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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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网络: Machine Learning with Python印度4 562教育2 398...

📈 Telegram 频道 Machine Learning with Python 的分析概览

频道 Machine Learning with Python (@codeprogrammer) 英语语言赛道中的是活跃参与者。目前社区聚集了 68 460 名订阅者，在教育类别中位列第 2 398，并在印度地区排名第 4 562 位。

📊 受众指标与增长动态

自 невідомо 创建以来，项目保持高速增长，吸引了 68 460 名订阅者。

根据 02 十一月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 -42，过去 24 小时变化为 -6，整体触达仍然可观。

认证状态： 未认证
互动率 (ER)： 平均受众互动率为 3.87%。内容发布后 24 小时内通常能获得 1.52% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 2 649 次浏览，首日通常累积 1 038 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 8。
主题关注点： 内容集中在 insidead, learning, degree, evaluation, algorithm 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台：
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

凭借高频更新（最新数据采集于 03 十一月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为教育类别中的关键影响点。

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68 460

订阅者

-624 小时

-1037 天

-4230 天

2 649

帖子浏览量

~ 1 03824 小时

~ 1 38148 小时

3.87%

参与率

~ 2

每日帖子数

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68 463

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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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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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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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