Дизайн систем машинного обучения. Введение для начинающих.

ML System Design. Autumn 23/24

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)

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 3, autumn 2022)

Курс по графам знаний (Knowledge Graphs) и как их готовить в 2021 году.

KG Course 2021

ML System Design. Autumn 22/23

Трек LightAutoML - создание, ускорение и кастомизация пайплайнов автоматизированного машинного обучения

AutoML Course часть 1

Продвинутые подходы и инструменты для разработки и внедрения ML-решений в production.

MLOps и production подход к ML исследованиям

Курс о линейной зависимости: поговорим о линейной и логистической регрессиях, метриках, валидации, генерации признаков и применим знания в конкурсе "Alice".

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