Some projects don’t scream for attention — they quietly change the way we learn🧑‍🎓.
Python Data Science Handbook is one of them📖.


🧑‍🎓 What is it?

A powerful open-source GitHub repository by Jake VanderPlas.
It turns his bestselling book into interactive Jupyter notebooks that teach:

🧮 NumPy → fast array operations

📊 Pandas → data cleaning & analysis

📈 Matplotlib → clear visualizations

🤖 Scikit-learn → machine learning made simple

🛸 IPython → efficient coding environment

🎚️Run everything instantly via Binder or Google Colab — no setup needed⚙.

Don’t chase complexity... Start with clarity.

🖥 GitHub

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✈️ Our Telegram channels: https://t.iss.one/addlist/0f6vfFbEMdAwODBk

📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Imagine an AI assistant for data scientists that builds an entire Jupyter notebook on request

Data Copilot - is like Cursor, but it is designed for working with data📊

Imports Dataset, cleans it, teaches models, tests it - all in one pass✅.

And yes, he is completely open-source 🤝.

📝 Documentation

🖥 GitHub
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✈️ Our Telegram channels⬅️

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4️⃣In this repository… you don’t just understand — you experience it😱.

🔗Machine-Learning-Projects by Aswin S isn’t just a collection of code👨‍💻.
It’s a hands-on learning environment powered by Jupyter Notebooks📔, designed to teach you how to apply machine learning algorithms to real-world data📱.

🛸 What’s inside?

A curated set of practical projects that turn theory into action:

🖼Predictive modeling for Black Friday and BigMart sales

🕯Time series forecasting for airline passenger trends

🧬Breast cancer detection from medical datasets.

💳Credit card fraud detection.

🏠Boston housing price prediction.

😳Face recognition using OpenCV.


Each project is built with clarity and purpose👨‍💻.
You can run everything directly in Google Colab — no setup required⚙.


🖥 GitHub

Start with a notebook… and walk away with real skills🏹.

✈️ Our Telegram channels⬅️

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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.iss.one/addlist/8_rRW2scgfRhOTc0

✅ https://t.iss.one/Codeprogrammer

🐍Looking to get started with Deep Learning using PyTorch?

This well-structured GitHub repository is a goldmine for beginners who want to learn PyTorch with hands-on examples and clear explanations📖.

🗂 What’s Inside?

🈂 Jupyter Notebooks with interactive code.
🧠 Step-by-step tutorials on Tensors, Autograd, and Neural Networks.
🖼 Real-world mini-projects like image classification.
⌛ Practical guides on using GPU with PyTorch.
✅ Beginner-friendly but also great for revision.

💡If you're serious about learning AI, this is one of the best free resources to kick off your journey🤝.

🖥 GitHub

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Found a cool tool for those who work with Pandas🐻.

You can now automatically profile a DataFrame in Jupyter with Pandas AutoProfiler.

The output includes column distribution, summary statistics, data on null values, code export, etc📊.

Everything works automatically - no need to write additional code🤝.

🖥 GitHub

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Have you ever opened a GitHub repository and wished someone would explain everything step by step?

🖥 Code2Tutorial is an amazing open-source tool that uses artificial intelligence to transform any software project on GitHub into an organized and clear interactive guide, as if you were reading documentation written specifically for you by the project developer📝.

🖥 Just paste the repository link and prepare for a comprehensive understanding journey: from the project structure, to the functions of each file, and the execution sequence🛸.

Don't let a unique project pass you by... Understand it, learn from it, and go ahead and write your next lines with confidence🤝.

🖥 Source Code

🌐 Tool Link

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👩‍💻 The best repository for learning artificial intelligence practically🎓!

📌 The official GitHub repository for the book
Hands-On Machine Learning (2nd Edition)

📔 Contains Jupyter notebooks covering:

⚪️Machine Learning Principles (Linear Models, Decision Trees, SVM).
⚪️Unsupervised Algorithms (Clustering, PCA).
⚪️Advanced Neural Networks (ANN, CNN, RNN, LSTM).
⚪️How Transformers Work.
⚪️Reinforcement Learning.
⚪️Building and Training Models with TensorFlow 2 & Keras.
⚪️Deploying and Scaling Models to Production Environments.
⚪️Data Science Tools (Pandas, NumPy, Matplotlib, Scikit-Learn).

🖱️Free - Comprehensive - Updated
🖱️Suitable for students, developers

🖥 GitHub

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