Data science/ML/AI

À propos de Data science/ML/AI

Data science/ML/AI est un canal Telegram dédié à Autre, avec plus de 13k abonnés. Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatac... Rejoignez le canal pour recevoir toutes les mises à jour directement sur Telegram.

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Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist

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Check List to Become DataScientist

16/07/26 1.04K

17/07/26 1.11K

Statistics for Data Science

18/07/26 1.13K

🔍 5 resources most ML people never stumble on These are the things practitioners quietly rely on but rarely share. 1. Google's "Rules of Machine Learning" 43 numbered rules from Google engineers on when to add complexity, how to catch training/serving skew, and when a heuristic beats a model. Written from real production postmortems. 2. Chip Huyen's ML Systems Design notes Free breakdown of how co

22/07/26 799

🚀 Build products faster with ready-to-use Web Data APIs Introducing CoreClaw — a platform that helps developers collect structured data without building and maintaining scrapers. ✨ What you can do: ✅ Google Maps data extraction ✅ Instagram posts & comments scraping ✅ YouTube data collection ✅ Amazon & LinkedIn data APIs ✅ JSON / CSV structured output ✅ API & no-code workflows Stop spending time ma

23/07/26 774

SQL Query Execution Order

25/07/26 691

✅ Before Building Any Model, Answer These 5 Questions A surprising number of ML projects fail before training even begins. Before writing a single line of code, answer these: 1. What decision will this prediction help someone make? 2. What data won't exist when this model is deployed? 3. What's the cost of a wrong prediction? 4. Which metric actually reflects that cost? 5. How will success be meas

27/07/26 442

❔ Why Batch Normalization Makes Deep Networks Easier to Train Training a deep network is a bit like passing a message through twenty people. If each person changes the message slightly, by the end it's completely different. The same thing happens inside neural networks. As earlier layers update, the distribution of values reaching later layers keeps changing. Every layer has to constantly readjust

29/07/26 209

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