Explaining the Intuition of Word2Vec & Implementing it in Python — Table of Contents Introduction What is a Word Embedding? Word2Vec Architecture - CBOW (Continuous Bag of Words) Model - Continuous Skip-Gram Model Implementation - Data - Requirements - Import Data - Preprocess Data - Embed - PCA on Embeddings Concluding Remarks Resources Introduction Word2Vec is a recent breakthrough in the world…

Explaining Bayesian A/B Testing with Python Implementation — There are many applications of A/B testing across various industries. From trying to identify optimal market groups to target to medical drug testing, it has various applications and allows businesses to make decisions based on the results. There are two common ways to approach A/B testing, the frequentist approach and…

Understand the Monte Carlo method and how to implement it in Python — In this post, I will introduce, explain and implement the Monte Carlo method to you. This method of simulation is one of my favourites because of its simplicity and yet it’s a refined method to resolve complex problems. It was invented by Stanislaw Ulam, a Polish mathematician in the 1940s…

Anomaly Detection using Machine Learning — Anomaly detection is a critical technique in modern data science and machine learning. By identifying patterns in data that deviate significantly from expected behaviour, anomaly detection can help detect fraud, prevent equipment failures, and more. Anomaly detection is used in a wide range of applications, from finance and cybersecurity to…

Build a Topic Modelling Pipeline from Scratch in Python — This article will provide a high level intuition behind topic modelling and its associated applications. It will do a deep dive into various ways one can approach solving a problem which requires topic modelling and how you can solve those problems in both a supervised and unsupervised manner. I placed…

Overview and Python Implementation of Node, Edge and Graph Embedding Methods — There has been a rise in graph based machine learning in recent years. Graph based approaches have applications in a variety of common problems in data science like link prediction, community detection, node classification, etc. Depending on how you structure your problem and the data you have available, there are…

Identify important features associated with models in Python using SHAP and Sci-Kit Learn — This article will focus on providing the intuition and Python implementation associated with the various ways to identify important features associated to machine learning models. The following is the structure of the article. Table of Contents What is Feature Importance in Machine Learning? Coefficient Based Feature Importance

Drift Detection for Machine Learning Models — This article will aim to provide the intuition and implementation associated with data drift in Python. It will cover the implementation and differences between two approaches to calculating drift namely, cross entropy and KL divergence. The following is the outline of this article. Table of Contents What is Data Drift? Drift Metrics - Cross…

A Guide on Scraping Project Gutenberg — This article will provide a comprehensive guide on scraping open source book data from project Gutenberg. These book files would be useful to anyone who wants to do a cool project in NLP, like book summarization, translation, classification, recommendation, etc. The following is the structure for this article: Disclaimer: This…