en
Feedback

Don't get caught by a cheater! Telemetrio finds and tags such channels 👉 If you want to see the tag, subscribe

Data Science | ML | AI Resources

Data Science | ML | AI Resources

Open in Telegram

Data Science community is built for Data Science Aspirants who wish to start their journey into the field Data Science. Download Free PDFs, Courses & Internships and Jobs related to Data Science and Machine Learning!

Show more
The country is not specifiedThe category is not specified
7 903
Subscribers
-424 hours
-267 days
-5530 days
Posts Archive
Repost from Anjana - AI & Tech
🚀 5 Unsupervised Machine Learning Algorithms You Should Know! 👇 https://www.instagram.com/reel/De52nqGSTSg/?rpxt=anpmenFob2
🚀 5 Unsupervised Machine Learning Algorithms You Should Know! 👇 https://www.instagram.com/reel/De52nqGSTSg/?rpxt=anpmenFob25kNjIz

Repost from Anjana - AI & Tech
Unsupervised Machine Learning Algorithms Complete Guide 👆

Repost from Anjana - AI & Tech
5 Most Important Unsupervised Machine Learning Algorithms You Should Know 👆

Repost from Anjana - AI & Tech
Asked in an Uber AI/ML Engineer interview 👇 Uber’s AI predicts that ride demand in a particular area will increase significantly in the next 20 minutes. How would you design an AI system that predicts demand and proactively moves drivers to that area? Prediction is the easy half. The value is in the allocation, and most candidates stop before they get there. 1. Forecast both sides, per cell per bucket - H3 hexagons × 5-minute buckets, forecasting rides requested and drivers available - Features: the same hour last week, weather, events, traffic, and live app-open sessions - Short horizons need current state; long horizons need seasonality 2. What matters is the gap - Rank cells by expected unmet demand, which is demand minus reachable supply - A surging cell that already has forty idle drivers needs nothing from you 3. Turn the forecast into assignments - For each driver, the expected value of moving versus staying, minus travel time and fuel - Solve it as min-cost flow across drivers and cells, not as a greedy nudge per driver - Greedy sends everyone to the same hex and creates the opposite problem 4. Drivers are people, not units of supply - Incentives and heatmaps, never orders — model the probability they accept - Respect end-of-shift, home direction and rest; an ignored nudge is worse than none 5. Do not stampede - Move a fraction of the nearby drivers, then re-solve a few minutes later - Closed-loop correction beats one confident dispatch 6. Evaluate on the marketplace - Geo-based A/B tests, because moving drivers changes the outcome for everyone in the city - Track unfulfilled requests, pickup ETA and driver earnings per hour together The principle: A demand forecast is only worth what the allocation on top of it earns. Optimise the assignment, not the RMSE. 💬 Incentives or dispatch — which moves drivers better in your experience?

Repost from Anjana - AI & Tech
Never Sit in AI Interview before knowing these concepts👆

Repost from Anjana - AI & Tech
Complete Machine Learning Engineer Roadmap - Download👆

Repost from Anjana - AI & Tech
🚀 Follow this roadmap to become a Machine Learning Engineer in 2026👇 https://www.instagram.com/reel/Des_6UOSplh/?exln=MWJva
🚀 Follow this roadmap to become a Machine Learning Engineer in 2026👇 https://www.instagram.com/reel/Des_6UOSplh/?exln=MWJvaXoxY2wxY3VpdA==

Repost from Anjana - AI & Tech
Complete Machine Learning Engineer Roadmap and Resources👇️️

Repost from Anjana - AI & Tech
🚀 STOP SCROLLING! These websites can level up your career in 2026 💻 Looking for a job? Want to build a standout resume or portfolio? Need to sharpen your technical skills? Bookmark these FREE & useful resources! 👇

Repost from Anjana - AI & Tech
8 Model Evaluation Techniques in Machine Learning 👆