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…

Understand how Markov Chains work and implement them in Python to generate text — In this article, I will explain and provide the python implementations of Markov chain. This article will not be a deep dive into the mathematics behind Markov chains, instead, it will prioritize the conceptual understanding of how it works and how to implement it with python. …

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…

Guide on ML Model Monitoring in Production — This article will outline model monitoring in a production environment. I will go over various metrics you should track when pushing your machine learning models into production and the associated implications and impact it can lead to. I’ll outline the model retraining process once the data scientist has received feedback…

Parallelize your Model Training and Prediction on Sci-Kit Learn — This article will outline how to train and tune certain models on sci-kit learn in parallel to optimize for time. I will also outline how you can parallelize the prediction process for these model(s). I’ll cover partitioning the data into multiple segments and generating predictions for each partition, as well…

Run your data science tasks in parallel to speed up computation time — This article will provide an intuitive understanding of how multiprocessing works and the associated steps to use it for running your jobs in parallel. I will provide sample functions using both pandas and numpy to run computationally expensive tasks in parallel to reduce time complexity. …

Build a Recommender System Pipeline using Latent Factor Recommendations (SVD) — This article will outline the intuition and the Python implementation of matrix factorization for recommendation systems. The following is the outline of the article. Table of Contents Intuition behind Matrix Factorization Singular Value Decomposition (SVD) - Mathematics of SVD - Example Walkthrough Problem Statement Data - Requirements