
3
Quiz: How to Use Google's Gemini CLI for AI Code Assistance

📖 Learn how to install, authenticate, and safely use the Gemini CLI to interact with Google's Gemini models.

🏷️ #intermediate #ai #tools
3
LlamaIndex in Python: A RAG Guide With Examples

📖 Learn how to set up LlamaIndex, choose an LLM, load your data, build and persist an index, and run queries to get grounded, reliable answers with examples.

🏷️ #intermediate #ai
4
Quiz: LlamaIndex in Python: A RAG Guide With Examples

📖 Take this Python LlamaIndex quiz to test your understanding of index persistence, reloading, and performance gains in RAG applications.

🏷️ #intermediate #ai
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