Thoughts and Theory

A glance at what Information Theory is and how Machine Learning relies on it.

Shannon Entropy : Explained
Shannon Entropy : Explained
Photo by Janko Ferlic from Pexels

Information. This term has been thrown around in every possible scenario. But rightfully so, the world runs on Information. So, what is it? The simplest way to explain it would be through an example. Imagine you are grocery shopping and you have picked up multiple items. You know the prices of those items; hence that is raw data for you. Later, when you check out at a counter, the cashier will scan those items and give you the summed cost of the items. To elaborate, the cashier will process the number of items with the cost of each item and…

Here we are, at the final part of the Heart Failure Detection series. In the previous articles, I discussed the dataset, performed some basic EDA and Feature Engineering and finally baseline modelling. Now, this part entails the process of Hyperparameter Optimization for the models and creating an inference pipeline.

The notebook and the dataset I am referring to are in my GitHub repository, in case you want to dive into it (Link:

Hyperparameter Optimization

Hyperparameter Optimization is the problem of choosing a set of optimal hyperparameters for a learning algorithm. There are several approaches to it, such as Grid search, Random…

In the last part of this series, I discussed the dataset and performed some basic EDA. So, the subsequent step should be harnessing these insights using Feature Engineering and baseline modelling. Hence, this article will focus on these two steps.

The notebook and the dataset I am referring to are in the Github repository, in case you want to dive into it (Link: If you want to follow the previous article in the series, here is the link

Feature Engineering

Feature Engineering is a process of transforming the given data into a form that is easier to interpret. From the…

Machine Learning in the medical field has come a long way, solving one complicated problem after another. As a result, medical professionals have started relying on Machine Learning tools to detect various diseases. Today, I will be demonstrating one such use case, Heart Failure.

Heart failure is a condition in which the heart can’t pump enough blood to meet the body’s needs. In some cases, the heart can’t fill with enough blood. In other cases, the heart can’t pump blood to the rest of the body with enough force. It is a serious condition and requires immediate attention.

There are…

Kaggle hosted the Aptos Blindness Challenge in September 2019. Although I am quite late to the party, I thought I would try it out anyways. The competition is about detecting Diabetic Retinopathy from images of pupil, taken across various hospitals in India. The implementation can be found on my GitHub (

I will be posting my implementations in multiple parts. This first part entails a basic EfficientNet implementation on default images (no preprocessing of images). This article will be divided into 3 major sections:

  1. Data Exploration
  2. Modelling
  3. Inference on a Test Set
  4. Scope for improvement

Data Exploration

What is Diabetic Retinopathy?


Preeyonuj Boruah

Data Scientist | Machine Learning enthusiast | Ethical Hacking hobbyist

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