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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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  • 10 авг.✅ The Most Underrated Habit in Data Science 👉 Keep a modeling journal. After every experiment, write down: • What changed • Why you changed it • The metric before • The metric after • What you learned Six months later, this notebook becomes more valuable than your code.0,79%
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  • 23 июл.🚀 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 maintaining scrapers. Start building with reliable data. 🔗 Try CoreClaw Free—>https://coreclaw.com0,56%
  • 7 авг.🚩7 Red Flags You Should Check in Every Dataset Before EDA, look for these. 🔻Duplicate rows 🔻Missing values that aren't random 🔻Impossible numbers (negative ages, future dates) 🔻Columns with only one value 🔻Categories with inconsistent spelling 🔻Target leakage 🔻Suspiciously perfect distributions Catching these early saves hours of debugging later.0,54%
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  • 27 июл.✅ 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 measured six months from now? Most modeling mistakes start here, not in the code.0,48%
  • 12 авг.We recently had a request from for Unsupervised Learning notes. To make this resource even more valuable for everyone, we decided to bundle them together with Supervised Learning notes as well! Source: Princeton University Lecture Notes @datascience_bds0,45%