ch
Feedback
Machine Learning

Machine Learning

前往频道在 Telegram

Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

显示更多

📈 Telegram 频道 Machine Learning 的分析概览

频道 Machine Learning (@machinelearning9) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 40 100 名订阅者,在 技术与应用 类别中位列第 3 398,并在 叙利亚 地区排名第 232

📊 受众指标与增长动态

невідомо 创建以来,项目保持高速增长,吸引了 40 100 名订阅者。

根据 23 六月, 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 379,过去 24 小时变化为 30,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 1.92%。内容发布后 24 小时内通常能获得 1.16% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 770 次浏览,首日通常累积 466 次浏览。
  • 互动与反馈: 受众积极参与,单帖平均反应数为 3
  • 主题关注点: 内容集中在 distance, insidead, gpu, learning, degree 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:
Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

凭借高频更新(最新数据采集于 24 六月, 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 技术与应用 类别中的关键影响点。

40 100
订阅者
+3024 小时
+337
+37930
帖子存档
💛 Top 10 Best Websites to Learn Machine Learning ⭐️ by [@codeprogrammer] --- 🧠 Google’s ML Course 🔗 https://developers.google.com/machine-learning/crash-course 📈 Kaggle Courses 🔗 https://kaggle.com/learn 🧑‍🎓 Coursera – Andrew Ng’s ML Course 🔗 https://coursera.org/learn/machine-learning ⚡️ Fast.ai 🔗 https://fast.ai 🔧 Scikit-Learn Documentation 🔗 https://scikit-learn.org 📹 TensorFlow Tutorials 🔗 https://tensorflow.org/tutorials 🔥 PyTorch Tutorials 🔗 https://docs.pytorch.org/tutorials/ 🏛️ MIT OpenCourseWare – Machine Learning 🔗 https://ocw.mit.edu/courses/6-867-machine-learning-fall-2006/ ✍️ Towards Data Science (Blog) 🔗 https://towardsdatascience.com --- 💡 Which one are you starting with? Drop a comment below! 👇 #MachineLearning #LearnML #DataScience #AI https://t.me/CodeProgrammer 🌟

📌 Layered Architecture for Building Readable, Robust, and Extensible Apps 🗂 Category: SOFTWARE ENGINEERING 🕒 Date: 2026-01
📌 Layered Architecture for Building Readable, Robust, and Extensible Apps 🗂 Category: SOFTWARE ENGINEERING 🕒 Date: 2026-01-27 | ⏱️ Read time: 11 min read If adding a feature feels like open-heart surgery on your codebase, the problem isn’t bugs,… #DataScience #AI #Python

📌 From Connections to Meaning: Why Heterogeneous Graph Transformers (HGT) Change Demand Forecasting 🗂 Category: DATA SCIENC
📌 From Connections to Meaning: Why Heterogeneous Graph Transformers (HGT) Change Demand Forecasting 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-27 | ⏱️ Read time: 12 min read How relationship-aware graphs turn connected forecasts into operational insight #DataScience #AI #Python

📌 Data Science as Engineering: Foundations, Education, and Professional Identity 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-
📌 Data Science as Engineering: Foundations, Education, and Professional Identity 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-27 | ⏱️ Read time: 15 min read Recognize data science as an engineering practice and structure education accordingly. #DataScience #AI #Python

📌 Going Beyond the Context Window: Recursive Language Models in Action 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-2
📌 Going Beyond the Context Window: Recursive Language Models in Action 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-27 | ⏱️ Read time: 24 min read Explore a practical approach to analysing massive datasets with LLMs #DataScience #AI #Python

📌 How Convolutional Neural Networks Learn Musical Similarity 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-26 | ⏱️ Read tim
📌 How Convolutional Neural Networks Learn Musical Similarity 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-26 | ⏱️ Read time: 13 min read Learning audio embeddings with contrastive learning and deploying them in a real music recommendation app #DataScience #AI #Python

📌 Ray: Distributed Computing For All, Part 2 🗂 Category: PROGRAMMING 🕒 Date: 2026-01-26 | ⏱️ Read time: 11 min read Deploy
📌 Ray: Distributed Computing For All, Part 2 🗂 Category: PROGRAMMING 🕒 Date: 2026-01-26 | ⏱️ Read time: 11 min read Deploying and running Python code on cloud-based clusters #DataScience #AI #Python

📌 How Cursor Actually Indexes Your Codebase 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-26 | ⏱️ Read time: 10 min re
📌 How Cursor Actually Indexes Your Codebase 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-26 | ⏱️ Read time: 10 min read Exploring the RAG pipeline in Cursor that powers code indexing and retrieval for coding agents #DataScience #AI #Python

📌 Causal ML for the Aspiring Data Scientist 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-26 | ⏱️ Read time: 18 min read An
📌 Causal ML for the Aspiring Data Scientist 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-26 | ⏱️ Read time: 18 min read An accessible introduction to causal inference and ML #DataScience #AI #Python

Data Science Interview questions #DeepLearning #AI #MachineLearning #NeuralNetworks #DataScience #DataAnalysis #LLM #InterviewQuestions https://t.me/CodeProgrammer

📌 SAM 3 vs. Specialist Models — A Performance Benchmark 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-25 | ⏱️ Read time: 19
📌 SAM 3 vs. Specialist Models — A Performance Benchmark 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-25 | ⏱️ Read time: 19 min read Why specialized models still hold the 30x speed advantage in production environments #DataScience #AI #Python

📌 Azure ML vs. AWS SageMaker: A Deep Dive into Model Training — Part 1 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-25 | ⏱
📌 Azure ML vs. AWS SageMaker: A Deep Dive into Model Training — Part 1 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-25 | ⏱️ Read time: 11 min read Compare Azure ML and AWS SageMaker for scalable model training, focusing on project setup, permission… #DataScience #AI #Python

📌 Air for Tomorrow: Mapping the Digital Air-Quality Landscape, from Repositories and Data Types to Starter Code 🗂 Category:
📌 Air for Tomorrow: Mapping the Digital Air-Quality Landscape, from Repositories and Data Types to Starter Code 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-24 | ⏱️ Read time: 25 min read Understand air quality: access the available data, interpret data types, and execute starter codes #DataScience #AI #Python

Listen, if you’re tired of those sketchy Forex signals that drain your account faster than your morning coffee, check this ou
Listen, if you’re tired of those sketchy Forex signals that drain your account faster than your morning coffee, check this out. At FREE | Forex Hollywood, we keep it simple: just 1TP and 1SL, no mess, all profit. This week? We nailed +500 PIPS, five days straight. Yep, others lose, we win. Wanna trade smarter, not harder? Join us and see why our analysis and strategy crush the rest. No fluff, just legit gains. Slide into @Forex_Hollywood and start winning today. 🎯 Join FREE | Forex Hollywood #ad InsideAds

Ant AI Automated Sales Robot is an intelligent robot focused on automating lead generation and sales conversion. Its core function simulates human conversation, achieving end-to-end business conversion and easily generating revenue without requiring significant time investment. I. Core Functions: Fully Automated "Lead Generation - Interaction - Conversion" Precise Lead Generation and Human-like Communication: Ant AI is trained on over 20 million real social chat records, enabling it to autonomously identify target customers and build trust through natural conversation, requiring no human intervention. High Conversion Rate Across Multiple Scenarios: Ant AI intelligently recommends high-conversion-rate products based on chat content, guiding customers to complete purchases through platforms such as iFood, Shopee, and Amazon. It also supports other transaction scenarios such as movie ticket purchases and utility bill payments. 24/7 Operation: Ant AI continuously searches for customers and recommends products. You only need to monitor progress via your mobile phone, requiring no additional management time. II. Your Profit Guarantee: Low Risk, High Transparency, Zero Inventory Pressure, Stable Commission Sharing We have established partnerships with platforms such as Shopee and Amazon, which directly provide abundant product sourcing. You don't need to worry about inventory or logistics. After each successful order, the company will charge the merchant a commission and share all profits with you. Earnings are predictable and withdrawals are convenient. Member data shows that each bot can generate $30 to $100 in profit per day. Commission income can be withdrawn to your account at any time, and the settlement process is transparent and open. Low Initial Investment Risk. Bot development and testing incur significant costs. While rental fees are required, in the early stages of the project, the company prioritizes market expansion and brand awareness over short-term profits. If you are interested, please join my Telegram group for more information and leave a message: https://t.me/+lVKtdaI5vcQ1ZDA1

📌 How to Build a Neural Machine Translation System for a Low-Resource Language 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-0
📌 How to Build a Neural Machine Translation System for a Low-Resource Language 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-24 | ⏱️ Read time: 15 min read An introduction to neural machine translation #DataScience #AI #Python

📌 From Transactions to Trends: Predict When a Customer Is About to Stop Buying 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-23
📌 From Transactions to Trends: Predict When a Customer Is About to Stop Buying 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-23 | ⏱️ Read time: 7 min read Customer churn is usually a gradual process, not a sudden event. In this post, we… #DataScience #AI #Python

📌 Why the Sophistication of Your Prompt Correlates Almost Perfectly with the Sophistication of the Response, as Research by
📌 Why the Sophistication of Your Prompt Correlates Almost Perfectly with the Sophistication of the Response, as Research by Anthropic Found 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-23 | ⏱️ Read time: 9 min read How prompt engineering has evolved, examined scientifically; and implications for the future of conversational AI… #DataScience #AI #Python

📌 Achieving 5x Agentic Coding Performance with Few-Shot Prompting 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-23 | ⏱
📌 Achieving 5x Agentic Coding Performance with Few-Shot Prompting 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-23 | ⏱️ Read time: 9 min read Learn to leverage few-shot prompting to increase your LLMs performance #DataScience #AI #Python