I am very excited to announce that my new course on statistical learning is now available on openclassrooms.
In this course I explore linear, logistic and polynomial regression with hands on exercises, real-world use-cases and non trivial datasets.
Regression is the mother of all statistical models. Simple, flexible and highly interpretable.
To shine a new light on such a venerable topic, I decided to bridge the gap between the classic statistical approach and the machine learning one.
Regression in the statistical sense aims at modeling the inner dynamics of a dataset. The method uses multiple statistical tests to validate the relevance and reliability of the observations and results.
On the other hand, the machine learning approach strives to build models that perform well on previously unseen data. We no longer care about p-values, null hypothesis or statistical tests but focus instead on the performance of the trained model on new data.
The great thing is that we can use the same simple modeling techniques, linear regression to illustrate both approaches. Bridging the gap between statistical modeling and machine learning.
Here’s the outline of the course:
I - Understand the Fundamentals of Statistical Modeling
II - Build Linear Regression Models
III - Build Generalized Linear Models
IV - Build Resilient Predictive Models
The course notebooks are available on github: Notebooks for the course Design Statistical Models on OpenClassrooms