Python Tutorials and Examples

Empirical distribution in Python describes the distribution of data from what is observed rather than having an underlying assumption. It represents the frequency or proportion of observations falling into a particular range by using histograms, cumulative distribution functions (CDFs), or probability mass functions (PMFs). It is a type of deductive distribution technique that makes direct

Optimization is one of the most fundamental functions of any algorithm. Unconstrained optimization problems refer to those problems where we need to maximize or minimize our function without any constraint on our decision variables. These problems find their usage in many fields such as economics, engineering, machine learning, and many more. The backtracking line search

It has become the need of the hour to use machine learning models and neural networks to detect fraud. The availability of modern technology has enabled us to detect scams without having to identify and scrutinize everything manually. In the era where digital currency dominates, the authentication of physical currency becomes more and more questionable.

Tensorflow is one of the most popular and widely used software applications in the world. Developers use it to create and implement deep learning algorithms. Even though it is popular for being easy to use, sometimes users might face difficulty when downloading and installing it in their system. These problems are mainly caused by dependencies,

Non-parametric statistics do not assume any strong assumptions of the distribution, which contrasts with parametric statistics. Non-parametric statistics focus on ranks and signs along with minimal assumptions. Non-parametric statistics focus on analyzing data without making strong assumptions about the underlying distribution. Python offers various methods for exploring data distributions, such as histograms, kernel density estimation

Maximum a posteriori ( MAP ) is an estimation technique that estimates the most probable value ( mode ) of an unknown quantity of some data. In this article, we calculate MAP using the combination of prior distribution and likelihood function which is Posterior Distribution. The posterior distribution reflects the belief about the unknown quantity

McNemar’s test is a statistical method used to compare paired categorical data with two categories, often used to evaluate the effectiveness of a treatment or intervention. It involves creating a 2×2 contingency table, calculating the test statistic, and comparing it against a chi-square distribution to determine statistical significance. Recommended: Understanding Bootstrap Statistics Understanding McNemar’s Test

As with any software or programming language, they evolve and the older versions reach the end of their support life cycle. The developer team no longer wants to update older versions when newer ones appear. The team only wants to keep the newer versions up-to-date so that they can support other applications and focus on