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This channel is for Programmers, Coders, Software Engineers.

1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning

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Numpy - Arithmetic Operations

πŸ’¬ Tags:
#Numpy

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βœ‰οΈ A comprehensive cheat sheet for Python for Data Science!

πŸ’¬ Tags: #Datacamp

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Forwarded from ENG. Hussein Sheikho
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

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Forwarded from Data Science Books
Learning Python, 6th Edition (2025)

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NumPy Practical Examples: Useful Techniques

πŸ”° Master #NumPy by diving into compelling examples. You will read data into an array, analyze structured arrays for reconciliation, efficiently chart your analysis, and transform a custom function into a vectorized function.

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πŸ‘Ύ Agentic RAG Survey

A guide exploring agent-enhanced #RAG systems. This repository demonstrates single and multi-agent RAG implementations using #LangGraph, with practical examples for building intelligent applications.

Explore the evolution of RAG πŸ€–
https://github.com/asinghcsu/AgenticRAG-Survey

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πŸ”° How to Work With Polars LazyFrames

In this tutorial, you'll gain an understanding of the principles behind #Polars LazyFrames. You'll also learn why using #LazyFrames is often the preferred option over more traditional #DataFrames

Read: https://realpython.com/polars-lazyframe/

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10 Python One-Liners That Will Boost Your Data Preparation Workflow

Data preparation is a step within the data project lifecycle where we prepare the raw data for subsequent processes, such as data analysis and machine learning modeling. Data preparation can quite literally make or break your data project, as inadequate preparation will produce lousy output.

Given the importance of data preparation, we need a proper methodology for it. That’s why this article will explore how a simple one-liner Python code can boost your data preparation workflow.


Read: https://machinelearningmastery.com/10-python-one-liners-that-will-boost-your-data-preparation-workflow/

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πŸ”₯ MIT has updated its famous course 6.S191: Introduction to Deep Learning.

The program covers topics of #NLP, #CV, #LLM and the use of technology in medicine, offering a full cycle of training - from theory to practical classes using current versions of libraries.

The course is designed even for beginners: if you know how to take derivatives and multiply matrices, everything else will be explained in the process.

The lectures are released for free on YouTube and the #MIT platform on Mondays, with the first one already available

.

All slides, #code and additional materials can be found at the link provided.

πŸ“Œ Fresh lecture : https://youtu.be/alfdI7S6wCY?si=6682DD2LlFwmghew

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Numpy @CodeProgrammer.pdf
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πŸ³οΈβ€πŸŒˆ "NumPy Library" Tutorial

πŸ‘¨πŸ»β€πŸ’» For the past few days, I've been busy preparing this comprehensive tutorial on the NumPy library for data science, trying to cover all the tips and tricks of this library.

βœ… Why is this booklet different? Because it is not written based on just theoretical concepts, but is the result of my own experiences and learning. It has real and practical examples that will help you better understand #NumPy concepts and use them in your projects.πŸ’―

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πŸ‘©β€πŸ’» Prompt Engineering: A Practical Example

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Link: https://realpython.com/practical-prompt-engineering/

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πŸ’‘ Here is a useful cheat sheet for KNNs!

#CheatSheet #KNNs #DataScience

https://t.iss.one/DataScienceM πŸ–•
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Google Cloud just recently released "The PyTorch developer's guide to JAX fundamentals".

Contains a side-by-side implementation of a training loop in both #Pytorch and #JAX + Flax NNX sor those interested in exploring the JAX world in familiar terms.

Link: https://cloud.google.com/blog/products/ai-machine-learning/guide-to-jax-for-pytorch-developers

https://t.iss.one/DataScienceM
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