Data Analytics
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Dive into the world of Data Analytics โ€“ uncover insights, explore trends, and master data-driven decision making.

Admin: @HusseinSheikho || @Hussein_Sheikho
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Numpy_Cheat_Sheet.pdf
4.8 MB
NumPy Cheat Sheet: Data Analysis in Python

This #Python cheat sheet is a quick reference for #NumPy beginners.

Learn more:
https://www.datacamp.com/cheat-sheet/numpy-cheat-sheet-data-analysis-in-python

https://t.me/DataAnalyticsX
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๐Ÿ‘ A fresh deep learning course from MIT is now available publicly

A full-fledged educational course has been published on the university's website: 24 lectures, practical tasks, homework assignments, and a collection of materials for self-study.

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A great opportunity to study deep learning based on the structure of a top university, free of charge and without simplifications โ€” let's learn here.
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tags: #python #deeplearning

โžก @codeprogrammer
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Data Science Roadmap.pdf
15.5 MB
๐Ÿท Comprehensive Data Science Roadmap Notes

โœ… This roadmap is exactly the secret recipe you need to get out of confusion and know how to step-by-step prepare yourself for the job market.

๐Ÿ•ก From mastering Python and SQL to cleaning data and working with cloud tools, which are prerequisites for any project.

๐Ÿ•‘ How to extract real analysis reports and strategies from raw data using statistics and visualization tools.

๐Ÿ•— You will learn everything from machine learning and advanced algorithms to precise model evaluation.

๐Ÿ•™ Get familiar with neural networks, generative artificial intelligence, and language models to have a voice in today's modern world.

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Pandas vs. Polars: A Complete Comparison of Syntax, Speed, and Memory

Need help choosing the right #Python dataframe library? This article compares #Pandas and #Polars to help you decide.

If you've been working with data in Python, you've almost certainly used pandas. It's been the go-to library for data manipulation for over a decade. But recently, Polars has been gaining serious traction. Polars promises to be faster, more memory-efficient, and more intuitive than pandas. But is it worth learning? And how different is it really?

In this article, we'll compare pandas and Polars side-by-side. You'll see performance benchmarks, and learn the syntax differences. By the end, you'll be able to make an informed decision for your next data project.

Read: https://www.kdnuggets.com/pandas-vs-polars-a-complete-comparison-of-syntax-speed-and-memory

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tags: #Python #DataScience #DeepLearning #AI
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Machine Learning in python.pdf
1 MB
Machine Learning in Python (Course Notes)

I just went through an amazing resource on #MachineLearning in #Python by 365 Data Science, and I had to share the key takeaways with you!

Hereโ€™s what youโ€™ll learn:

๐Ÿ”˜ Linear Regression - The foundation of predictive modeling

๐Ÿ”˜ Logistic Regression - Predicting probabilities and classifications

๐Ÿ”˜ Clustering (K-Means, Hierarchical) - Making sense of unstructured data

๐Ÿ”˜ Overfitting vs. Underfitting - The balancing act every ML engineer must master

๐Ÿ”˜ OLS, R-squared, F-test - Key metrics to evaluate your models

https://t.me/CodeProgrammer || Share ๐ŸŒ and Like ๐Ÿ‘
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I found the BEST video explaining how LLMs work ๐Ÿ‘‡

๐Ÿ”— check out the full video here : https://lnkd.in/dvjZS89d

#LLM #ML #AI #Python

By: https://t.me/DataAnalyticsX
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LLM Interview Questions.pdf
71.2 KB
๐Ÿ”– 50 interview questions for LLM

A good warm-up before the interview: 50 questions on Large Language Models in one document. Not in-depth, but as a checklist to test your knowledge โ€” just perfect.

tags: #LLM #ML #python #pytorch

โžก https://t.me/DataAnalyticsX
๐Ÿ”ฅ3โค2
Aโ€“ZDictionaryofData.pdf
1008.6 KB
Data is everywhere. Clarity is rare.โฃ
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Behind every dashboard, SQL query, or machine learning model lies a common challenge โ€” understanding the language of data.โฃ
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The ๐€โ€“๐™ ๐ƒ๐ข๐œ๐ญ๐ข๐จ๐ง๐š๐ซ๐ฒ ๐จ๐Ÿ ๐ƒ๐š๐ญ๐š ๐€๐ง๐š๐ฅ๐ฒ๐ญ๐ข๐œ๐ฌ & ๐๐ฎ๐ฌ๐ข๐ง๐ž๐ฌ๐ฌ ๐ˆ๐ง๐ญ๐ž๐ฅ๐ฅ๐ข๐ ๐ž๐ง๐œ๐ž brings together 500+ essential terms across SQL, Python, Power BI, Excel, Statistics, and Machine Learning in one structured reference. โฃ
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This is the layer many professionals underestimate.โฃ
Not tools. Not dashboards.โฃ
But the ability to understand, interpret, and communicate concepts with precision.โฃ
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๐–๐ก๐š๐ญ ๐ฆ๐š๐ค๐ž๐ฌ ๐ญ๐ก๐ข๐ฌ ๐ฏ๐š๐ฅ๐ฎ๐š๐›๐ฅ๐ž:โฃ
- Clear definitions without unnecessary complexityโฃ
- Concepts connected across tools and domainsโฃ
- Coverage from foundational terms to advanced analytics conceptsโฃ
- Useful for both technical execution and business communicationโฃ
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๐–๐ก๐ž๐ซ๐ž ๐ญ๐ก๐ข๐ฌ ๐›๐ž๐œ๐จ๐ฆ๐ž๐ฌ ๐ข๐ฆ๐ฉ๐š๐œ๐ญ๐Ÿ๐ฎ๐ฅ:โฃ
- During interviews, when explaining concepts matters more than just knowing themโฃ
- In projects, where misinterpreting a term can lead to incorrect insightsโฃ
- In stakeholder discussions, where clarity builds credibilityโฃ
- In learning journeys, where structured understanding accelerates growthโฃ
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๐’๐ญ๐ซ๐จ๐ง๐  ๐๐š๐ญ๐š ๐ฉ๐ซ๐จ๐Ÿ๐ž๐ฌ๐ฌ๐ข๐จ๐ง๐š๐ฅ๐ฌ ๐๐จ๐งโ€™๐ญ ๐ฃ๐ฎ๐ฌ๐ญ ๐ฐ๐จ๐ซ๐ค ๐ฐ๐ข๐ญ๐ก ๐๐š๐ญ๐š. ๐“๐ก๐ž๐ฒ ๐ฌ๐ฉ๐ž๐š๐ค ๐ข๐ญ๐ฌ ๐ฅ๐š๐ง๐ ๐ฎ๐š๐ ๐ž ๐ฐ๐ข๐ญ๐ก ๐œ๐จ๐ง๐Ÿ๐ข๐๐ž๐ง๐œ๐ž.โฃ
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#DataAnalytics #BusinessIntelligence #DataScience #SQL #Python #PowerBI #Excel #MachineLearning #Statistics #DataEngineering #AnalyticsCareer #DataLearning #DataProfessionals #CareerGrowth #InterviewPreparation

https://t.me/DataAnalyticsX
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Forwarded from Machine Learning
๐Ÿ”– 10 Stanford courses on AI and ML โ€” with official pages and all materials

โ–ถ๏ธ CS221: Artificial Intelligence
โ–ถ๏ธ CS229: Machine Learning
โ–ถ๏ธ CS229M: Theory of Machine Learning
โ–ถ๏ธ CS230: Deep Learning
โ–ถ๏ธ CS234: Reinforcement Learning
โ–ถ๏ธ CS224N: Natural Language Processing
โ–ถ๏ธ CS231N: Deep Learning for Computer Vision
โ–ถ๏ธ CME295: Large Language Models
โ–ถ๏ธ CS236: Deep Generative Models
โ–ถ๏ธ CS336: Modeling Language from Scratch

They cover the entire spectrum: classic ML, LLM, and generative models โ€” with theory and practice.

tags: #python #ML #LLM #AI

โžก https://t.me/MachineLearning9
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