A journey into three intuitions: Common, Bayesian and Causal

A breakdown of the three fundamental math fields required for machine learning: statistics, linear algebra, and calculus.

Using Python to determine where NBA coaches come from and what makes them successful

Using one model to personalize ML results

These common, basic statistics and machine learning concepts are crucial for landing a data scientist role

Monitoring is easy; what to monitor is not. In the field of machine learning, data drift is just noise until you know what it means.

From last-minute goals to coin tosses: How much does randomness influence the outcomes of games?

for UX people and anyone who wants to experiment better (some willingness to deal with statistics and Python required)

A brief analysis using density estimation to compare the two-verdict and three-verdict systems.

Using GPU acceleration to speed up Bayesian Inference from months to minutes...

Understanding and Implementing Brant’s Tests in Ordinal Logistic Regression with Python

Learn Causal Structures and make inferences with Bayesian Methods: Python Tutorial The post A Practical Starters’ Guide to Causal Structure Learning with Bayesian Methods in Python appeared first on…