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

MLOps и production подход к ML исследованиям 2.0

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)

Дизайн систем машинного обучения. Введение для начинающих.

ML System Design. Autumn 22/23

MTC. Your first recsys

Трек для глубокого изучения рекомендательных систем на практике

Your Second RecSys

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.

Active

MLOps и production подход к ML исследованиям 2.0

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

ML System Design. Autumn 22/23

MTC. Your first recsys

Your Second RecSys

Natural Language Processing course (stream 2, spring 2022)

MLOps и production подход к ML исследованиям