#### Yugesh Verma

Yugesh is a graduate in automobile engineering and worked as a data analyst intern. He completed several Data Science projects. He has a strong interest in Deep Learning and writing blogs on data science and machine learning.

### What is the Poisson process and how is it used in data science?

Various probability theories enable us to calculate and interpret the distribution of randomly selected variables. We mainly find the use of the Poisson process and distribution when the number of upcoming events is large and their probability of occurring is very low.

### A beginner’s guide to Eigendecomposition from scratch

Mathematically, Eigen decomposition is a part of linear algebra where we use it for factoring a matrix into its canonical form. After factorization using the eigendecomposition, we represent the matrix in terms of its eigenvectors and eigenvalues.

### How to ensure privacy of training data with Tensorflow Privacy?

Developing a high-performing and accurate model blessing to a data scientist but maintaining the privacy of the data while training is one of the tasks

### A guide to publication-quality data visualization using Proplot

Proplot is a wrapper of the matplotlib library for the visualization of data.

### A guide to Base Rate Fallacy in machine learning

The base rate fallacy is a kind of fallacy that is also known as base rate bias and base rate neglect. This kind of fallacy has information about the base rate and specific information. There can be ignorance of base rate data in favor of individuating data.

### A beginner’s guide to stacking ensemble deep learning models

the idea behind stack ensemble method is to handle a machine learning problem using different types of models that are capable of learning to an extent, not the whole space of the problem. Using these models we can make intermediate predictions and then add a new model that can learn using the intermediate predictions.

### One vs One, One vs Rest with SVM for multi-class classification

In machine learning, binary classification algorithms become one of the most important and used algorithms when things come into the accuracy part of modelling. In

### A hands-on guide to ridge regression for feature selection

One of the most important things about ridge regression is that without wasting any information about predictions it tries to determine variables that have exactly zero effects. Ridge regression is popular because it uses regularization for making predictions and regularization is intended to resolve the problem of overfitting.

### A hands-on guide to anomaly detection in time series using ADTK

ADTK is an open-source python package for time series anomaly detection. The name ADTK stands for Anomaly detection toolkit. This package is developed by ARUNDO. Its features enable us to implement pragmatic models very easily, and also these features make ADTK different from other anomaly detection tools.

### A complete tutorial on Ordinal Regression in Python

In statistics and machine learning, ordinal regression is a variant of regression models that normally gets utilized when the data has an ordinal variable. Ordinal variable means a type of variable where the values inside the variable are categorical but in order.

### A beginner’s guide to Bayesian CNN

Applying bayesian on neural networks is a method of controlling overfitting. We can also apply bayesian on CNN to reduce the overfitting and we can call CNN with applied Bayesian as a BayesianCNN.

### A beginner’s guide to text regression with AutoKeras

We can think of text regression as a method of using attributes from the text data as a covariate in regression models. There are various fields where we may require regression analysis methods such as predicting salary based on the text where work requirement is mentioned or views on any website based on the content written on the website. The basic difference between text classification and text regression is the target variable.