Project Jupyter’s Post

🎦 Watch the recording of Madelon Hulsebos's seminar at BIDS! "There is actually a lot of structured data available on the web.... But really, the task here is that we should be able to retrieve that easily." #datascience #structureddata #dataretrieval https://lnkd.in/gJtQac9g

To hell with Data Prep: Be the Data Scientist of your Childhood Dreams! Excited to share my talk at the Open Data Science Conference (ODSC) community where we delved into radically new methods for constructing highly accurate models over both structured and semistructured data. Check out the details here: https://lnkd.in/deqSSe4Y Kumo.AI

Special shoutout to Grishma Jena who's NDC Porto talk "Data Science beasts (failures) and where to find them" was just uploaded. If you haven't had any experience with data science projects yet and don't know if it's worth it or what to expect - especially if you're a lead or manager - this is the talk to watch! #NDC #NDCConferences #NDCPorto #DataScience #MachineLearning #ML P.S. I was the one with the wand. P.P.S. It wasn't actually a wand; it was my Apple Pencil. P.P.P.S. I didn't pick the Apple Pencil; it picked me.

We are excited to have 🐍 Matt Harrison presenting at #ODSCWest 2024! 🎉 Join us as he dives into overcoming the challenges of integrating CatBoost and SHAP, and explore solutions beyond just XGBoost. Don’t miss out on this valuable session from one of the leading experts in the field. 🚀 Make sure to grab your ticket and join the conversation on cutting-edge machine learning techniques - https://lnkd.in/drjNivMk #ODSCWest #MachineLearning #AI #CatBoost #XGBoost #DataScience

CatBoost is awesome. SHAP is awesome. But it's a classic case of "they're perfect for each other, except for one *small* incompatibility." SHAP just doesn't love visualizing categorical features—exactly where CatBoost shines. So, what do I do? Jump through some hoops to get these two on the same page. I'll share all the details on this in my upcoming Professional XGBoost course (yes, there are lessons here for more than just XGBoost!) and discuss it at the Open Data Science Conference (ODSC) later this month. Come learn how to make them work together.

Tons of interest around LLM Evals and Synthetic Data at ODSC! DM us if you want a copy of the presentation. #odsc #llm #syntheticdata #responsibleai #finetuning

Excited to attend the Tech Guide 1.0 virtual event hosted by Tech Hierarchy 🚀 This event promises to be a great learning experience with insightful discussions on the latest tech trends. Looking forward to connecting with fellow professionals and diving into the data science. #TechGuide1 #TechHierarchy #TechCommunity

Hi network, I’ll be speaking at PyData Global 2024 alongside Egor Kraev, our data science lead at Wise! 🎉🎉 I’m thrilled to invite you to our session titled “Fast, intuitive feature selection via regression on Shapley values” at PyData Global 2024. This incredible event brings together some of the brightest minds in data science, machine learning, and beyond, and we’re honored to be a part of it! 📅 When: 04-12-2024, 14:30–15:00 (UTC) 📍 Where: Online – accessible from anywhere in the world! In our talk, we’ll explore our feature selection framework, shap-select. For a deeper dive, check out our paper and our repo here: https://lnkd.in/e6PYcqTJ, https://lnkd.in/e4Vsnu3b. Whether you’re a product manager, data science enthusiast, machine learning practitioner, or simply curious about feature selection, this session will have something for you. We can’t wait to share our learnings and engage in discussions with all of you! 👉 Register here to attend live: https://lnkd.in/dZkJ_FAM. Let’s connect, learn, and grow together at PyData Global 2024! See you there!🙌 #PyDataGlobal #DataScience #MachineLearning #FeatureSelection

🚀 Day 122 in my data science journey! Today's highlights: 🔍 Understanding Covariance: Interpretation and calculation. ⚠️ Disadvantages: Limitations explored. 🔄 Covariance with Itself: Demystified! 🔍 Purpose of Correlation: Solving relationship problems. 📈 Exploring Correlation: Definition and differences from covariance. 🔍⚖️ Correlation vs. Causation: Important distinction! 📊 Visualizing Multiple Variables: Insights unlocked! Excited for more discoveries! 💡 #DataScience #LearningJourney 📚