Machine Learning with Python

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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

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Machine Learning with Python (@codeprogrammer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 67 816 obunachidan iborat bo'lib, Taʼlim toifasida 2 417-o'rinni va Hindiston mintaqasida 5 033-o'rinni egallagan.

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невідомо sanasidan buyon loyiha tez o‘sib, 67 816 obunachiga ega bo‘ldi.

10 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 49 ga, so‘nggi 24 soatda esa -10 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

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Jalb etish (ER): Auditoriya o‘rtacha 2.61% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.40% ini tashkil etuvchi reaksiyalarni to‘playdi.
Post qamrovi: Har bir post o‘rtacha 1 770 marta ko‘riladi; birinchi sutkada odatda 1 628 ta ko‘rish yig‘iladi.
Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 7 ta reaksiya keladi.
Tematik yo‘nalishlar: Kontent insidead, learning, degree, evaluation, algorithm kabi asosiy mavzularga jamlangan.

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Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 11 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

67 816

Obunachilar

-1024 soatlar

-17 kunlar

+4930 kunlar

1 770

Post ko'rishlar

~ 1 62824 soatlar

~ 2 19748 soatlar

2.61%

Muloqot nisbati

~ 3

Kuniga postlar

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Postlar arxiv

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"Machine Learning & LLMs for Beginners" Don't miss these 2 books of 100-pages. Both are #FREE to read. 🌟 The Hundred-Page Machine Learning Book: themlbook.com/wiki/doku.php 🌟 The Hundred-Page Language Model Book: thelmbook.com

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"Data Structures & Algorithms using Python" This book of 222 pages implements all types of #DATASTRUCTURES and #ALGORITHMS. And it's 💯 #FREE. Download Free: https://donsheehy.github.io/datastructures/fullbook.pdf By: https://t.me/DataScience4

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Stanford’s Machine Learning - by Andrew Ng A complete lecture notes of 227 pages. Available Free. Download the notes: cs229.stanford.edu/main_notes.pdf

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🔗 Machine Learning from Scratch by Danny Friedman

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

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Introduction to Machine Learning Class Notes by Huy Nguyen https://www.cs.cmu.edu/~hn1/documents/machine-learning/notes.pdf

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Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post. https://t.me/DataScienceN

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We have launched a support and donation campaign for our channel in order to improve the content and enrich it with more resources. You can donate to our channel using stars through this post. Thanks to everyone who contributes to improving our content, the benefit will be extended to all members

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Repost from Machine Learning

#MachineLearning Systems — Principles and Practices of Engineering Artificially Intelligent Systems: https://mlsysbook.ai/ open-source textbook focuses on how to design and implement AI systems effectively

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Linear Algebra The 2nd best book on linear algebra with ~1000 practice problems. A MUST for AI & Machine Learning. Completely FREE. Download it: https://www.cs.ox.ac.uk/files/12921/book.pdf

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Exercises in Machine Learning This book contains 75+ exercises Download, read, and practice: arxiv.org/pdf/2206.13446 GitHub Repo: https://github.com/michaelgutmann/ml-pen-and-paper-exercises

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Repost from Machine Learning with Python

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.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

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📄 "Pandas Introduction to Advanced" booklet 👨🏻‍💻 You can't attend a #datascience interview and not be asked about Pandas! But you don't have to memorize all its methods and functions! With this booklet, you'll learn everything you need. ✔️ One of the most useful and interesting combinations is using #Pandas with #AWS Lambda, which can be very useful in real projects.

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👨🏻‍💻 One of the most popular GitHub repositories for "learning and using algorithms in Python" is The Algorithms - Python repo with 196K stars. ✏️ It has a lot of organized and categorized code that you can use to find, read, and run different algorithms. Everything you can think of is here; from simple algorithms like sorting to advanced algorithms for machine learning, artificial intelligence, neural networks, and more. ✅ Why should we use it? 🔢 For learning: If you're looking to learn algorithms in action, this is great. 🔢 For practice: You can take the codes, run them, and modify them to better understand. 🔢 For projects : You can even use the codes here in real-life or academic projects. 🔢 For interviews: If you're preparing for data science interviews, this is full of practical algorithms. ┌ 🏳️‍🌈 The Algorithms - Python └ 🐱 GitHub-Repos

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course lecture on building Transformers from first principles: https://www.dropbox.com/scl/fi/jhfgy8dnnvy5qq385tnms/lectureattentionneuralnetworks.pdf?rlkey=fddnkonsez76mf8bzider3hrv&dl=0 The #PyTorch notebooks also demonstrate how to implement #Transformers from scratch: https://github.com/xbresson/CS52422025/tree/main/labslecture07

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