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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 40 145 subscribers, ranking 3 375 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 40 145 subscribers.

According to the latest data from 28 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 378 over the last 30 days and by 7 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.09%. Within the first 24 hours after publication, content typically collects 1.91% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 841 views. Within the first day, a publication typically gains 766 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.

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 29 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.

40 145
Subscribers
+724 hours
+1147 days
+37830 days
Posts Archive
๐Ÿ“Œ A New Method to Detect โ€œConfabulationsโ€ Hallucinated by Large Language Models ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date
๐Ÿ“Œ A New Method to Detect โ€œConfabulationsโ€ Hallucinated by Large Language Models ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-06-25 | โฑ๏ธ Read time: 12 min read By calculating semantic entropy with a second LLM, we can better flag answers as unreliableโ€ฆ

๐Ÿ“Œ Making LLMs Write Better and Better Code for Self-Driving Using LangProp ๐Ÿ—‚ Category: CHATGPT ๐Ÿ•’ Date: 2024-06-25 | โฑ๏ธ Rea
๐Ÿ“Œ Making LLMs Write Better and Better Code for Self-Driving Using LangProp ๐Ÿ—‚ Category: CHATGPT ๐Ÿ•’ Date: 2024-06-25 | โฑ๏ธ Read time: 11 min read Analogy from classical machine learning: LLM (Large Language Model) = optimizer; code = parameters; LangPropโ€ฆ

๐Ÿ“Œ Improving RAG Performance Using Rerankers ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-06-25 | โฑ๏ธ Read time: 11 min
๐Ÿ“Œ Improving RAG Performance Using Rerankers ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-06-25 | โฑ๏ธ Read time: 11 min read A tutorial on using rerankers to improve your RAG pipeline

๐Ÿ“Œ The Intuitive Basics of Optimization ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-06-26 | โฑ๏ธ Read time: 14 min read A gentle in
๐Ÿ“Œ The Intuitive Basics of Optimization ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-06-26 | โฑ๏ธ Read time: 14 min read A gentle introduction to the amazing field of optimization

๐Ÿ“Œ Business Planning with Python โ€“ Revenue Optimization ๐Ÿ—‚ Category: BUSINESS ๐Ÿ•’ Date: 2024-06-26 | โฑ๏ธ Read time: 14 min read
๐Ÿ“Œ Business Planning with Python โ€“ Revenue Optimization ๐Ÿ—‚ Category: BUSINESS ๐Ÿ•’ Date: 2024-06-26 | โฑ๏ธ Read time: 14 min read How can you use data analytics to help small businesses maximize their revenue while maintainingโ€ฆ

๐Ÿ“Œ How Bend Works: A Parallel Programming Language That โ€œFeels Like Python but Scales Like CUDAโ€ ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-0
๐Ÿ“Œ How Bend Works: A Parallel Programming Language That โ€œFeels Like Python but Scales Like CUDAโ€ ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-06-26 | โฑ๏ธ Read time: 26 min read A brief introduction to Lambda Calculus, Interaction Combinators, and how they are used to parallelizeโ€ฆ

๐Ÿ“Œ The Ultimate Guide to Finding Outliers in Your Time-Series Data (Part 2) ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-06-26 | โฑ
๐Ÿ“Œ The Ultimate Guide to Finding Outliers in Your Time-Series Data (Part 2) ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-06-26 | โฑ๏ธ Read time: 1 min read Effective machine learning methods and tools for outlier detection in time-series analysis

๐Ÿ“Œ A Complete Guide to Master Step Functions on AWS ๐Ÿ—‚ Category: SCIENCE AND TECHNOLOGY ๐Ÿ•’ Date: 2024-06-27 | โฑ๏ธ Read time: 1
๐Ÿ“Œ A Complete Guide to Master Step Functions on AWS ๐Ÿ—‚ Category: SCIENCE AND TECHNOLOGY ๐Ÿ•’ Date: 2024-06-27 | โฑ๏ธ Read time: 10 min read Workflow orchestration made easier

๐Ÿ“Œ 3 Challenges to Being a Data Scientist in 2024 ๐Ÿ—‚ Category: CAREER ADVICE ๐Ÿ•’ Date: 2024-06-27 | โฑ๏ธ Read time: 7 min read G
๐Ÿ“Œ 3 Challenges to Being a Data Scientist in 2024 ๐Ÿ—‚ Category: CAREER ADVICE ๐Ÿ•’ Date: 2024-06-27 | โฑ๏ธ Read time: 7 min read Given the current climate, is data science for you?

๐Ÿ“Œ Classification Loss Functions: Intuition and Applications ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-06-27 | โฑ๏ธ Re
๐Ÿ“Œ Classification Loss Functions: Intuition and Applications ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-06-27 | โฑ๏ธ Read time: 9 min read A simpler way to understand derivations of loss functions for classification and when/how to applyโ€ฆ

๐Ÿ“Œ Prompt Engineering: Tips, Approaches, and Future Directions ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-06-27 | โฑ๏ธ Read time:
๐Ÿ“Œ Prompt Engineering: Tips, Approaches, and Future Directions ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-06-27 | โฑ๏ธ Read time: 5 min read Our weekly selection of must-read Editorsโ€™ Picks and original features

๐Ÿ“Œ Understanding Transformers ๐Ÿ—‚ Category: DEEP LEARNING ๐Ÿ•’ Date: 2024-06-27 | โฑ๏ธ Read time: 12 min read A straightforward br
๐Ÿ“Œ Understanding Transformers ๐Ÿ—‚ Category: DEEP LEARNING ๐Ÿ•’ Date: 2024-06-27 | โฑ๏ธ Read time: 12 min read A straightforward breakdown of โ€œAttention is All You Needโ€ยน

๐Ÿ“Œ I Invented a Way to Speak to an AI, Keeping Your Privacy ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-06-28 | โฑ๏ธ Rea
๐Ÿ“Œ I Invented a Way to Speak to an AI, Keeping Your Privacy ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-06-28 | โฑ๏ธ Read time: 9 min read The tech is called โ€œSilent Voice.โ€

๐Ÿ“Œ The Math Behind Risk โ€“ Part 1 ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-06-28 | โฑ๏ธ Read time: 11 min read Does the attack re
๐Ÿ“Œ The Math Behind Risk โ€“ Part 1 ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-06-28 | โฑ๏ธ Read time: 11 min read Does the attack really have an advantage in the game of world conquest?

๐Ÿ“Œ The History of Convolutional Neural Networks for Image Classification (1989- Today) ๐Ÿ—‚ Category: DEEP LEARNING ๐Ÿ•’ Date: 20
๐Ÿ“Œ The History of Convolutional Neural Networks for Image Classification (1989- Today) ๐Ÿ—‚ Category: DEEP LEARNING ๐Ÿ•’ Date: 2024-06-28 | โฑ๏ธ Read time: 18 min read A tour through the history of Computer Vision!

๐Ÿ“Œ Safeguarding Demand Forecasting with Causal Graphs ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-06-28 | โฑ๏ธ Read time: 11 min re
๐Ÿ“Œ Safeguarding Demand Forecasting with Causal Graphs ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-06-28 | โฑ๏ธ Read time: 11 min read Causal AI, exploring the integration of causal reasoning into machine learning

๐Ÿ“Œ Diving Deep into AutoGen and Agentic Frameworks ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-06-28 | โฑ๏ธ Read time: 13 min read This blog pos
๐Ÿ“Œ Diving Deep into AutoGen and Agentic Frameworks ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-06-28 | โฑ๏ธ Read time: 13 min read This blog post will go into the details of the โ€œAutoGen: Enabling Next-Gen LLM Applicationsโ€ฆ

๐Ÿ“Œ Estimate the unobserved โ€“ Moving-Average Model Estimation with Maximum Likelihood in Python ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ D
๐Ÿ“Œ Estimate the unobserved โ€“ Moving-Average Model Estimation with Maximum Likelihood in Python ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-06-28 | โฑ๏ธ Read time: 8 min read How unobserved covariatesโ€™ coefficients can be estimated with MLE

๐Ÿ“Œ CRAG โ€“ Intuitively and Exhaustively Explained ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-06-28 | โฑ๏ธ Read time: 13
๐Ÿ“Œ CRAG โ€“ Intuitively and Exhaustively Explained ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-06-28 | โฑ๏ธ Read time: 13 min read Defining the Limits of Retrieval Augmented Generation

๐Ÿ“Œ System Design: Load Balancer ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-06-28 | โฑ๏ธ Read time: 9 min read Orchestrating strategies for opti
๐Ÿ“Œ System Design: Load Balancer ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-06-28 | โฑ๏ธ Read time: 9 min read Orchestrating strategies for optimal workload distribution in microservice applications