Data Science Jupyter Notebooks
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Explore the world of Data Science through Jupyter Notebooksβ€”insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
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πŸš€ New Tutorial: Automatic Number Plate Recognition (ANPR) with YOLOv11 + GPT-4o-mini!


This hands-on tutorial shows you how to combine the real-time detection power of YOLOv11 with the language understanding of GPT-4o-mini to build a smart, high-accuracy ANPR system! From setup to smart prompt engineering, everything is covered step-by-step. πŸš—πŸ’‘

🎯 Key Highlights:
βœ… YOLOv11 + GPT-4o-mini = High-precision number plate recognition
βœ… Real-time video processing in Google Colab
βœ… Smart prompt engineering for enhanced OCR performance

πŸ“’ A must-watch if you're into computer vision, deep learning, or OpenAI integrations!


πŸ”— Colab Notebook
▢️ Watch on YouTube


#YOLOv11 #GPT4o #OpenAI #ANPR #OCR #ComputerVision #DeepLearning #AI #DataScience #Python #Ultralytics #MachineLearning #Colab #NumberPlateRecognition

πŸ” By : https://t.me/DataScienceN
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python_basics.pdf
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πŸš€ Master Python with Ease!

I've just compiled a set of clean and powerful Python Cheat Sheets to help beginners and intermediates speed up their coding workflow.

Whether you're brushing up on the basics or diving into data science, these sheets will save you time and boost your productivity.

πŸ“Œ Topics Covered:
Python Basics
Jupyter Notebook Tips
Importing Libraries
NumPy Essentials
Pandas Overview

Perfect for students, developers, and anyone looking to keep essential Python knowledge at their fingertips.

#Python #CheatSheets #PythonTips #DataScience #JupyterNotebook #NumPy #Pandas #MachineLearning #AI #CodingTips #PythonForBeginners

🌟 Join the communities:
βœ‰οΈ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk

πŸ“± Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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πŸ’£ China's Alibaba company has released a new competitor for Cursor and Windsurf!

πŸ‘¨πŸ»β€πŸ’» Its name is Qoder, an AI IDE that thinks, plans, writes code, and executes it by itself so you can build software more easily.

✏️ Its interface is also very similar to Cursor; internal chat, code autocomplete, Agent Workflow, and support for MCP.

⬅️ What is Qoder's main focus? Context Engineering, and it is entirely built on that; meaning:

βœ… It deeply understands the project, structure, and codebase.

βœ… It builds persistent memory from past interactions.

βœ… It assigns tasks to the best possible AI model by itself.

⬅️ Two impressive features that really stand out:

1⃣ Quest Mode
⬅️ You just write and hand over the project specifications or the task you want, then go on with your other work, and later you receive the results. That means asynchronous coding without you having to oversee it.

2⃣ Repo Wiki
⬅️ It automatically generates documentation, architectural explanations, and project structure for the entire project.

β”Œ πŸ₯΅ Qoder
β”œ 🌎 Website
β””
πŸ“„ Documentation

🌐 #Data_Science #DataScience
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https://t.me/DataScienceN
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πŸ”° New version of Colab; Data Scientists' Partner
πŸ”ƒ Major update for Google Colab!

πŸ‘¨πŸ»β€πŸ’» From now on, you have a real AI assistant inside your notebook, not just a code completion tool!

▢️ This AI assistant is directly integrated inside Colab and Colab Enterprise (within Vertex AI and BigQuery), and it basically acts like a teammate and coding partner.


πŸ€– What exactly does this AI assistant do for you?

1⃣ It handles the entire workflow by itself!
🏷 You just need to tell it the goal, for example: "Build a model that predicts people's income level based on a BigQuery table." Then it plans a multi-step program, cleans the data, creates features, builds the model, and trains it.

πŸ”’ It writes code for every task!
🏷For example: it can create a chart, manage the cloud environment, perform causal analysis. You just have to ask.

πŸ”’ It finds and fixes errors!
🏷 If a cell throws an error, it explains the cause, provides a corrected version of the code, and even shows a diff so you can approve it.

βž– βž– βž–

🎯 What is its goal?

βœ… Professionals work much faster.

βœ… Beginners learn more easily.

βœ… The entire data science process from idea to final model becomes faster, cleaner, and less error-prone.

➑️ AI First Colab Notebooks
➑️ AI First Colab Notebooks

🌐 #Data_Science #DataScience
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https://t.me/DataScienceN
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πŸ”₯ Trending Repository: Fast-F1

πŸ“ Description: FastF1 is a python package for accessing and analyzing Formula 1 results, schedules, timing data and telemetry

πŸ”— Repository URL: https://github.com/theOehrly/Fast-F1

🌐 Website: https://docs.fastf1.dev

πŸ“– Readme: https://github.com/theOehrly/Fast-F1#readme

πŸ“Š Statistics:
🌟 Stars: 3.9K stars
πŸ‘€ Watchers: 48
🍴 Forks: 377 forks

πŸ’» Programming Languages: Python

🏷️ Related Topics:
#formula1 #datascience #motorsport


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🧠 By: https://t.me/DataScienceM