Uber Eats debiases conversion rates with deep learning

Uber Eats tackles position bias with a cutting-edge deep learning approach. Their research team recently unveiled a novel method to mitigate position bias, where users tend to favor higher-ranked stores regardless of relevance. By refining their model architecture on biased interaction data, Uber Eats effectively debiases the conversion rate to reveal true conversion probabilities. Their innovative solution involves a deep learning CVR model with a dedicated position bias side tower, enabling simultaneous estimation of True CVR and Position Bias. Careful feature selection and regularization ensure each tower operates independently, enhancing home feed recommendations and boosting user orders. Dive into my detailed video exploring these biases in recommender systems and Uber Eats' groundbreaking approach. Video Link: youtu.be/ZCO75OuMRY0   Channel Link: youtube.com/@datatrek #datatrek #datascience #machinelearning #statistics #deeplearning #ai

Every data scientist should know this. Not tools. Not buzzwords. The 10 timeless foundations that separate good analysts from great ones 👇 #DataScience #MachineLearning #Analytics #AI #CareerGrowth

🚀 𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞’𝐬 𝐭𝐚𝐥𝐤𝐢𝐧𝐠 𝐚𝐛𝐨𝐮𝐭 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞. 𝐅𝐞𝐰 𝐫𝐞𝐚𝐥𝐥𝐲 𝐠𝐞𝐭 𝐢𝐭. 𝐃𝐨 𝐲𝐨𝐮? 👇 Most people think it’s just coding and AI. But that’s only the surface. 💡 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 = 𝐭𝐮𝐫𝐧𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐢𝐧𝐭𝐨 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬. It’s not about algorithms, it’s about understanding. Not about data, but about clarity. And no — you don’t need to be a math genius to start. I broke down what Data Science actually means here ⬇️ 🔗 Read the full story on Medium (link in comments). #DataScience #AI #MachineLearning #Analytics #LearningJourney #CareerGrowth

Mastering Data Science is a must-read for anyone eager to turn data into impactful insights. It covers everything from machine learning to AI with practical, real-world examples. #DataScience #MachineLearning #AI #Analytics #BigData #DeepLearning #CareerGrowth

Concepts I’m Learning in Data Science I’ve been learning about two interesting types of machine learning models — Predictive and Probabilistic. Here’s how I think of them #DataScience #MachineLearning #LearningJourney #AI #MLConcepts #DataScienceCommunity

This weekend we’re kicking off the 𝗔𝗜 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 at Treford — starting with 𝗠𝗼𝗱𝘂𝗹𝗲 𝟭: 𝗔𝗜 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝘀. In this first week, we’ll go back to the basics: what AI, ML, and Deep Learning actually mean and what every product manager needs to know to work effectively with Data scientist and Machine learning engineers. The goal: build a strong foundation before we dive into strategy, design, and ethics in the coming weeks. Big thanks to Harry ENAHOLO, Toyin Olasehinde, Esther Oluwafunmilayo Bangboje, Majana Havranek 🙏 Check it out here 👉  https://lnkd.in/dtEvZa82

🚀 Statistics Roadmap for Machine Learning When we step into the world of Machine Learning, one thing becomes clear — Statistics is the backbone of everything we build. Before diving deep into algorithms, it’s important to understand the “why” behind data behavior. Here’s a roadmap of must-know Statistics concepts for anyone starting in Machine Learning 👇 📘 1. Descriptive Statistics Mean, Median, Mode Variance & Standard Deviation Skewness and Kurtosis Percentiles and Quartiles 📊 2. Probability Fundamentals Conditional Probability & Bayes Theorem Random Variables Probability Distributions (Normal, Binomial, Poisson, etc.) 📈 3. Inferential Statistics Sampling & Sampling Distribution Confidence Intervals Hypothesis Testing (p-value, t-test, chi-square, ANOVA) 🧮 4. Correlation & Regression Correlation vs. Causation Simple and Multiple Linear Regression Residuals and R² Interpretation 📉 5. Advanced Topics Central Limit Theorem Z-score & Outlier Detection Maximum Likelihood Estimation Statistical Significance in ML models 💡 Why it matters: These concepts help us understand data patterns, validate assumptions, and make better predictions — making our models not just accurate, but explainable. 📚 I’m currently exploring these topics as part of my Machine Learning journey. Excited to share more as I progress! #MachineLearning #Statistics #DataScience #LearningJourney #AI #MLRoadmap

In Data Science, tools will change. **Tech stacks will evolve. **AI will automate tasks we do today. But one thing will always stay valuable: Your ability to learn fast. The most successful Data Professionals aren’t the ones who know everything. They’re the ones who adapt, experiment, and stay curious. If you learn a little every day, you will never fall behind. What are you learning this week? #DataScience #MachineLearning #AI #LearningMindset #Analytics #CareerGrowth #FutureOfWork

Start with a hook "Data Science is not just about coding it’s about asking the right questions.” Then explain in simple terms how it combines statistics, machine learning, and domain expertise to extract insights from data. . . . . #DataScience #AI #MachineLearning #Analytics

Today, while working on a Generative AI based solution , I had to write a few SQL queries to prepare a test dataset. Funny enough, that small step reminded me how much the fundamentals still matter, even in the GenAI era. No matter how advanced our tools get, understanding the basics like SQL is still as valuable as exploring modern concepts like vector databases. Another key takeaway was about evaluation. Just like in predictive AI, building a great Generative AI solution isn’t only about creativity or model power, it’s about systematic evaluation. Using the right evaluation matrices to prioritize and measure results is what keeps innovation grounded in real value. Yet, this is something I often see being overlooked, especially by people entering the field from non–data science backgrounds. In the end, combining solid fundamentals with a structured, evaluation-driven mindset is what truly sets a Data Scientist apart. #GenerativeAI #DataScience #MachineLearning #Evaluation #SQL #LLMs #VectorDatabases