Machine Learning

📌 How to Keep MCPs Useful in Agentic Pipelines

🗂 Category: AGENTIC AI

🕒 Date: 2026-01-03 | ⏱️ Read time: 10 min read

Check the tools your LLM uses before replacing it with just a more powerful model

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Machine Learning

🔖

40 NumPy methods that cover 95% of tasks

A convenient cheat sheet for those who work with data analysis and ML.

Here are collected the main functions for:

▶️ Creating and modifying arrays;
▶️ Mathematical operations;
▶️ Working with matrices and vectors;
▶️ Sorting and searching for values.

Save it for yourself — it will come in handy when working with NumPy.

tags: #NumPy #Python

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Machine Learning

📌 Prompt Engineering vs RAG for Editing Resumes

🗂 Category: LLM APPLICATIONS

🕒 Date: 2026-01-04 | ⏱️ Read time: 12 min read

Running a code-free comparison in Azure

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Machine Learning

📌 How to Filter for Dates, Including or Excluding Future Dates, in Semantic Models

🗂 Category: DATA ANALYSIS

🕒 Date: 2026-01-04 | ⏱️ Read time: 5 min read

It is common to have either planning data or the previous year’s data displayed beyond…

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Machine Learning

nature papers: 1400$

Q1 and Q2 papers    900$

Q3 and Q4 papers   500$

Doctoral thesis (complete)    700$

M.S thesis         300$

paper simulation   200$

Contact me
https://t.iss.one/m/-nTmpj5vYzNk

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OnSpace Mobile App builder: Build AI Apps in minutes

Visit website: https://www.onspace.ai/?via=tg_datas
Or Download app:https://onspace.onelink.me/za8S/h1jb6sb9?c=datas

With OnSpace, you can build website or AI Mobile Apps by chatting with AI, and publish to PlayStore or AppStore.

What will you get:
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✔️ Database + dashboard in minutes.
✔️ Full tutorial on YouTube and within 1 day customer service

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Machine Learning

Machine Learning pinned a video

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Machine Learning

📌 Stop Blaming the Data: A Better Way to Handle Covariance Shift

🗂 Category: DATA SCIENCE

🕒 Date: 2026-01-05 | ⏱️ Read time: 9 min read

Instead of using shift as an excuse for poor performance, use Inverse Probability Weighting to…

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Machine Learning

📌 YOLOv1 Loss Function Walkthrough: Regression for All

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-01-05 | ⏱️ Read time: 26 min read

An explanation of how YOLOv1 measures the correctness of its object detection and classification predictions

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1.78K views23:50

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Machine Learning

📌 How to Optimize Your AI Coding Agent Context

🗂 Category: PROGRAMMING

🕒 Date: 2026-01-06 | ⏱️ Read time: 7 min read

Make your coding agents more efficient

#DataScience #AI #Python

1.63K views13:42

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Machine Learning

📌 GliNER2: Extracting Structured Information from Text

🗂 Category: NATURAL LANGUAGE PROCESSING

🕒 Date: 2026-01-06 | ⏱️ Read time: 11 min read

From unstructured text to structured Knowledge Graphs

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Machine Learning

📌 Feature Detection, Part 3: Harris Corner Detection

🗂 Category: MACHINE LEARNING

🕒 Date: 2026-01-05 | ⏱️ Read time: 7 min read

Finding the most informative points in images

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Machine Learning

📌 Measuring What Matters with NeMo Agent Toolkit

🗂 Category: LLM APPLICATIONS

🕒 Date: 2026-01-06 | ⏱️ Read time: 13 min read

A practical guide to observability, evaluations, and model comparisons

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1.42K views07:43

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Machine Learning

📌 The Best Data Scientists Are Always Learning

🗂 Category: DATA SCIENCE

🕒 Date: 2026-01-06 | ⏱️ Read time: 10 min read

Part 2: Avoiding burnout, learning strategies and the superpower of solitude

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Machine Learning

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Machine Learning

📌 HNSW at Scale: Why Your RAG System Gets Worse as the Vector Database Grows

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2026-01-07 | ⏱️ Read time: 18 min read

How approximate vector search silently degrades Recall—and what to do about It

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Machine Learning

📌 I Evaluated Half a Million Credit Records with Federated Learning. Here’s What I Found

🗂 Category: DATA SCIENCE

🕒 Date: 2026-01-07 | ⏱️ Read time: 12 min read

Why privacy breaks fairness at small scale—and how collaboration fixes both without sharing a single…

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Machine Learning

📌 Probabilistic Multi-Variant Reasoning: Turning Fluent LLM Answers Into Weighted Options

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2026-01-07 | ⏱️ Read time: 21 min read

Human-guided AI collaboration

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Machine Learning

Forwarded from Machine Learning with Python

𝐒𝐮𝐩𝐩𝐨𝐫𝐭_𝐕𝐞𝐜𝐭𝐨𝐫_𝐌𝐚𝐜𝐡𝐢𝐧𝐞𝐬_𝐒𝐕𝐌⁣.pdf

5.8 MB

📐 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐕𝐞𝐜𝐭𝐨𝐫 𝐌𝐚𝐜𝐡𝐢𝐧𝐞𝐬 (𝐒𝐕𝐌)⁣

🔹 What I covered today⁣
What SVM is and how it works⁣
Concept of hyperplane, margin, and support vectors⁣
Hard margin vs Soft margin⁣
Role of kernel trick⁣
⁣
When SVM performs better than other classifiers⁣
⁣
🎯 𝐓𝐨𝐩 𝟏𝟎 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 (𝐌𝐮𝐬𝐭-𝐊𝐧𝐨𝐰)⁣
⁣
1️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘚𝘶𝘱𝘱𝘰𝘳𝘵 𝘝𝘦𝘤𝘵𝘰𝘳 𝘔𝘢𝘤𝘩𝘪𝘯𝘦 (𝘚𝘝𝘔)?⁣
2️⃣ 𝘞𝘩𝘢𝘵 𝘢𝘳𝘦 𝘴𝘶𝘱𝘱𝘰𝘳𝘵 𝘷𝘦𝘤𝘵𝘰𝘳𝘴?⁣
3️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘢 𝘮𝘢𝘳𝘨𝘪𝘯 𝘪𝘯 𝘚𝘝𝘔?⁣
4️⃣ 𝘋𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘤𝘦 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘩𝘢𝘳𝘥 𝘮𝘢𝘳𝘨𝘪𝘯 𝘢𝘯𝘥 𝘴𝘰𝘧𝘵 𝘮𝘢𝘳𝘨𝘪𝘯?⁣
5️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘦 𝘬𝘦𝘳𝘯𝘦𝘭 𝘵𝘳𝘪𝘤𝘬 𝘢𝘯𝘥 𝘸𝘩𝘺 𝘪𝘴 𝘪𝘵 𝘯𝘦𝘦𝘥𝘦𝘥?⁣
6️⃣ 𝘊𝘰𝘮𝘮𝘰𝘯 𝘬𝘦𝘳𝘯𝘦𝘭𝘴 𝘶𝘴𝘦𝘥 𝘪𝘯 𝘚𝘝𝘔 (𝘓𝘪𝘯𝘦𝘢𝘳, 𝘗𝘰𝘭𝘺𝘯𝘰𝘮𝘪𝘢𝘭, 𝘙𝘉𝘍)?⁣
7️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘦 𝘳𝘰𝘭𝘦 𝘰𝘧 𝘊 (𝘳𝘦𝘨𝘶𝘭𝘢𝘳𝘪𝘻𝘢𝘵𝘪𝘰𝘯 𝘱𝘢𝘳𝘢𝘮𝘦𝘵𝘦𝘳)?⁣
8️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘨𝘢𝘮𝘮𝘢 𝘪𝘯 𝘙𝘉𝘍 𝘬𝘦𝘳𝘯𝘦𝘭?⁣
9️⃣ 𝘊𝘢𝘯 #𝘚𝘝𝘔 𝘣𝘦 𝘶𝘴𝘦𝘥 𝘧𝘰𝘳 𝘳𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯? (𝘚𝘝𝘙)⁣
🔟 𝘞𝘩𝘦𝘯 𝘴𝘩𝘰𝘶𝘭𝘥 𝘺𝘰𝘶 𝘢𝘷𝘰𝘪𝘥 𝘶𝘴𝘪𝘯𝘨 𝘚𝘝𝘔?⁣

https://t.iss.one/CodeProgrammer

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Machine Learning

📌 Why Supply Chain is the Best Domain for Data Scientists in 2026 (And How to Learn It)

🗂 Category: DATA SCIENCE

🕒 Date: 2026-01-07 | ⏱️ Read time: 13 min read

My take after 10 years in Supply Chain on why this can be an excellent…

#DataScience #AI #Python

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1.53K views07:32

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The single most undervalued fact of linear algebra: matrices are graphs, and graphs are matrices.

Encoding matrices as graphs is a cheat code, making complex behavior simple to study.

https://t.iss.one/DataScienceM

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