Converting Categorical Data using OneHotEncoding — We cannot directly feed categorical data into Machine Learning (ML) algorithms. We must provide a numerical representation to ML models of the categorical features of a dataset. While working in python, we generally use label encoder, get dummies, and OneHotEncoder for converting the categorical features. However, in PySpark, we can…

Achieving size, latency and portability for tinyML — Machine Learning (ML) has been the talk of the decades with successful real-world applications in every aspect of our life ranging from health informatics to business to cybersecurity. Embedding ML directly on edge devices revolutionize the research and applications of ML on inter of things (IoT), where thousands of thasounds…

Data is stored with Boolean bits at the lowest level in classical computing, where each bit can take only one of two possible values (0 or… — Data is stored with Boolean bits at the lowest level in classical computing, where each bit can take only one of two possible values (0 or 1) depending on the existence of electron charge: the existence of electron charge indicates 1, otherwise 0. On the other hand, the basic unit…

In the pipeline of a Machine Learning model, there are a few environments that we consider — research environment, development environment… — In the pipeline of a Machine Learning model, there are a few environments that we consider — research environment, development environment, and production environment. In the research environment, different integral parts of model development are performed such as exploratory data analysis (EDA), model building, model evaluation, and result analysis. …

Recently, I come to know about one of the most interesting experiments that were conducted in the last century. It was quite surprising to… — Recently, I come to know about one of the most interesting experiments that were conducted in the last century. It was quite surprising to swallow! The experiment was conducted in 1996 to study the effect of smoking on a sample of over 1300 English women. The experiment was conducted for…

Bayesian optimization with Gaussian processes and Random Search Optimization methods for Optimal DNN in application to Network Intrusion… — Deep neural networks (DNN) have been successfully applied for many real-world problems ranging from disease classification to cybersecurity. The optimal use of DNN-based classifiers requires careful tuning of the hyper-parameters. Manually tuning the hyperparameters is tedious, time-consuming, and computationally expensive. Hence, there is a need for an automatic technique to…

What Central Limit Theorem Tells You??? — Fundamentals of Statistics Central Limit Theorem (CLT) is a fundamental and key concept in statistics/ probability theory. In this article, we will discuss: Central Limit Theorem Visually Understand the CLT Why sample size should be a minimum of 30? Effect of Sample Size in Central Limit Theorem …

Introduction to PySpark for Big Data Analytics — After you become comfortable with applying. Machine learning methods on your local machine, the next you can do is to use a cloud server like AWS to increase your skill. This will introduce you to a new set of challenges ranging from pretty simple to hard problems. Sometimes a simple…

Application of Prims’ and Kruskals’ algorithms towards Network Topology — Application of Prims’ and Kruskals’ algorithms towards Network Topology A network topology is a significant procedure of a network where devices (nodes) are interconnected in the network using network lines such as ethernet. The topology also describes the process of transferring data between the connection of the network. Thus, the…