Roshna S H

Dr. Roshna S H is a scientist by profession with more than five years of teaching experience at graduate and postgraduate levels. She has obtained a Ph.D. from IIT Madras after M.Sc., M. Phil in Physics. She has also cleared CSIR-NET and GATE with top ranks. She is a constant content creator through publications in peer-reviewed international journals and by writing concept based blog articles. She has research experiences in diverse areas spans from atmospheric science to optics as well as spintronics and magnetism. She has obtained awards and recognitions at various international platforms for her contributions as scientific articles and oral presentations. Dr. Roshna completed her Ph.D. with exposure at American physical society, Material research society, University of Oxford, etc. She focuses on communicating the concepts with utmost clarity in the simplest possible way.

Python Foundations-4: Intermediate Probability and Statistics Challenges for Machine Learning Mastery

Welcome back to the “Python Foundations” series! In our previous articles, we explored beginner-level probability and statistics problems to build a strong foundation for machine learning. Now, let’s delve into intermediate level probability and statistics challenges for machine learning that will further enhance your skills in this crucial aspect of data science. Problem 1: Confidence …

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Python Foundations-3: Progressive Probability and Statistics Challenges for Machine Learning Enthusiasts

Building on the fundamental concepts covered in the initial articles of the “Python Foundations” series, we are excited to continue our exploration with “Python Foundations-3: Progressive Probability and Statistics Challenges for Machine Learning.” In this article, we present a set of beginner-level problems with a slight increase in difficulty, each accompanied by detailed explanations and …

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Python Foundations-2: Beginner Probability and Statistics Problems for Machine Learning

Building on the fundamental concepts covered in the initial article of the “Python Foundations” series, we are excited to continue our exploration with “Python Foundations-2: Beginner Probability and Statistics for Machine Learning.” In this article, we present 10 beginner-level problems, each accompanied by detailed explanations and Python code, aimed at reinforcing your understanding of probability …

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Python Foundations for Machine learning-1: Exploring Fundamental Probability and Statistics

In the ever-evolving landscape of machine learning, a profound understanding of probability and statistics forms the bedrock upon which intelligent algorithms thrive. In this inaugural article of the “Python Foundations for machine learning” series, we embark on a journey delving into the fundamental principles of probability and statistics, unravelling their significance in the context of …

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The Subtle art of Not Giving a F*ck

Navigating Life’s Uncertainties: A Candid Exploration of “The Subtle Art of Not Giving a F*ck” by Mark Manson

In a world saturated with motivational literature, Mark Manson’s “The Subtle Art of Not Giving a F*ck” stands out as a beacon of unfiltered wisdom. Released in 2016, this book provides a candid and pragmatic approach to navigating life’s complexities. Manson challenges traditional self-help narratives, encouraging readers to confront life’s struggles head-on and redefine their …

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sot

Fundamental Concepts of Spin-Orbit torque based Magnetic RAM

Spin orbit torque based magnetic random access memory (SOT-MRAM) are non-volatile memory devices which make use of spin based transport electronics named as spintronics. In such devices, capacitors in conventional memory devices are replaced by a trilayer structure called magnetic tunnel junction (MTJ). As spin is non-volatile, it overcomes the charge leakage associated with conventional …

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rejection-proof

Rethink Rejection and Overcome Fear of Rejection: Book Review of Rejection Proof

The book titled “Rejection Proof” is written by Jia Jiang. The author had great entrepreneurial aspirations but got stuck to achieve his dreams by the fear of rejection. The author decided to overcome the fear of rejection and in order to achieve it, he designed a plan to take up a challenge he named “100 …

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Statistical Testing Methods in Machine Learning

Statistical testing methods are used in machine learning to ensure that a finding is statistically significant. Hypothesis testing most commonly termed statistical testing evaluates two mutually exclusive statements about a population and determines which among the two statements best describe the experimental/sample data. Hypothesis testing is a statistical method to determine whether two samples have …

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