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In this article, we will explain what is Gradient descent from scratch, why it is important, and pick you up with simple math examples.
Now, in this article, we will be going through some of the advanced concepts for the Bayesian decision theory for data scientists
Bayesian decision theory refers to the statistical approach based on tradeoff quantification among various classification decisions
A normal (or Gaussian) distribution is a type of continuous probability distribution. Learn about transformation of normal distribution
Regularization is one of the most important concepts of ML. Learn about the regularization techniques in ML and the difference between them
Unlock the full potential of the Expectation-Maximization Algorithm with this comprehensive guide. Learn its intricacies and applications.
Cross-validation is a resampling technique. This article covers various cross-validation methods in machine learning to evaluate models.
Explore outliers in data with our guide on types, detection methods, and treatment techniques like trimming and capping. Learn more!
Order statistics are a useful concept in statistical science. This is an introduction to order statistics and its distribution
Five number summary is a part of descriptive statistics. Learn how to calculate 5 number summary and how it is used in interpretation of data
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