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.me/m/-nTmpj5vYzNk

📌 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… #DataScience #AI #Python

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.me/m/-nTmpj5vYzNk

📌 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 #DataScience #AI #Python

📌 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 #DataScience #AI #Python

📌 Optimizing Data Transfer in AI/ML Workloads 🗂 Category: DEEP LEARNING 🕒 Date: 2026-01-03 | ⏱️ Read time: 16 min read A deep dive on data transfer bottlenecks, their identification, and their resolution with the help… #DataScience #AI #Python

200$ to 20k$ SOL Challenge! As promised, i will do another challenge for those who missed the previous one! Last one we completed in 6 days, let’s do this one even quicker! Join my free group Before closing 👇 https://t.me/+DAKLP7eUy9Y3ZjY0 #ad InsideAds

All assignments for the #Stanford The Modern Software Developer course are now available online. This is the first full-fledged university course that covers how code-generative #LLMs are changing every stage of the development lifecycle. The assignments are designed to take you from a beginner to a confident expert in using AI to boost productivity in development. Enjoy your studies! ✌️ https://github.com/mihail911/modern-software-dev-assignments https://t.me/CodeProgrammer

📌 The Real Challenge in Data Storytelling: Getting Buy-In for Simplicity 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-02 | ⏱️ Read time: 7 min read What happens when your clear dashboard meets stakeholders who want everything on one screen #DataScience #AI #Python

📌 Off-Beat Careers That Are the Future Of Data 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-02 | ⏱️ Read time: 8 min read The unconventional career paths you need to explore #DataScience #AI #Python

📌 Drift Detection in Robust Machine Learning Systems 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-02 | ⏱️ Read time: 18 min read A prerequisite for long-term success of machine learning systems #DataScience #AI #Python

200$ to 20k$ SOL Challenge! As promised, i will do another challenge for those who missed the previous one! Last one we completed in 6 days, let’s do this one even quicker! Join my free group Before closing 👇 https://t.me/+DAKLP7eUy9Y3ZjY0 #ad InsideAds

📌 Deep Reinforcement Learning: The Actor-Critic Method 🗂 Category: REINFORCEMENT LEARNING 🕒 Date: 2026-01-01 | ⏱️ Read time: 19 min read Robot friends collaborate to learn to fly a drone #DataScience #AI #Python

Harvard has made its textbook on ML systems publicly available. It's extremely practical: not just about how to train models, but how to build production systems around them - what really matters. The topics there are really top-notch: > Building autograd, optimizers, attention, and mini-PyTorch from scratch to understand how the framework is structured internally. (This is really awesome) > Basic things about DL: batches, computational accuracy, model architectures, and training > Optimizing ML performance, hardware acceleration, benchmarking, and efficiency So this isn't just an introductory course on ML, but a complete cycle from start to practical application. You can already read the book and view the code for free. For 2025, this is one of the strongest textbooks to have been released, so it's best not to miss out. The repository is here, with a link to the book inside 👏 👉 @codeprogrammer

📌 EDA in Public (Part 3): RFM Analysis for Customer Segmentation in Pandas 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-01 | ⏱️ Read time: 13 min read How to build, score, and interpret RFM segments step by step #DataScience #AI #Python

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📌 The Machine Learning “Advent Calendar” Bonus 2: Gradient Descent Variants in Excel 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-12-31 | ⏱️ Read time: 8 min read Gradient Descent, Momentum, RMSProp, and Adam all aim for the same minimum. They do not… #DataScience #AI #Python

📌 Chunk Size as an Experimental Variable in RAG Systems 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-12-31 | ⏱️ Read time: 12 min read Understanding retrieval in RAG systems by experimenting with different chunk sizes #DataScience #AI #Python