Formulating the Principal Component Analysis (PCA) Algorithm From Scratch
Approaching PCA as an optimization problem.
A daily column with insights, observations, tutorials and best practices on data science. Relevant for industry professionals at big tech, startups, and engineering students.
Approaching PCA as an optimization problem.
The limitations of linear regression and how GLMs solve them.
Where did the regularization term come from?
Gaussian Mixture Models: A more robust alternative to KMeans.
The caveats of grid search and random search and how Bayesian optimization addresses them.
The origin of the Sigmoid function and a guide on modeling classification datasets.
A Comprehensive Guide to Pytest for data scientists.
The most extensive and in-depth guide to linear regression.
A daily column with insights, observations, tutorials, and best practices on data science.
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