Machine Learning – Telegram
Machine Learning
39.7K subscribers
3.99K photos
36 videos
47 files
1.34K links
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
Download Telegram
📌 Bridging the Gap Between Research and Readability with Marco Hening Tallarico

🗂 Category: AUTHOR SPOTLIGHTS

🕒 Date: 2026-01-19 | ⏱️ Read time: 6 min read

Diluting complex research, spotting silent data leaks, and why the best way to learn is…

#DataScience #AI #Python
📌 Using Local LLMs to Discover High-Performance Algorithms

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2026-01-19 | ⏱️ Read time: 10 min read

How I used open-source models to explore new frontiers in efficient code generation, using my…

#DataScience #AI #Python
1
📌 Time Series Isn’t Enough: How Graph Neural Networks Change Demand Forecasting

🗂 Category: DATA SCIENCE

🕒 Date: 2026-01-19 | ⏱️ Read time: 11 min read

Why modeling SKUs as a network reveals what traditional forecasts miss

#DataScience #AI #Python
📌 You Probably Don’t Need a Vector Database for Your RAG — Yet

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2026-01-20 | ⏱️ Read time: 14 min read

Numpy or SciKit-Learn might meet all your retrieval needs

#DataScience #AI #Python
📌 Why Package Installs Are Slow (And How to Fix It)

🗂 Category: DATA ENGINEERING

🕒 Date: 2026-01-20 | ⏱️ Read time: 7 min read

How sharded indexing patterns solve a scaling problem in package management

#DataScience #AI #Python
📌 Does Calendar-Based Time-Intelligence Change Custom Logic?

🗂 Category: DATA SCIENCE

🕒 Date: 2026-01-20 | ⏱️ Read time: 8 min read

Let’s look at calculating the moving average over time

#DataScience #AI #Python
📌 How to Perform Large Code Refactors in Cursor

🗂 Category: AGENTIC AI

🕒 Date: 2026-01-20 | ⏱️ Read time: 10 min read

Learn how to perform code refactoring with LLMs

#DataScience #AI #Python
1
🙏💸 500$ FOR THE FIRST 500 WHO JOIN THE CHANNEL! 🙏💸

Join our channel today for free! Tomorrow it will cost 500$!

https://news.1rj.ru/str/+0-w7MQwkOs02MmJi

You can join at this link! 👆👇

https://news.1rj.ru/str/+0-w7MQwkOs02MmJi
🔥 Trending Repository: Data-Science-For-Beginners

📝 Denoscription: 10 Weeks, 20 Lessons, Data Science for All!

🔗 Repository URL: https://github.com/microsoft/Data-Science-For-Beginners

📖 Readme: https://github.com/microsoft/Data-Science-For-Beginners#readme

📊 Statistics:
🌟 Stars: 31.9K stars
👀 Watchers: 513
🍴 Forks: 6.8K forks

💻 Programming Languages: Jupyter Notebook

🏷️ Related Topics:
#python #data_science #pandas #data_visualization #data_analysis #microsoft_for_beginners


==================================
🧠 By: https://news.1rj.ru/str/DataScienceM
4
📌 Google Trends is Misleading You: How to Do Machine Learning with Google Trends Data

🗂 Category: DATA SCIENCE

🕒 Date: 2026-01-21 | ⏱️ Read time: 11 min read

Google Trends is one of the most widely used tools for analysing human behaviour at…

#DataScience #AI #Python
📌 If You Want to Become a Data Scientist in 2026, Do This

🗂 Category: DATA SCIENCE

🕒 Date: 2026-01-21 | ⏱️ Read time: 10 min read

Learn from my mistakes and fast track your data science career

#DataScience #AI #Python
1
📌 Building a Self-Healing Data Pipeline That Fixes Its Own Python Errors

🗂 Category: LLM APPLICATIONS

🕒 Date: 2026-01-21 | ⏱️ Read time: 8 min read

How I built a self-healing pipeline that automatically fixes bad CSVs, schema changes, and weird…

#DataScience #AI #Python
Guide to AI Coding Agents & Assistants: How to Choose the Right One

There are now so many AI tools for coding that it can be confusing to know which one to pick. Some act as simple helpers (Assistant), while others can do the work for you (Agent). This guide breaks down the top AI coding tools that you should be aware of. We will look at what they do, who they are for, and how much they cost.

Read: https://habr.com/en/articles/979402/

https://news.1rj.ru/str/DataScienceM
📌 A Case for the T-statistic

🗂 Category: DATA SCIENCE

🕒 Date: 2026-01-21 | ⏱️ Read time: 21 min read

And how it compares to the run-of-the-mill z-score

#DataScience #AI #Python
1
📌 Evaluating Multi-Step LLM-Generated Content: Why Customer Journeys Require Structural Metrics

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2026-01-22 | ⏱️ Read time: 13 min read

How to evaluate goal-oriented content designed to build engagement and deliver business results, and why…

#DataScience #AI #Python
📌 Why SaaS Product Management Is the Best Domain for Data-Driven Professionals in 2026

🗂 Category: PRODUCT MANAGEMENT

🕒 Date: 2026-01-22 | ⏱️ Read time: 14 min read

How I use analytics, automation, and AI to build better SaaS

#DataScience #AI #Python
📌 Stop Writing Messy Boolean Masks: 10 Elegant Ways to Filter Pandas DataFrames

🗂 Category: DATA SCIENCE

🕒 Date: 2026-01-22 | ⏱️ Read time: 7 min read

Master the art of readable, high-performance data selection using .query(), .isin(), and advanced vectorized logic.

#DataScience #AI #Python
3
📌 What Other Industries Can Learn from Healthcare’s Knowledge Graphs

🗂 Category: DATA SCIENCE

🕒 Date: 2026-01-22 | ⏱️ Read time: 11 min read

How shared meaning, evidence, and standards create durable semantic infrastructure

#DataScience #AI #Python
📌 Optimizing Data Transfer in Distributed AI/ML Training Workloads

🗂 Category: DATA ENGINEERING

🕒 Date: 2026-01-23 | ⏱️ Read time: 15 min read

A deep dive on data transfer bottlenecks, their identification, and their resolution with the help…

#DataScience #AI #Python
📌 Achieving 5x Agentic Coding Performance with Few-Shot Prompting

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2026-01-23 | ⏱️ Read time: 9 min read

Learn to leverage few-shot prompting to increase your LLMs performance

#DataScience #AI #Python
📌 Why the Sophistication of Your Prompt Correlates Almost Perfectly with the Sophistication of the Response, as Research by Anthropic Found

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2026-01-23 | ⏱️ Read time: 9 min read

How prompt engineering has evolved, examined scientifically; and implications for the future of conversational AI…

#DataScience #AI #Python