Machine Learning

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

Network:Machine Learning with Python Syria232 Technologies & Applications3 398...

📈 Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 100 subscribers, ranking 3 398 in the Technologies & Applications category and 232 in the Syria region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 40 100 subscribers.

According to the latest data from 23 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 379 over the last 30 days and by 30 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 1.92%. Within the first 24 hours after publication, content typically collects 1.16% reactions from the total number of subscribers.
Post reach: On average, each post receives 770 views. Within the first day, a publication typically gains 466 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
Thematic interests: Content is focused on key topics such as distance, insidead, gpu, learning, degree.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho”

Thanks to the high frequency of updates (latest data received on 24 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

40 100

Subscribers

+3024 hours

+337 days

+37930 days

770

Post views

~ 46624 hours

~ 62048 hours

1.92%

Engagement rate

~ 3

Posts per day

Ads index

beta

Posts Archive

40 106

Repost from Machine Learning with Python

💛 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 🌟

40 106

📌 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

40 106

📌 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

40 106

📌 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

40 106

📌 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

40 106

📌 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

40 106

📌 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

40 106

📌 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

40 106

📌 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

40 106

Repost from Machine Learning with Python

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

40 106

📌 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

40 106

📌 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

40 106

📌 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

40 106

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40 106

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

40 106

📌 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

40 106

📌 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

40 106

📌 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

40 106

📌 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