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 Syria228 Technologies & Applications3 344...

📈 Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 208 subscribers, ranking 3 344 in the Technologies & Applications category and 228 in the Syria region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 40 208 subscribers.

According to the latest data from 03 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 338 over the last 30 days and by 9 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 2.42% reactions from the total number of subscribers.
Post reach: On average, each post receives 822 views. Within the first day, a publication typically gains 973 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 04 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.

40 208

Subscribers

+924 hours

+727 days

+33830 days

822

Post views

~ 97324 hours

~ 1 32148 hours

2.04%

Engagement rate

~ 3

Posts per day

Ads index

beta

Posts Archive

40 221

📌 Automatic Differentiation (AutoDiff): A Brief Intro with Examples 🗂 Category: DEEP LEARNING 🕒 Date: 2024-10-11 | ⏱️ Read time: 11 min read An introduction to the mechanics of AutoDiff, exploring its mathematical principles, implementation strategies, and applications

40 221

📌 Topic Alignment for NLP Recommender Systems 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-11 | ⏱️ Read time: 18 min read Leveraging topic modeling to align user queries with document themes, enhancing the relevance and contextual…

40 221

📌 A Mixed-Methods Approach to Offline Evaluation of News Recommender Systems 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-11 | ⏱️ Read time: 8 min read Combining reader feedback from surveys with behavioral click data to optimize content personalization.

40 221

📌 Understanding Automatic Differentiation in JAX: A Deep Dive 🗂 Category: DEEP LEARNING 🕒 Date: 2024-10-11 | ⏱️ Read time: 12 min read Unleashing the Gradient: How JAX Makes Automatic Differentiation Feel Like Magic

40 221

📌 Common Misconceptions About Data Science 🗂 Category: CAREER ADVICE 🕒 Date: 2024-10-11 | ⏱️ Read time: 7 min read Data science advice that you should question

40 221

📌 Bursting the AI Hype Bubble Once and for All 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-12 | ⏱️ Read time: 11 min read Misinformation and poor research: a case study

40 221

📌 Gaussian Naive Bayes, Explained: A Visual Guide with Code Examples for Beginners 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-12 | ⏱️ Read time: 8 min read Bell-shaped assumptions for better predictions

40 221

📌 Improve Your RAG Context Recall by 95% with an Adapted Embedding Model. 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-10-12 | ⏱️ Read time: 11 min read Step by Step Model Adaptation Code and Results Attached.

40 221

📌 Why the 2024 Nobel Prize in (AI for) Chemistry Matters So Much 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-12 | ⏱️ Read time: 6 min read To Demis Hassabis and John Jumper, from DeepMind, and to David Baker, leader of the…

40 221

📌 Upgrading to Prefect Push Workers on AWS ECS 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-10-12 | ⏱️ Read time: 6 min read Upgrade from Prefect 2.0 to 3.0 and use the new Push Work Pools that greatly…

40 221

📌 Linear Discriminant Analysis (LDA) 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-12 | ⏱️ Read time: 13 min read Discover how LDA helps identify critical data features

40 221

📌 Top 5 Principles for Building User-Friendly Data Tables 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-10-13 | ⏱️ Read time: 9 min read Designing intuitive and reliable tables that your data team will love

40 221

📌 Recruiting vs. Interviewing for Data Roles in Diverse Markets 🗂 Category: CAREER ADVICE 🕒 Date: 2024-10-13 | ⏱️ Read time: 12 min read Factors of success in recruiting and interviewing after applying for 150+ positions and reviewing 500+…

40 221

📌 How to Perform A/B Testing with Hypothesis Testing in Python: A Comprehensive Guide 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-13 | ⏱️ Read time: 11 min read A Step-by-Step Guide to Making Data-Driven Decisions with Practical Python Examples

40 221

📌 Bringing Structure to Your Data 🗂 Category: 🕒 Date: 2024-10-14 | ⏱️ Read time: 13 min read Testing assumptions with path models

40 221

📌 lintsampler: a new way to quickly get random samples from any distribution 🗂 Category: PROBABILITY 🕒 Date: 2024-10-14 | ⏱️ Read time: 5 min read lintsampler is a pure Python package that can easily and efficiently generate random samples from…

40 221

📌 Product-Oriented ML: A Guide for Data Scientists 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-14 | ⏱️ Read time: 30 min read How to build ML products users love

40 221

📌 How to Set Bid Guardrails in PPC Marketing 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-14 | ⏱️ Read time: 14 min read Without controls, bidding algorithms can be quite volatile. Learn how to protect performance through adding…

40 221

📌 PyTorch Optimizers Aren’t Fast Enough. Try These Instead 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-14 | ⏱️ Read time: 12 min read These 4 advanced optimizers will open your mind.

40 221

📌 Florence-2: Advancing Multiple Vision Tasks with a Single VLM Model 🗂 Category: 🕒 Date: 2024-10-14 | ⏱️ Read time: 8 min read A Guided Exploration of Florence-2’s Zero-Shot Capabilities: Captioning, Object Detection, Segmentation and OCR.