Showcased through personal experiences and tangible results.

Photo by Maria Teneva on Unsplash

Consistency is one of the most undervalued skills for any individual. It is nothing but something done ritualistically. It is very important for establishing yourself in this information-loaded world. Many people have put their feet into creative fields but failed, even though they are good enough. More often than not, the reason for this is not being consistent. I came to this realization after a long struggle, and I will say that I am not still at the level of consistency that I aspire to reach.

Let me tell you the power of consistency with a short story of my…


Its concepts and implications in Machine Learning

Photo by Hunter Harritt on Unsplash

With every passing day, the number of people in Data Science is increasing. Everything can be boiled down to its essentials, which is knowing your fundamentals in any academic/research setting. In the past, I have shared my knowledge about topics such as Types of Data Sets, Data Preprocessing, Data Preprocessing in Python, Linear Regression, Decision Trees, and Naive Bayes Classifier.

In this post, I will discuss the concepts of clustering. Clustering is a subset of Unsupervised Learning. It bears a vital role in Data Science. To say naively, the purpose of clustering to identify patterns within unstructured data. It applies…


A Brief Conceptual Overview

Photo by Uriel SC on Unsplash

With every passing day, the number of people in Data Science is increasing. Significantly, the domain of Deep Learning is an essential talking point of the Data Science community. Nevertheless, the fundamentals are the essentials in any academic/research setting. In the past, I have written about topics such as Types of Data Sets, Data Preprocessing, Data Preprocessing in Python, Linear Regression, Decision Trees, and Naive Bayes Classifier.

In this post, we will discuss the concepts of Artificial Neural Networks (ANNs). Understanding of ANNs is a must to apply advanced Deep Learning (DL) to real-world problems. These days, we use DL…


A python3 and scikit-learn implementation

Photo by Uriel SC on Unsplash

With every passing day, the number of people in Data Science is increasing. Especially, the domain of Deep Learning is the most essential talking point of the Data Science community. But everything can be boiled down to its essentials, which in any academic/research setting is knowing your fundamentals. In the past, I have shared my knowledge about topics such as Types of Data Sets, Data Preprocessing, Data Preprocessing in Python, Linear Regression, Decision Trees and Naive Bayes Classifier.

In this post, I will discuss a very simple implementation of Artificial Neural Networks (ANNs) using scikit-learn. Understanding of ANNs is a…


And methods to evaluate the performance of a classifier

Photo by Franck V. on Unsplash

If you are building any Machine Learning model, be it on dummy dataset or real world problem, the most important part is to determine how well your models works. This is determined usually with a combination of two approaches.

→ Using a performance metric and usage of methods that take the performance metric and provide empirical performance data.

This post will cover two broader aspects of classification models:

  1. Multiple Performance Measures for a Classification Model
  2. Different Methods to evaluate the performance based on the measures from point 1

The content covered will provide a conceptual grasp and they can be…


To increase your Instagram reach

Photo by Samuel Pereira on Unsplash

It is never easy to grow an audience it becomes rather painful when it comes to growing an audience that shares common interests with you and is relevant to you. I am a Computer Science student and a writer as well. I like to write quotes, poetry (in English and my mother tongue Hindi), and fiction stories.

For a long time, I wanted to share my work with the people of the world, and one day about six months ago, I decided to create an Instagram account specifically for these purposes. I posted content daily for more than 50 days…


Conceptually with example using ID3 algorithm

Photo by Kevin Ku on Unsplash

In this post, we are going to discuss the workings of Decision Tree classifier conceptually so that it can later be applied to a real world dataset.

Classification can be defined as the task of learning a target function f that maps each attribute set x to one of the predefined labels y.

Examples:

  • Assigning a piece of news to one of the predefined categories.
  • Detecting spam email messages based upon the message header and content
  • Categorising cells as malignant or benign based upon the results of MRI scans
  • Classifying galaxies based upon their shape

Decision Tree can be a…


in Python with a real world dataset

Photo by Kevin Ku on Unsplash

In the context of Supervised Learning (Classification), Naive Bayes or rather Bayesian Learning acts as a gold standard for evaluating other learning algorithms along with acting as a powerful probabilistic modelling technique.

In this post, we are going to discuss the workings of Naive Bayes classifier implementationally with Python by applying it to a real world dataset.

The post is divided more broadly into the following parts:

  • Data Preprocessing
  • Training the model
  • Predicting the results
  • Checking the performance of the model

The above parts can be further divided as follows:

Data Preprocessing

  1. Importing the libraries
  2. Importing the dataset
  3. Splitting…


How to handle it mathematically and conceptually

Photo by Kevin Ku on Unsplash

In the context of Supervised Learning (Classification), Naive Bayes or rather Bayesian Learning acts as a gold standard for evaluating other learning algorithms along with acting as a powerful probabilistic modelling technique. But, working with Naive Bayes comes with some challenges.

  • It performs well in case of categorical data as compared to numeric data. So, how do we perform classification using Naive Bayes when the data we have is continuous in nature.
  • If an instance in test data set has a category that was not present during training then it will assign it “Zero” probability and won’t be able to…


With multiple examples

Photo by Kevin Ku on Unsplash

In the context of Supervised Learning (Classification), Naive Bayes or rather Bayesian Learning acts as a gold standard for evaluating other learning algorithms along with acting as a powerful probabilistic modelling technique.

In this post, we are going to discuss the workings of Naive Bayes classifier conceptually so that it can later be applied to a real world dataset.

In many applications the relationship between the attribute set and the class variable is non-deterministic. In other words, the class label of a test record cannot be predicted with certainty even though its attribute set is identical to some of the…

Tarun Gupta

Trying to spread technical knowledge I have, along with some thoughts about life in form of experiences and poetry

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store