Course Overview
This course provides a broad introduction to machine learning. It covers statistical inference, regression models, machine learning, and the development of data products. Topics include: (i) Supervised learning (ii) Unsupervised learning (iii) Best practices in deploying machine learning to production. The course will also draw from numerous case studies and application.
Learning Outcomes
One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.