Machine Learning – Telegram
Machine Learning
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Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.

Admin: @HusseinSheikho || @Hussein_Sheikho
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📌 Spatial Index: Space-Filling Curves

🗂 Category: DATABASE DESIGN

🕒 Date: 2024-06-11 | ⏱️ Read time: 11 min read

Spatial Index and Space Filling Curves for Multi-dimensional data
🤖🧠 Agentic Entropy-Balanced Policy Optimization (AEPO): Balancing Exploration and Stability in Reinforcement Learning for Web Agents

🗓️ 17 Oct 2025
📚 AI News & Trends

AEPO (Agentic Entropy-Balanced Policy Optimization) represents a major advancement in the evolution of Agentic Reinforcement Learning (RL). As large language models (LLMs) increasingly act as autonomous web agents – searching, reasoning and interacting with tools – the need for balanced exploration and stability has become crucial. Traditional RL methods often rely heavily on entropy to ...

#AgenticRL #ReinforcementLearning #LLMs #WebAgents #EntropyBalanced #PolicyOptimization
📌 Task-Aware RAG Strategies for When Sentence Similarity Fails

🗂 Category: DATA SCIENCE

🕒 Date: 2024-06-10 | ⏱️ Read time: 19 min read

When retrieval is based on an additional metric besides just sentence similarity, traditional methods perform…
📌 Analyzing Lake Mendota Ice Phenology with Python

🗂 Category: CLIMATE CHANGE

🕒 Date: 2024-06-10 | ⏱️ Read time: 10 min read

An analysis-ready dataset you can use right now
AI Engineering roadmap that beginners can actually follow. Everything is based on 100% free, open-source, and community resources

All resources can be found here: GitHub

👉  @codeprogrammer
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📌 Building a Data Engineering Center of Excellence

🗂 Category: DATA ENGINEERING

🕒 Date: 2025-02-13 | ⏱️ Read time: 11 min read

As data continues to grow in importance and become more complex, the need for skilled…
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📌 Learnings from a Machine Learning Engineer — Part 5: The Training

🗂 Category: MACHINE LEARNING

🕒 Date: 2025-02-13 | ⏱️ Read time: 16 min read

In this fifth part of my series, I will outline the steps for creating a…
📌 Learnings from a Machine Learning Engineer — Part 3: The Evaluation

🗂 Category: MACHINE LEARNING

🕒 Date: 2025-02-13 | ⏱️ Read time: 13 min read

In this third part of my series, I will explore the evaluation process which is…
📌 Learnings from a Machine Learning Engineer — Part 1: The Data

🗂 Category: MACHINE LEARNING

🕒 Date: 2025-02-13 | ⏱️ Read time: 13 min read

It is said that in order for a machine learning model to be successful, you…
📌 Learnings from a Machine Learning Engineer — Part 4: The Model

🗂 Category: MACHINE LEARNING

🕒 Date: 2025-02-13 | ⏱️ Read time: 8 min read

In this latest part of my series, I will share what I have learned on…
📌 Learnings from a Machine Learning Engineer — Part 2: The Data Sets

🗂 Category: MACHINE LEARNING

🕒 Date: 2025-02-13 | ⏱️ Read time: 10 min read

In Part 1, we discussed the importance of collecting good image data and assigning proper labels…
🤖🧠 Sora: OpenAI’s Breakthrough Text-to-Video Model Transforming Visual Creativity

🗓️ 18 Oct 2025
📚 AI News & Trends

Introduction Artificial Intelligence (AI) is rapidly transforming the creative world. From generating realistic images to composing music and writing code, AI has redefined how humans interact with technology. But one of the most revolutionary advancements in this domain is Sora, OpenAI’s text-to-video generative model that converts written prompts into hyper-realistic video clips. Ithas captured global ...

#Sora #OpenAI #TextToVideo #AI #VisualCreativity #GenerativeModel
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🤖🧠 Sora: OpenAI’s Breakthrough Text-to-Video Model Transforming Visual Creativity

🗓️ 18 Oct 2025
📚 AI News & Trends

Introduction Artificial Intelligence (AI) is rapidly transforming the creative world. From generating realistic images to composing music and writing code, AI has redefined how humans interact with technology. But one of the most revolutionary advancements in this domain is Sora, OpenAI’s text-to-video generative model that converts written prompts into hyper-realistic video clips. Ithas captured global ...

#Sora #OpenAI #TextToVideo #AI #VisualCreativity #GenerativeModel
📌 Machine Learning Meets Panel Data: What Practitioners Need to Know

🗂 Category: MACHINE LEARNING

🕒 Date: 2025-10-17 | ⏱️ Read time: 8 min read

How to avoid overestimating machine learning models’ performance, usefulness, and real-world applicability due to hidden…
📌 How to Classify Lung Cancer Subtype from DNA Copy Numbers Using PyTorch

🗂 Category: DEEP LEARNING

🕒 Date: 2025-10-17 | ⏱️ Read time: 12 min read

A step-by-step introduction to understanding cancer from the perspective of a data scientist.
📌 How I Used Machine Learning to Predict 41% of Project Delays Before They Happened

🗂 Category: PROJECT MANAGEMENT

🕒 Date: 2025-10-17 | ⏱️ Read time: 12 min read

How data science can help project managers anticipate risks and save time
📌 Statistical Method mcRigor Enhances the Rigor of Metacell Partitioning in Single-Cell Data Analysis

🗂 Category: DATA SCIENCE

🕒 Date: 2025-10-17 | ⏱️ Read time: 6 min read

mcRigor detects dubious metacells within each metacell partition and selects the optimal metacell partitioning method…
📌 TDS Newsletter: The Rapid Transformation of Data Science in the Age of AI

🗂 Category: THE VARIABLE

🕒 Date: 2025-10-16 | ⏱️ Read time: 3 min read

How data science became a strikingly different discipline in the span of a couple of…
📌 TDS Newsletter: How to Keep LLMs Effective and Reliable Over Time

🗂 Category: THE VARIABLE

🕒 Date: 2025-10-09 | ⏱️ Read time: 4 min read

Those of you who’ve worked with LLM-powered applications know this: by now, building and deploying these tools…
📌 How Do Computers Actually Compute?

🗂 Category: DATA SCIENCE

🕒 Date: 2024-06-10 | ⏱️ Read time: 10 min read

A Budding Data Scientist’s Introduction to Computer Hardware
📌 Deploy a LightGBM ML Model With GitHub Actions

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-06-10 | ⏱️ Read time: 9 min read

A beginner’s guide to getting out of Jupyter notebooks and deploying ML models