Natural Language Processing (NLP) is a domain of research whose objective is to analyze and understand human languages and develop technologies to enable human machine interactions with natural languages. NLP is an interdisciplinary field involving linguistics, computer sciences and artificial intelligence. The goal of this course is to provide students with comprehensive knowledge of NLP. Students will be equiped with the principles and theories of NLP, as well as various NLP technologies, including rule-based, statistical and neural network ones. After this course, students will be able to conduct NLP research and develop state-of-the-art NLP systems.

Natural Language Processing course (stream 5, autumn 2023)

Дизайн систем машинного обучения. Введение для начинающих.

ML System Design. Autumn 22/23

Особенности применения NLP в практических задачах

NLP in practice

mlcourse.ai is an open Machine Learning course by OpenDataScience (ods.ai), led by Yury Kashnitsky (yorko). Having both a Ph.D. degree in applied math and a Kaggle Competitions Master tier, Yury aimed at designing an ML course with a perfect balance between theory and practice. Thus, the course meets you with mathematical formulae in lectures and a lot of practice in a form of assignments and Kaggle Inclass competitions. In 2017-2019, some 26k people participated in active course sessions offered twice a year, and ~1500 finished the course. ODS has witnessed multiple stories about the course changing careers of those who had passed it. Currently, the course is in a self-paced mode. Here we guide you through the self-paced mlcourse.ai.

Open Machine Learning course mlcourse.ai (self-paced)

AIJ Contest - an international online data analysis and AI competition held among industry developers and experts worldwide since 2016.

AIJ Contest

ПРО//ЧТЕНИЕ - серия технологических конкурсов для разработчиков в области машинного обучения.

Up Great. ПРО//ЧТЕНИЕ

Лучшие из лучших в 2020 году

🎄ODS Awards 2020 ⭐

Graph Machine Learning is the science between graph theory and machine learning. It is now a very active area of research, with hundreds of publications each month, impactful applications in the industry, and new insights about our world. Intersection of graph theory and machine learning. Overview, SOTA, applications.

Graph ML

Our goal is to engage Data Scientists in the process of creating objects and tools of modern media and technological art. Creating collaborations between artists and scientists.
During the conference we will hold several meetups with talks from various artists working with machine learning.

ML Art

Расскажем про оптимизацию ритейл сети, учёт причинно-следственных связей, Positive Unlabeled learning и про акселератор для стажёров. Также вас ждет воркшоп по основам анализа геоданных в Python и технический data science квест.

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