Title: Statistical Learning with Python

Location: US

Company: Stanford University

About this course:
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: 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; multiple testing. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).
This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data science. Computing in this course is done in Python. There are lectures devoted to Python, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chatper. We also offer the separate and original version of this course called Statistical Learning with R – the chapter lectures are the same, but the lab lectures and computing are done using R.
The lectures cover all the material in An Introduction to Statistical Learning, with Applications in Python by James, Witten, Hastie, Tibshirani, and Taylor (Springer, 2023. The pdf for this book is available for free on the book website.

Overview of statistical learning

Linear regression

Classificaiton

Resampling methods

Linear model selection and regularization

Moving beyond linearity

Tree-based methods

Support vector machines

Deep learning

Survival modeling

Unsupervised learning

Multiple testing

Learn some of the main tools used in statistical modeling and data science. We cover both traditional as well as exciting new methods, and how to use them in Python.

Duration: 11 weeks

Skills:
- Artificial Neural Networks
- Basic Math
- Boosting
- Bootstrap (Front-End Framework)
- Data Science
- Deep Learning
- K-Means Clustering
- Lasso (Programming Language)
- Lecturing
- Linear Discriminant Analysis
- Logistic Regression
- Polynomial Regression
- Principal Component Analysis
- Python (Programming Language)
- Random Forest Algorithm
- Statistical Learning Theory
- Statistical Modeling
- Statistics
- Supervised Learning
- Support Vector Machine
- Unsupervised Learning

Curriculum:
- Overview of statistical learning
- Linear regression
- Classificaiton
- Resampling methods
- Linear model selection and regularization
- Moving beyond linearity
- Tree-based methods
- Support vector machines
- Deep learning
- Survival modeling
- Unsupervised learning

- Multiple testing