Data Science Machine Learning Data Analysis
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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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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

This real-world project tutorial covers zero-shot and few-shot prompting, delimiters, numbered steps, role prompts, chain-of-thought prompting, and more. Improve your LLM-assisted projects today.

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

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming  #Keras

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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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Machine Learning from Scratch by Danny Friedman

This book is for readers looking to learn new machine learning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or, seeing these derivations might help a reader experienced in modeling understand how different algorithms create the models they do and the advantages and disadvantages of each one.

This book will be most helpful for those with practice in basic modeling. It does not review best practicesβ€”such as feature engineering or balancing response variablesβ€”or discuss in depth when certain models are more appropriate than others. Instead, it focuses on the elements of those models.

🌟 Link: https://dafriedman97.github.io/mlbook/content/introduction.html

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πŸ”– The book that paved the way for me to "data science"!

πŸ‘¨πŸ»β€πŸ’» "Where do I start now?" This was the first and biggest question I faced when I started my Data Science learning journey!

βͺ I was really overwhelmed by the large number of scattered sources, long courses, and specialized books full of heavy terminology. I didn't know how to start and move forward in this direction...

βœ”οΈ But the book Intro to Data Science with Python changed everything for me and gave me a new perspective!

✏️ This book is a complete guide to starting from scratch and is great for both beginners and professionals in this field!! From coding with Python to working with data, visualization, and even AI tools, it explains everything in the simplest and most practical way possible.

πŸ’Έ A great start for anyone looking to learn data science with Python!πŸ‘‡

β”Œ πŸ³οΈβ€πŸŒˆ Intro to Data Science with Python
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πŸ“„ E-book
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🐱 GitHub-Repos

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