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Machine Learning

Machine Learning

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

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๐Ÿ“ˆ Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 193 subscribers, ranking 3 365 in the Technologies & Applications category and 227 in the Syria region.

๐Ÿ“Š Audience metrics and dynamics

Since its creation on ะฝะตะฒั–ะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 40 193 subscribers.

According to the latest data from 01 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 355 over the last 30 days and by 21 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.04%. Within the first 24 hours after publication, content typically collects 2.12% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 818 views. Within the first day, a publication typically gains 851 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
  • Thematic interests: Content is focused on key topics such as distance, insidead, gpu, learning, degree.

๐Ÿ“ Description and content policy

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

Thanks to the high frequency of updates (latest data received on 02 July, 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.

40 193
Subscribers
+2124 hours
+857 days
+35530 days
Posts Archive
Want to see real profits from trading? Join the winning side right nowโ€”todayโ€™s trades hit +190 pips in just 1 hour! Ready to
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๐Ÿ“Œ Feature Extraction for Time Series, from Theory to Practice, with Python ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-08-24 | โฑ
๐Ÿ“Œ Feature Extraction for Time Series, from Theory to Practice, with Python ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-08-24 | โฑ๏ธ Read time: 12 min read Hereโ€™s everything you need to know when extracting features for Time Series analysis

๐Ÿ“Œ Building a Command-Line Quiz Application in R ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2025-10-05 | โฑ๏ธ Read time: 6 min read Pra
๐Ÿ“Œ Building a Command-Line Quiz Application in R ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2025-10-05 | โฑ๏ธ Read time: 6 min read Practice control flow, input handling, and functions in R by creating an interactive quiz game.

๐Ÿ“Œ Real-Time Intelligence in Microsoft Fabric: The Ultimate Guide ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2025-10-04 | โฑ๏ธ Read tim
๐Ÿ“Œ Real-Time Intelligence in Microsoft Fabric: The Ultimate Guide ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2025-10-04 | โฑ๏ธ Read time: 21 min read Once upon a time, handling streaming data was considered an avant-garde approach. Since the introduction of relationalโ€ฆ

๐Ÿ“Œ A Simple Framework for RAG Enhanced Visual Question Answering ๐Ÿ—‚ Category: MACHINE LEARNING ๐Ÿ•’ Date: 2024-08-30 | โฑ๏ธ Read
๐Ÿ“Œ A Simple Framework for RAG Enhanced Visual Question Answering ๐Ÿ—‚ Category: MACHINE LEARNING ๐Ÿ•’ Date: 2024-08-30 | โฑ๏ธ Read time: 20 min read Empowering Phi-3.5-vision with Wikipedia knowledge for augmented Visual Question Answering.

๐Ÿ“Œ Deploy Models with AWS SageMaker Endpoints โ€“ Step by Step Implementation ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-08-30 | โฑ
๐Ÿ“Œ Deploy Models with AWS SageMaker Endpoints โ€“ Step by Step Implementation ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-08-30 | โฑ๏ธ Read time: 13 min read A 4-step tutorial on creating a SageMaker endpoint and calling it.

๐Ÿ“Œ Advanced SQL for Data Science ๐Ÿ—‚ Category: ANALYTICS ๐Ÿ•’ Date: 2024-08-24 | โฑ๏ธ Read time: 15 min read Expert techniques to
๐Ÿ“Œ Advanced SQL for Data Science ๐Ÿ—‚ Category: ANALYTICS ๐Ÿ•’ Date: 2024-08-24 | โฑ๏ธ Read time: 15 min read Expert techniques to elevate your analysis

๐Ÿ“Œ Automating ETL to SFTP Server Using Python and SQL ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-08-24 | โฑ๏ธ Read time: 19 min re
๐Ÿ“Œ Automating ETL to SFTP Server Using Python and SQL ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-08-24 | โฑ๏ธ Read time: 19 min read Learn how to automate a daily data transfer process on Windows, from PostgreSQL database toโ€ฆ

๐Ÿ“Œ Solving The Travelling Salesman Problem Using A Genetic Algorithm ๐Ÿ—‚ Category: MACHINE LEARNING ๐Ÿ•’ Date: 2024-08-25 | โฑ๏ธ R
๐Ÿ“Œ Solving The Travelling Salesman Problem Using A Genetic Algorithm ๐Ÿ—‚ Category: MACHINE LEARNING ๐Ÿ•’ Date: 2024-08-25 | โฑ๏ธ Read time: 16 min read An Exploration with Python

๐Ÿ“Œ How to Network as a Data Scientist ๐Ÿ—‚ Category: CAREER ADVICE ๐Ÿ•’ Date: 2024-08-26 | โฑ๏ธ Read time: 8 min read Times are cha
๐Ÿ“Œ How to Network as a Data Scientist ๐Ÿ—‚ Category: CAREER ADVICE ๐Ÿ•’ Date: 2024-08-26 | โฑ๏ธ Read time: 8 min read Times are changing โ€“ if you want to get into data science, you have toโ€ฆ

๐Ÿ“Œ Advanced Retrieval Techniques in a World of 2M Token Context Windows: Part 2 on Re-rankers ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-08-2
๐Ÿ“Œ Advanced Retrieval Techniques in a World of 2M Token Context Windows: Part 2 on Re-rankers ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-08-26 | โฑ๏ธ Read time: 8 min read Exploring RAG techniques to improve retrieval accuracy

Big surprise in our channels on Discord https://discord.gg/PGZku7DrSz ๐Ÿ”” Subscribe now

๐Ÿ“Œ Tackle Complex LLM Decision-Making with Language Agent Tree Search (LATS) & GPT-4o ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-08-26 | โฑ๏ธ R
๐Ÿ“Œ Tackle Complex LLM Decision-Making with Language Agent Tree Search (LATS) & GPT-4o ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-08-26 | โฑ๏ธ Read time: 11 min read Enhancing LLM Decision-Making: Integrating Language Agent Tree Search with GPT-4o for Superior Problem Solving

๐Ÿ“Œ Introducing Markov Decision Processes, Setting up Gymnasium Environments and Solving them via Dynamic Programming Methods
๐Ÿ“Œ Introducing Markov Decision Processes, Setting up Gymnasium Environments and Solving them via Dynamic Programming Methods ๐Ÿ—‚ Category: MACHINE LEARNING ๐Ÿ•’ Date: 2024-08-26 | โฑ๏ธ Read time: 12 min read Dissecting โ€œReinforcement Learningโ€ by Richard S. Sutton with custom Python implementations, Episode II

๐Ÿ“Œ How Can We Continually Adapt Vision-Language Models? ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-08-26 | โฑ๏ธ Read time: 9 min read Exploring
๐Ÿ“Œ How Can We Continually Adapt Vision-Language Models? ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-08-26 | โฑ๏ธ Read time: 9 min read Exploring Continual Learning Strategies for CLIP.

๐Ÿ“Œ How to Achieve Near Human-Level Performance in Chunking for RAGs ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-08-26
๐Ÿ“Œ How to Achieve Near Human-Level Performance in Chunking for RAGs ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-08-26 | โฑ๏ธ Read time: 10 min read The costly yet powerful splitting technique for superior RAG retrieval

๐Ÿ“Œ No Baseline? No Benchmarks? No Biggie! An Experimental Approach to Agile Chatbot Development ๐Ÿ—‚ Category: INNOVATION ๐Ÿ•’ Da
๐Ÿ“Œ No Baseline? No Benchmarks? No Biggie! An Experimental Approach to Agile Chatbot Development ๐Ÿ—‚ Category: INNOVATION ๐Ÿ•’ Date: 2024-08-26 | โฑ๏ธ Read time: 15 min read Lessons learned bringing LLM-based products to production

๐Ÿ“Œ AWS DeepRacer : A Practical Guide to Reducing The Sim2Real Gap โ€“ Part 2 || Training Guide ๐Ÿ—‚ Category: ROBOTICS ๐Ÿ•’ Date: 2
๐Ÿ“Œ AWS DeepRacer : A Practical Guide to Reducing The Sim2Real Gap โ€“ Part 2 || Training Guide ๐Ÿ—‚ Category: ROBOTICS ๐Ÿ•’ Date: 2024-08-26 | โฑ๏ธ Read time: 13 min read This article describes how to train the AWS DeepRacer to drive safely around a trackโ€ฆ

๐Ÿ“Œ Exploring the Strategic Capabilities of LLMs in a Risk Game Setting ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-08-27 | โฑ๏ธ Rea
๐Ÿ“Œ Exploring the Strategic Capabilities of LLMs in a Risk Game Setting ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-08-27 | โฑ๏ธ Read time: 39 min read In a simulated Risk environment, large language models from Anthropic, OpenAI, and Meta showcase distinctโ€ฆ

๐Ÿ“Œ How to Color Polars DataFrame ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-08-27 | โฑ๏ธ Read time: 6 min read Continue working wi
๐Ÿ“Œ How to Color Polars DataFrame ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-08-27 | โฑ๏ธ Read time: 6 min read Continue working with the Polars library while being able to color and style the table