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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📌 On Data Problems, and How to Solve (or Prevent) Them

🗂 Category: THE VARIABLE

🕒 Date: 2025-04-17 | ⏱️ Read time: 3 min read

This week, we focus on the workflows that can help keep your data focused.
📌 Google’s New AI System Outperforms Physicians in Complex Diagnoses

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2025-04-17 | ⏱️ Read time: 13 min read

Published in Nature, Google’s new paper advances the future of AI-powered medicine: more automated thus…
📌 The Good-Enough Truth

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2025-04-17 | ⏱️ Read time: 7 min read

Lies, damned lies, and LLMs
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📌 When Predictors Collide: Mastering VIF in Multicollinear Regression

🗂 Category: DATA SCIENCE

🕒 Date: 2025-04-16 | ⏱️ Read time: 11 min read

Explore how the Variance Inflation Factor helps detect and manage multicollinearity in your regression models.
📌 An Unbiased Review of Snowflake’s Document AI

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2025-04-15 | ⏱️ Read time: 8 min read

Or, how we spared a human from manually inspecting 10,000 flu shot documents.
📌 Plotly’s AI Tools Are Redefining Data Science Workflows

🗂 Category: SPONSORED CONTENT

🕒 Date: 2025-04-15 | ⏱️ Read time: 8 min read

How Plotly’s AI-powered tools are transforming data science workflows with faster development, smarter insights, and…
📌 An LLM-Based Workflow for Automated Tabular Data Validation

🗂 Category: DATA SCIENCE

🕒 Date: 2025-04-14 | ⏱️ Read time: 12 min read

Clean data, clear insights: detect and correct data quality issues without manual intervention.
📌 Layers of the AI Stack, Explained Simply

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2025-04-14 | ⏱️ Read time: 14 min read

And why I decided to work at the application layer
📌 Sesame  Speech Model:  How This Viral AI Model Generates Human-Like Speech

🗂 Category: CONVERSATIONAL AI

🕒 Date: 2025-04-11 | ⏱️ Read time: 9 min read

A deep dive into residual vector quantizers, conversational speech AI, and talkative transformers.
📌 Learnings from a Machine Learning Engineer — Part 6: The Human Side

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2025-04-11 | ⏱️ Read time: 16 min read

Practical advice for the humans involved with machine learning
📌 Are You Sure Your Posterior Makes Sense?

🗂 Category: DATA SCIENCE

🕒 Date: 2025-04-11 | ⏱️ Read time: 26 min read

A detailed guide on how to use diagnostics to evaluate the performance of MCMC samplers
📌 The Basis of Cognitive Complexity: Teaching CNNs to See Connections

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2025-04-11 | ⏱️ Read time: 9 min read

Transforming CNNs: From task-specific learning to abstract generalization
📌 The What, How, and Why of Agentic AI

🗂 Category: THE VARIABLE

🕒 Date: 2025-04-10 | ⏱️ Read time: 3 min read

This week, we tackle the nitty-gritty details of working with agentic AI.
📌 The Invisible Revolution: How Vectors Are (Re)defining Business Success

🗂 Category: DATA SCIENCE

🕒 Date: 2025-04-10 | ⏱️ Read time: 26 min read

The hidden force behind AI is powering the next wave of business transformation
📌 How to Measure Real Model Accuracy When Labels Are Noisy

🗂 Category: DATA SCIENCE

🕒 Date: 2025-04-10 | ⏱️ Read time: 5 min read

The math behind “true” accuracy and error correlation
📌 Ivory Tower Notes: The Problem

🗂 Category: DATA SCIENCE

🕒 Date: 2025-04-10 | ⏱️ Read time: 12 min read

When a data science problem is “the” problem
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📌 Deb8flow: Orchestrating Autonomous AI Debates with LangGraph and GPT-4o

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2025-04-10 | ⏱️ Read time: 29 min read

Inside Deb8flow: Real-time AI debates with LangGraph and GPT-4o
📌 Why CatBoost Works So Well: The Engineering Behind the Magic

🗂 Category: MACHINE LEARNING

🕒 Date: 2025-04-09 | ⏱️ Read time: 10 min read

CatBoost stands out by directly tackling a long-standing challenge in gradient boosting—how to handle categorical…
📌 Time Series Forecasting Made Simple (Part 1): Decomposition and Baseline Models

🗂 Category: DATA SCIENCE

🕒 Date: 2025-04-09 | ⏱️ Read time: 12 min read

Learn the intuition behind time series decomposition, additive vs. multiplicative models and build your first…
📌 Mining Rules from Data

🗂 Category: DATA SCIENCE

🕒 Date: 2025-04-09 | ⏱️ Read time: 20 min read

Using decision trees for quick segmentation
📌 A Data Scientist’s Guide to Docker Containers

🗂 Category: DATA SCIENCE

🕒 Date: 2025-04-08 | ⏱️ Read time: 11 min read

How to enable your ML model to run anywhere