Michael Wiktorek’s Post

Great point. Labels are often the most overlooked part of a machine learning pipeline, yet they hold the key to unlocking better performance. In my experience, fixing labels not only improves your current dataset but also helps you uncover systematic issues in your labeling process. I’d add that it’s not just about correcting existing mistakes it’s about rethinking how you approach labeling altogether. Implementing strategies like active learning to prioritize ambiguous cases or using semi-automated labeling with human validation can make a huge difference. Regular audits of your labels also go a long way in maintaining quality and consistency. More data isn’t always the answer. Clean, accurate labels are often all you need to elevate your model’s performance. #MachineLearning #AI #DataScience #MLTips #DataQuality #AIInnovation #TechLeadership #MLModels #AIOptimization

Interesting essay about how to get further with classic ML algorithms by going beyond default parameters

Ever wondered how companies process millions of transactions in real-time using ML? The secret lies in algorithm optimization. From standard to optimized implementations: Linear Models: O(n) → O(n/w) with SIMD Random Forest: O(th) → O(tlog(h)) with LightGBM k-NN: O(n*d) → O(log(n)) with modern variants I prepared an analysis of the performance trade-offs of traditional ML algorithms, and implementation considerations. Perfect for ML practitioners focused on system optimization.

Anomaly detection involves spotting unusual patterns in data through statistical techniques like the Z-score or machine learning methods such as One-class SVM . This approach is useful for identifying fraud, system failures, and outliers by utilizing tools like Isolation Forest and Autoencoders 🔄 #AnomalyDetection #Outliers #MachineLearning #DataScience #FraudDetection #Datalyticsai

Machine Learning in Financial services - a brief view point.

Machine learning has long found itself in the toolkit of many fraud and risk engineering teams. Yet dated and underpowered tooling has often left ML teams plagued with headaches that have them worrying more about infrastructure than iterating. Today on The Chalkboard, we break down how leveraging a feature store drastically simplifies shipping production-grade fraud models. Check out “Feature Store at Work: A Tutorial on Fraud and Risk”! Link in comments!

Practical one this on using ML https://lnkd.in/gCC9-Bd7

Hello Everyone, "Day 10 of my 30-day machine learning challenge! 🚀 Just completed a project on detecting credit card fraud using ML techniques. Excited to share my progress and learnings! 💳🔍 GitHub link :https://lnkd.in/d3JBWc4R dataset link :https://lnkd.in/djxJuFGt #MachineLearning #DataScience #30DaysOfML #FraudDetection"

🤖💡 Discover how machine learning is transforming trading! 🌟 At Global Financial Engineering, Inc., we integrate ML models into our trading algorithms to enhance GATS and achieve superior outcomes. Learn more in our latest article. 📈 #MachineLearning #AlgorithmicTrading #PropTrading #FinancialEngineering #GATS #Finance #DrGlenBrown 📖 Read the full article: https://lnkd.in/e7rYm9w3

🤖💡 Discover how machine learning is transforming trading! 🌟 At Global Financial Engineering, Inc., we integrate ML models into our trading algorithms to enhance GATS and achieve superior outcomes. Learn more in our latest article. 📈 #MachineLearning #AlgorithmicTrading #PropTrading #FinancialEngineering #GATS #Finance #DrGlenBrown 📖 Read the full article: https://lnkd.in/e5wjhUBh