Learn some of the advanced concepts that make Support Vector Machine a powerful linear classifier

SVM’s soft margin formulation technique in action


Support Vector Machine (SVM) is one of the most popular classification techniques which aims to minimize the number of misclassification errors directly. There are many accessible resources to understand the basics of how Support Vector Machines (SVMs) work, however, in almost all the real-world applications (where the data is linearly inseparable), SVMs use some advanced concepts.

If you want to get a refresher on the basics of SVM first, I’d recommend going through the following…

Learn how Machine Learning is being used to recommend fitting sizes of clothing products to customers


Who doesn’t love online shopping? It is convenient, offers a wide selection of products and we get great deals. Retailers also benefit from the reach internet provides and get a chance to establish their brand. As a result, the online fashion industry has seen tremendous growth in recent years. However, shopping for clothes online is still tricky owing to wide sizing variations across different brands that make it tough for customers to identify proper fitting clothes. …

Learn in-depth about the magic of EM algorithm and start training your own graphical models

What happens when your inference algorithm is not good 😅[Source: https://bit.ly/2RSKuoI]


The goal of this post is to explain a powerful algorithm in statistical analysis: the Expectation-Maximization (EM) algorithm. It is powerful in the sense that it has the ability to deal with missing data and unobserved features, the use-cases for which come up frequently in many real-world applications.


  • Patience (since this post is detailed and dives deep into mathematical concepts).
  • Basic Probability concepts.
  • Logistic Regression.
  • Iterative optimization…

Rishabh Misra

ML Engineer @ Twitter | UCSD Alum | rishabhmisra.github.io

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