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Machine Learning

Machine Learning

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

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πŸ“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.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, 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.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œReal Machine Learning β€” 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 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
πŸ“Œ The Math Behind Keras 3 Optimizers: Deep Understanding and Application πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-17 | ⏱️
πŸ“Œ The Math Behind Keras 3 Optimizers: Deep Understanding and Application πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-17 | ⏱️ Read time: 9 min read This is a bit different from what the books say.

πŸ“Œ Massive Energy for Massive GPU Empowering AI πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-08-18 | ⏱️ Read time: 7 min read
πŸ“Œ Massive Energy for Massive GPU Empowering AI πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-08-18 | ⏱️ Read time: 7 min read Massive GPUs for AI model training and deployment require significant energy. As AI scales, optimizing…

πŸ“Œ How to Talk to a PDF File Without Using Proprietary Models: CLI + Streamlit + Ollama πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date
πŸ“Œ How to Talk to a PDF File Without Using Proprietary Models: CLI + Streamlit + Ollama πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-08-14 | ⏱️ Read time: 17 min read A contribution to the creation of a locally executed, free PDF chat app with Streamlit…

πŸ“Œ Heckman Selection Bias Modeling in Causal Studies πŸ—‚ Category: STATISTICS πŸ•’ Date: 2024-08-14 | ⏱️ Read time: 9 min read H
πŸ“Œ Heckman Selection Bias Modeling in Causal Studies πŸ—‚ Category: STATISTICS πŸ•’ Date: 2024-08-14 | ⏱️ Read time: 9 min read How selection bias is related to the identification assumptions of OLS, and what steps should…

πŸ“Œ VAE for Time Series πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-08-14 | ⏱️ Read time: 11 min read Generate realistic seque
πŸ“Œ VAE for Time Series πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-08-14 | ⏱️ Read time: 11 min read Generate realistic sequential data with this easy-to-train model

πŸ“Œ Must-Know Techniques for Handling Big Data in Hive πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-14 | ⏱️ Read time: 8 min rea
πŸ“Œ Must-Know Techniques for Handling Big Data in Hive πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-14 | ⏱️ Read time: 8 min read HQL’s Unique Features- PARTITIONED BY, STORED AS, DISTRIBUTE BY / CLUSTER BY, LATERAL VIEW with…

πŸ“Œ Must-Know in Statistics: The Bivariate Normal Projection Explained πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-14 | ⏱️ Read
πŸ“Œ Must-Know in Statistics: The Bivariate Normal Projection Explained πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-14 | ⏱️ Read time: 7 min read Derivation and practical examples of this powerful concept

πŸ“Œ Dummy Classifier Explained: A Visual Guide with Code Examples for Beginners πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-08
πŸ“Œ Dummy Classifier Explained: A Visual Guide with Code Examples for Beginners πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-08-14 | ⏱️ Read time: 7 min read Setting the Bar in Machine Learning with Simple Baseline Models

πŸ“Œ Towards Mamba State Space Models for Images, Videos and Time Series πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-08-14 | ⏱️ Re
πŸ“Œ Towards Mamba State Space Models for Images, Videos and Time Series πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-08-14 | ⏱️ Read time: 20 min read Part 1

πŸ“Œ How to Create Well-Styled Streamlit Dataframes, Part 1: Using the Pandas Styler πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08
πŸ“Œ How to Create Well-Styled Streamlit Dataframes, Part 1: Using the Pandas Styler πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-14 | ⏱️ Read time: 6 min read Streamlit and the pandas Styler object are not friends. But, we will change that!

πŸ“Œ Vision Transformers, Contrastive Learning, Causal Inference, and Other Deep Dives You Shouldn’t Miss πŸ—‚ Category: DATA SCI
πŸ“Œ Vision Transformers, Contrastive Learning, Causal Inference, and Other Deep Dives You Shouldn’t Miss πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-15 | ⏱️ Read time: 3 min read Our weekly selection of must-read Editors’ Picks and original features

πŸ“Œ A Fresh Look at Nonlinearity in Deep Learning πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-08-15 | ⏱️ Read time: 9 min read Th
πŸ“Œ A Fresh Look at Nonlinearity in Deep Learning πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-08-15 | ⏱️ Read time: 9 min read The traditional reasoning behind why we need nonlinear activation functions is only one dimension of…

πŸ“Œ 5 Ways You Are Sabotaging AI As A Leader πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-15 | ⏱️ Read time: 9 min re
πŸ“Œ 5 Ways You Are Sabotaging AI As A Leader πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-15 | ⏱️ Read time: 9 min read The key mistakes that are derailing AI potential and burning investment

πŸ“Œ Real world Use Cases: Forecasting Service Utilization Using Tabnet and Optuna πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-1
πŸ“Œ Real world Use Cases: Forecasting Service Utilization Using Tabnet and Optuna πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-15 | ⏱️ Read time: 7 min read Data science is at its best out in the real world. I intend to share…

πŸ“Œ From Surrogate Modelling to Aerospace Engineering: a NASA Case Study πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08
πŸ“Œ From Surrogate Modelling to Aerospace Engineering: a NASA Case Study πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-15 | ⏱️ Read time: 12 min read This is how Surrogate Modelling is revolutionizing the world of Aerospace Engineering, from theory to…

πŸ“Œ Simplify Information Extraction: A Reusable Prompt Template for GPT Models πŸ—‚ Category: CHATGPT πŸ•’ Date: 2024-08-15 | ⏱️ R
πŸ“Œ Simplify Information Extraction: A Reusable Prompt Template for GPT Models πŸ—‚ Category: CHATGPT πŸ•’ Date: 2024-08-15 | ⏱️ Read time: 8 min read A prompt template containing prompting techniques that have worked for me on over a dozen…

πŸ“Œ Powering Experiments with CUPED and Double Machine Learning πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-15 | ⏱️ Read time:
πŸ“Œ Powering Experiments with CUPED and Double Machine Learning πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-15 | ⏱️ Read time: 19 min read Causal AI, exploring the integration of causal reasoning into machine learning

From solo miners to small teams, Padma scales with you: structured quests for beginners, leaderboard challenges for pros, and
From solo miners to small teams, Padma scales with you: structured quests for beginners, leaderboard challenges for pros, and staking to keep assets productive. Start light, measure results, and ramp strategically. Start! #ad InsideAds

πŸ“Œ From Basics to Advanced: Exploring LangGraph πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-15 | ⏱️ Read time: 25 m
πŸ“Œ From Basics to Advanced: Exploring LangGraph πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-15 | ⏱️ Read time: 25 min read Building single- and multi-agent workflows with human-in-the-loop interactions

πŸ“Œ Step-by-Step Guide for Building Interactive Calendars in Plotly πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-16 | ⏱️ Read ti
πŸ“Œ Step-by-Step Guide for Building Interactive Calendars in Plotly πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-16 | ⏱️ Read time: 7 min read Create interactive calendars with heatmaps using Plotly