Data science/ML/AI
@datascience_bds - Canal Telegram
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Data science/ML/AI é um canal do Telegram dedicado a Outros, com mais de 13k inscritos. 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... Entre no canal para receber todas as novidades diretamente no 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
Machine Learning Roadmap
Machine Learning Roadmap
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|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus (Gradients, Optimization)
| | |-- Probability and Statistics
| | |-- Matrix Operations
| |
| |-- Programming
| | |-- Python (NumPy, Pandas, Scikit-learn)
| | |-- R (Optional for Statistical Modeling)
| | |-- SQL (For Data Extraction)
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|--
Statistics for Data Science
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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
🚀
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
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JSON / CSV structured output
✅
API & no-code workflows
Stop spending time ma
SQL Query Execution Order
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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
❔
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
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