Тренировки ML

Your challenge is to tell a data story about college basketball through a combination of both narrative text and data exploration. A “story” could be defined any number of ways, and that’s deliberate. You are to deeply explore (through data) the mania of the Men’s and Women’s NCAA College Basketball tournaments.

Visualize and Present the CORD-19 dataset in intuitive manner

In this challenge participants will be asked to extract building footprints using a combination of these two data-sources over the port of Rotterdam.

Участникам предстоит построить алгоритм, который наиболее точно предскажет динамику числа зарегистрированных случаев заражения коронавирусом COVID-19 по каждой из стран на следующие семь дней.

In this competition, you’ll use ion channel data to better model automatic identification methods. If successful, you’ll be able to detect individual ion channel events in noisy raw signals. The data is simulated and injected with real world noise to emulate what scientists observe in laboratory experiments.

In this competition, you’ll create an AI that can solve reasoning tasks it has never seen before. Each ARC task contains 3-5 pairs of train inputs and outputs and a test input for which you need to predict the corresponding output with the pattern learned from the train examples.

The objective of this challenge is to interpret and generate insights from the data on the 2015 flooding and its aftermath specifically in southern Malawi.

Predict flood extent caused by storms in southern Malawi.

In this competition we've extracted support phrases from Figure Eight's Data for Everyone platform. The dataset is titled Sentiment Analysis: Emotion in Text tweets with existing sentiment labels, used here under creative commons attribution 4.0. international licence. Your objective in this competition is to construct a model that can do the same - look at the labeled sentiment for a given tweet and figure out what word or phrase best supports it.

In the previous 2018 Toxic Comment Classification Challenge, Kagglers built multi-headed models to recognize toxicity and several subtypes of toxicity. In 2019, in the Unintended Bias in Toxicity Classification Challenge, you worked to build toxicity models that operate fairly across a diverse range of conversations. This year, we're taking advantage of Kaggle's new TPU support and challenging you to build multilingual models with English-only training data.

In this competition, the fifth iteration, you will use hierarchical sales data from Walmart, the world’s largest company by revenue, to forecast daily sales for the next 28 days and to make uncertainty estimates for these forecasts.

In this competition, the fifth iteration, you will use hierarchical sales data from Walmart, the world’s largest company by revenue, to forecast daily sales for the next 28 days.