Русский 
English 
Чему вы научились?
What machine learning is and how it is related to statistics and data analysis
How machine learning uses computer algorithms to search for patterns in data
How to use data patterns to make decisions and predictions with real-world examples from healthcare involving genomics and preterm birth
How to uncover hidden themes in large collections of documents using topic modeling
How to prepare data, deal with missing data and create custom data analysis solutions for different industries
Basic and frequently used algorithmic techniques including sorting, searching, greedy algorithms and dynamic programming
О курсе
Machine Learning is a growing field that is used when searching the web, placing ads, credit scoring, stock trading and for many other applications.
This data science course is an introduction to machine learning and algorithms. You will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics. We will also examine why algorithms play an essential role in Big Data analysis.
Программа
Machine Learning for Data Science and Analytics
Learn the principles of machine learning and the importance of algorithms.
Лекторы
Ansaf Salleb-Aouissi
Department of Computer Science Columbia University
Cliff Stein
Professor of IEOR and of Computer Science Columbia University
David Blei
Professor of Computer Science and Statistics Columbia University
Itsik Peer
Associate Professor of Computer Science Columbia University
Mihalis Yannakakis
Professor of Computer Science Columbia University
Peter Orbanz
Assistant Professor of Statistics Columbia University
Платформа
Эта платформа предоставляет все курсы бесплатно. Авторами выступают топовые университеты и корпорации, которые стараются удерживать стандарты качества. За несоблюдение дедлайнов, невыполнение домашнего задания студенты теряют баллы. Как и в других платформах, лекционные видео чередуются с практическими заданиями. Обучение проводится на английском, китайском, испанском, французском и хинди.