Jure Leskovec’s Post

Greetings connections, On Day 45 of my 100 Day Data Science Challenge, I explored functions that aid in manipulating an array. 1) Transpose of an array 2) Swapping axes 3) Flattening an array 4) Appending, adding, deleting elements 5) Making copies of an array With this, I wrapped up day 45. I am excited to explore new horizons in Data science tomorrow!! PS: You can refer to an article I have written on the same, explaining these with relevant codes.

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

Thanks to David Hoyle at dunnhumby and the Data Science Festival for a great session on demand modelling in the grocery retail industry! 🛒📊 🔍 Key Topics Covered: 1. Direct and Cross Price Elasticity: Understanding how changes in prices impact demand and how related products affect each other’s sales. 2. Practical Applications of Demand Models: Real-world scenarios where demand modelling plays a crucial role in decision-making. 3. Behind the Scenes: An introduction into the datasets, features, and models used to create accurate and insightful demand models.

A bit dated, but earlier this summer I participated in an advanced data science workshop focusing on causal inference. Thanks to Muhammad Noor E Alam, Sahil Shikalgar, and Kimberly Stochaj at the Decision Analytics Lab for organizing and instructing! It was interesting to dive deeper into the application of causal inference to large datasets and a great way to learn more about the matching techniques and other methods being developed to improve the robustness of findings.

More to you: Towards Data Science started as a side-project created to help the data science and machine learning community share concepts, ideas, and code. https://lnkd.in/dzq8Pg-b

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.

Navigating imbalanced datasets? The confusion matrix is your best ally! It not only measures accuracy but also highlights where your model can improve. Let’s harness its power for better decision-making in machine learning! Read Open Data Science Conference (ODSC)'s full article here: https://lnkd.in/gksTNkHm

Just like fine-tuning a race car for optimal performance, hyperparameter tuning adjusts specific settings within a machine learning model to achieve the best results. This infographic dives into the world of hyperparameter tuning and the importance of choosing the right algorithms for different tasks. Start your Data Science journey with us and join our 5-Day Data Science Bootcamp both online and in-person with industry experts now➡️ https://hubs.la/Q02NfYxj0 #HyperparameterTuning #MachineLearning #datascience

Just one week stands between us and Data Science Summit Machine Learning edition, which happens on 13th-14th June. I'm looking forward to listen to all the great talks we have in the lineup for this year and see all of you there on Friday in person on-site. If you are still undecided, here's a discount code. 😉 #DataScience #DSS #DSSML #MachineLearning