Classification is a common task in machine learning, where the goal is to classify or label given data or observations. Specifically, in the field of machine learning, binary classification is the most common type. …

Exploring the Benefits and Applications of this Emerging Technology — Differentiable programming is a relatively new approach to machine learning that is designed to make it easier and more efficient to train machine learning models. …

From Adversarial Robustness to Privacy-Preserving Machine Learning, Discover the Cutting-Edge Developments in AI Machine learning is one of the hottest and most exciting fields in technology today. With new breakthroughs happening all the time, it can be tough to keep up with what’s new and what’s next. But don’t worry…

Maximizing Data Accuracy and Reliability with Mean, Regression, and Multiple Imputation Techniques — Missing values are a common occurrence in most datasets, and they can have a significant impact on the results of data analysis. I have come across many datasets that contain missing values. In many cases, missing values can be due to various reasons, such as data entry errors, miscommunication, or…

Why should we focus on Explainable Machine Learning?? Model debugging, Model monitoring, and Model Transparency for Explainable ML — When I first started learning Machine Learning (ML) four or five years ago, the environment was that you were set to go if you knew how to implement domain-specific supervised and unsupervised approaches. As things evolve, so do the expectations from data scientists (or machine learning engineers). Because the field…

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 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, 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. …