Statistical Learning
Statistical Learning is an introductory supervised learning course that focuses on regression and classification approaches. This is one of the best online courses to learn business intelligence. The course covers linear and polynomial regression, logistic regression, and linear discriminant analysis; cross-validation and the bootstrap; model selection and regularization methods (ridge and lasso); nonlinear models, splines, and generalized additive models; tree-based methods, random forests, and boosting; support vector machines; neural networks and deep learning; survival models; and multiple testing. Principal components and clustering are two unsupervised learning approaches addressed (k-means and hierarchical).
At a glance
- Institution: StanfordOnline
- Subject: Data Analysis & Statistics
- Level: Introductory
- Prerequisites: First courses in statistics, linear algebra, and computing.
- Language: English
- Video Transcript: English
What you'll learn
- Statistical Learning Overview
- Classification using linear regression
- Methods of resampling
- Selection and regularization of linear models
- Beyond the limits of linearity
- Methods based on trees
- Vector machines should be supported.
- Learning at a deeper level
- Modeling for survival
- Learning without supervision
- Several tests were conducted.
Instructors: Trevor Hastie, Robert Tibshirani
Price: $149
Website: https://www.edx.org/course/statistical-learning
