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

Сітка:Machine Learning with Python Індія4 731 Освіта2 365...

📈 Аналітичний огляд Telegram-каналу Machine Learning with Python

Канал Machine Learning with Python (@codeprogrammer) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 68 138 підписників, посідаючи 2 365 місце в категорії Освіта та 4 731 місце у регіоні Індія.

📊 Показники аудиторії та динаміка

З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 68 138 підписників.

За останніми даними від 31 серпня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 80, а за останні 24 години на 1, загальне охоплення залишається високим.

Статус верифікації: Не верифікований
Рівень залученості (ER): Середній показник залученості аудиторії становить 4.09%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.54% реакцій від загальної кількості підписників.
Охоплення публікацій: В середньому кожен допис отримує 2 784 переглядів. Протягом першої доби публікація в середньому набирає 1 052 переглядів.
Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 5.
Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як insidead, learning, degree, evaluation, algorithm.

📝 Опис та контентна політика

Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Завдяки високій частоті оновлень (останні дані отримано 01 вересня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Освіта.

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68 138

Підписники

+124 години

-97 днів

+8030 день

2 784

Перегляди допису

~ 1 05224 години

~ 1 13048 годин

4.09%

Коефіцієнт залучення

~ 2

Дописів на день

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Where are you from? Put the flag of your country

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Machine learning and deep learning ✅@Machine_learn Large language Model Git 🔺https://t.me/deep_learning_proj

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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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The 2025 MIT deep learning course is excellent, covering neural networks, CNNs, RNNs, and LLMs. You build three projects for hands-on experience as part of the course. It is entirely free. Highly recommended for beginners. Enroll Free: https://introtodeeplearning.com/

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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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AI vs ML vs Deep Learning vs Generative AI

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