In a world where data is abundant, leveraging machines to learn valuable patterns from structured data can be extremely powerful. In this course, we will explore the basics of machine learning, discussing concepts like regression, classification, model evaluation metrics, overfitting, variance versus bias, linear regression, ensemble methods, model selection, and hyperparameter optimization.
Come away with a strong understanding of the core concepts in machine learning and the ability to efficiently train and benchmark accurate predictive models.
Gain hands-on practice with powerful packages like scikit-learn, building complex ETL pipelines to handle data in a variety of formats and techniques, developing models with tools like feature unions and pipelines that allow them to reuse existing models and reduce duplicate work, and practicing tricks like parallelization to speed up prototyping and development.
Mini Project: Working with a real data sets students will take restaurant reviews and, based on various characteristics, build predictive models to predict the restaurant’s score.
Meet Your Instructors
Michael Li has worked as a data scientist (Foursquare, A16Z), quant (D.E. Shaw, J.P. Morgan), and a rocket scientist (NASA). He did his PhD at Princeton as a Hertz fellow and read Part III Maths at Cambridge as a Marshall scholar.
Francesco Mosconi is a Data Scientist at Catalit. Previously, he was the Chief Data Officer at Spire, a next-generation wearables company, a co-founder at Axelera, and an application engineer at Roche. He received a joint PhD from Université Pierre et Marie Curie (Paris VI) and Università degli Studi di Padova (Padua, Italy).
Ariel M'ndange-Pfupfu studied physics at Stanford and got an engineering PhD from Northwestern. Since joining The Data Incubator as a Data Scientist in Residence, he’s worked on a variety of data science and software engineering projects, as well as curriculum development and instruction.
Robert Schroll is a Data Scientist in residence at The Data Incubator and has been a key contributor to a variety of open source software development and data science projects. He received his PhD from the University of Chicago in computational physics and his undergraduate degree from Maryland.
$2995 Silver Package: The full 3 day course, including breakfast, lunch & networking drinks
$3295 Gold Package: The full 3 day course, including breakfast, lunch & networking drinks | Access to presentations from the latest Machine Learning Innovation Summit from the Innovation Enterprise
$3595 Full Access: The full 3 day course, including breakfast, lunch & networking drinks | Annual Subscription to Big Data & Analytics channels via ieOnDemand.com | 4000+ hours of on-demand presentations & case studies | 40hrs of new content added monthly
Corporate Bookings: We offer generously discounted rates for team bookings, please email email@example.com
The Data Incubators fellowship Data Science was named as on of the "15 Things That Are Harder To Get Into Than Harvard" by Business Insider
About The Data Incubator
The Data Incubator is a Cornell-funded data science training organization. They run an 8-week fellowship that was selected by Business Insider as one of 15 competitive programs in the US with more competitive admissions than Harvard. The Data Incubator was founded in 2014 in New York City by Michael Li, a former Data Scientist at local-mobile-social startup Foursquare and Andreessen Horowitz & Rocket Scientist at NASA. A variety of innovative companies partner with The Data Incubator for their hiring and training needs, including LinkedIn, Genentech, Capital One, Pfizer, and many others.