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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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Сеть: Machine Learning with PythonИндия4 562Образование2 398...

📈 Аналитический обзор Telegram-канала Machine Learning with Python

Канал Machine Learning with Python (@codeprogrammer) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 68 473 подписчиков, занимая 2 398 место в категории Образование и 4 562 место в регионе Индия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 68 473 подписчиков.

Согласно последним данным от 01 ноября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -24, а за последние 24 часа — -28, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 3.65%. В первые 24 часа после публикации контент обычно набирает 1.44% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 2 501 просмотров. В течение первых суток публикация набирает 987 просмотров.
Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 8.
Тематические интересы: Контент сосредоточен на ключевых темах, таких как insidead, learning, degree, evaluation, algorithm.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Благодаря высокой частоте обновлений (последние данные получены 02 ноября, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.

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68 473

Подписчики

-2824 часа

-1137 дней

-2430 дней

2 501

Просмотры поста

~ 98724 часа

~ 1 37148 часов

3.65%

Коэффициент вовлеченности

~ 2

Постов в день

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Архив постов

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10 GitHub repos to build a career in AI engineering: (100% free step-by-step roadmap) 1️⃣ ML for Beginners by Microsoft A 12-week project-based curriculum that teaches classical ML using Scikit-learn on real-world datasets. Includes quizzes, lessons, and hands-on projects, with some videos. GitHub repo → https://lnkd.in/dCxStbYv 2️⃣ AI for Beginners by Microsoft This repo covers neural networks, NLP, CV, transformers, ethics & more. There are hands-on labs in PyTorch & TensorFlow using Jupyter. Beginner-friendly, project-based, and full of real-world apps. GitHub repo → https://lnkd.in/dwS5Jk9E 3️⃣ Neural Networks: Zero to Hero Now that you’ve grasped the foundations of AI/ML, it’s time to dive deeper. This repo by Andrej Karpathy builds modern deep learning systems from scratch, including GPTs. GitHub repo → https://lnkd.in/dXAQWucq 4️⃣ DL Paper Implementations So far, you have learned the fundamentals of AI, ML, and DL. Now study how the best architectures work. This repo covers well-documented PyTorch implementations of 60+ research papers on Transformers, GANs, Diffusion models, etc. GitHub repo → https://lnkd.in/dTrtDrvs 5️⃣ Made With ML Now it’s time to learn how to go from notebooks to production. Made With ML teaches you how to design, develop, deploy, and iterate on real-world ML systems using MLOps, CI/CD, and best practices. GitHub repo → https://lnkd.in/dYyjjBGb 6️⃣ Hands-on LLMs - You've built neural nets. - You've explored GPTs and LLMs. Now apply them. This is a visually rich repo that covers everything about LLMs, like tokenization, fine-tuning, RAG, etc. GitHub repo → https://lnkd.in/dh2FwYFe 7️⃣ Advanced RAG Techniques Hands-on LLMs will give you a good grasp of RAG systems. Now learn advanced RAG techniques. This repo covers 30+ methods to make RAG systems faster, smarter, and accurate, like HyDE, GraphRAG, etc. GitHub repo → https://lnkd.in/dBKxtX-D 8️⃣ AI Agents for Beginners by Microsoft After diving into LLMs and mastering RAG, learn how to build AI agents. This hands-on course covers building AI agents using frameworks like AutoGen. GitHub repo → https://lnkd.in/dbFeuznE 9️⃣ Agents Towards Production The above course will teach what AI agents are. Next, learn how to ship them. This is a practical playbook for building agents covering memory, orchestration, deployment, security & more. GitHub repo → https://lnkd.in/dcwmamSb 🔟 AI Engg. Hub To truly master LLMs, RAG, and AI agents, you need projects. This covers 70+ real-world examples, tutorials, and agent app you can build, adapt, and ship. GitHub repo → https://lnkd.in/geMYm3b6

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Over the last year, several articles have been written to help candidates prepare for data science technical interviews. These resources cover a wide range of topics including machine learning, SQL, programming, statistics, and probability. 1️⃣ Machine Learning (ML) Interview Types of ML Q&A in Data Science Interview https://shorturl.at/syN37 ML Interview Q&A for Data Scientists https://shorturl.at/HVWY0 Crack the ML Coding Q&A https://shorturl.at/CDW08 Deep Learning Interview Q&A https://shorturl.at/lHPZ6 Top LLMs Interview Q&A https://shorturl.at/wGRSZ Top CV Interview Q&A [Part 1] https://rb.gy/51jcfi Part 2 https://rb.gy/hqgkbg Part 3 https://rb.gy/5z87be 2️⃣ SQL Interview Preparation 13 SQL Statements for 90% of Data Science Tasks https://rb.gy/dkdcl1 SQL Window Functions: Simplifying Complex Queries https://t.ly/EwSlH Ace the SQL Questions in the Technical Interview https://lnkd.in/gNQbYMX9 Unlocking the Power of SQL: How to Ace Top N Problem Questions https://lnkd.in/gvxVwb9n How To Ace the SQL Ratio Problems https://lnkd.in/g6JQqPNA Cracking the SQL Window Function Coding Questions https://lnkd.in/gk5u6hnE SQL & Database Interview Q&A https://lnkd.in/g75DsEfw 6 Free Resources for SQL Interview Preparation https://lnkd.in/ghhiG79Q 3️⃣ Programming Questions Foundations of Data Structures [Part 1] https://lnkd.in/gX_ZcmRq Part 2 https://lnkd.in/gATY4rTT Top Important Python Questions [Conceptual] https://lnkd.in/gJKaNww5 Top Important Python Questions [Data Cleaning and Preprocessing] https://lnkd.in/g-pZBs3A Top Important Python Questions [Machine & Deep Learning] https://lnkd.in/gZwcceWN Python Interview Q&A https://lnkd.in/gcaXc_JE 5 Python Tips for Acing DS Coding Interview https://lnkd.in/gsj_Hddd 4️⃣ Statistics Mastering 5 Statistics Concepts to Boost Success https://lnkd.in/gxEuHiG5 Mastering Hypothesis Testing for Interviews https://lnkd.in/gSBbbmF8 Introduction to A/B Testing https://lnkd.in/g35Jihw6 Statistics Interview Q&A for Data Scientists https://lnkd.in/geHCCt6Q 5️⃣ Probability 15 Probability Concepts to Review [Part 1] https://lnkd.in/g2rK2tQk Part 2 https://lnkd.in/gQhXnKwJ Probability Interview Q&A [Conceptual Questions] https://lnkd.in/g5jyKqsp Probability Interview Q&A [Mathematical Questions] https://lnkd.in/gcWvPhVj 🔜 All links are available in the GitHub repository: https://lnkd.in/djcgcKRT

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This book covers foundational topics within computer vision, with an image processing and machine learning perspective. We want to build the reader’s intuition and so we include many visualizations. The audience is undergraduate and graduate students who are entering the field, but we hope experienced practitioners will find the book valuable as well. Our initial goal was to write a large book that provided a good coverage of the field. Unfortunately, the field of computer vision is just too large for that. So, we decided to write a small book instead, limiting each chapter to no more than five pages. Such a goal forced us to really focus on the important concepts necessary to understand each topic. Writing a short book was perfect because we did not have time to write a long book and you did not have time to read it. Unfortunately, we have failed at that goal, too. Read it online: https://visionbook.mit.edu/

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Top 50 LLM Interview Questions!

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Top 50 LLM Interview Questions! A comprehensive resource that covers traditional ML basics, model architectures, real-world case studies, and theoretical foundations. 👇👇👇👇👇👇

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New to Pandas? Here's a cheat sheet you can download (2025)

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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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A curated collection of Kaggle notebooks showcasing how to build end-to-end AI applications using Hugging Face pretrained models, covering text, speech, image, and vision-language tasks — full tutorials and code available on GitHub: 1️⃣ Text-Based Applications 1.1. Building a Chatbot Using HuggingFace Open Source Models https://lnkd.in/dku3bigK 1.2. Building a Text Translation System using Meta NLLB Open-Source Model https://lnkd.in/dgdjaFds 2️⃣ Speech-Based Applications 2.1. Zero-Shot Audio Classification Using HuggingFace CLAP Open-Source Model https://lnkd.in/dbgQgDyn 2.2. Building & Deploying a Speech Recognition System Using the Whisper Model & Gradio https://lnkd.in/dcbp-8fN 2.3. Building Text-to-Speech Systems Using VITS & ArTST Models https://lnkd.in/dwFcQ_X5 3️⃣ Image-Based Applications 3.1. Step-by-Step Guide to Zero-Shot Image Classification using CLIP Model https://lnkd.in/dnk6epGB 3.2. Building an Object Detection Assistant Application: A Step-by-Step Guide https://lnkd.in/d573SvYV 3.3. Zero-Shot Image Segmentation using Segment Anything Model (SAM) https://lnkd.in/dFavEdHS 3.4. Building Zero-Shot Depth Estimation Application Using DPT Model & Gradio https://lnkd.in/d9jjJu_g 4️⃣ Vision Language Applications 4.1. Building a Visual Question Answering System Using Hugging Face Open-Source Models https://lnkd.in/dHNFaHFV 4.2. Building an Image Captioning System using Salesforce Blip Model https://lnkd.in/dh36iDn9 4.3. Building an Image-to-Text Matching System Using Hugging Face Open-Source Models https://lnkd.in/d7fsJEAF ➡️ You can find the articles and the codes for each article in this GitHub repo: https://lnkd.in/dG5jfBwE

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🐍📰 This tutorial will give you an overview of LangGraph fundamentals through hands-on examples, and the tools needed to build your own LLM workflows and agents in LangGraph Link: https://realpython.com/langgraph-python/

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𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗟𝗶𝗸𝗲 𝗮 𝗣𝗿𝗼 – 𝗔𝗹𝗹-𝗶𝗻-𝗢𝗻𝗲 𝗚𝘂𝗶𝗱𝗲 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 If you're a data engineer, aspiring Spark developer, or someone preparing for big data interviews — this one is for you. I’m sharing a powerful, all-in-one PySpark notes sheet that covers both fundamentals and advanced techniques for real-world usage and interviews. 𝗪𝗵𝗮𝘁'𝘀 𝗶𝗻𝘀𝗶𝗱𝗲? • Spark vs MapReduce • Spark Architecture – Driver, Executors, DAG • RDDs vs DataFrames vs Datasets • SparkContext vs SparkSession • Transformations: map, flatMap, reduceByKey, groupByKey • Optimizations – caching, persisting, skew handling, salting • Joins – Broadcast joins, Shuffle joins • Deployment modes – Cluster vs Client • Real interview-ready Q&A from top use cases • CSV, JSON, Parquet, ORC – Format comparisons • Common commands, schema creation, data filtering, null handling 𝗪𝗵𝗼 𝗶𝘀 𝘁𝗵𝗶𝘀 𝗳𝗼𝗿? Data Engineers, Spark Developers, Data Enthusiasts, and anyone preparing for interviews or working on distributed systems.

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Best Data Science Archive Notes

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