Original Python implementation of how to find the best places to be in one of the fundamental worlds of reinforcement learning — the grid world — The purpose of this article is to present fundamental concepts and definitions in Reinforcement Learning (from here on — RL) using Python code and comments. The article was heavily inspired by the great RL course: https://www.coursera.org/learn/fundamentals-of-reinforcement-learning

How to use big data on a small computer using Tensorflow, Python and iterators — The amount of data in the current business world is increasing every day. There are new data sources to merge, more rows to append and new columns to concatenate. Unfortunately, for a typical data scientist or a machine learning engineer, the pace at which one can buy a new laptop…

How to make an equivalent elastic net regression between sklearn and Tensorflow in Python — This article is intended for the practitioners who want to compare the sklearn and Keras implementation of elastic net regression. Mainly, how to go from Sklearn loss function to Keras (Tensorflow) loss function. Main parts of the article: A brief introduction to regularization in regression. Sklearn implementation of the elastic…

A complete Python implementation and explanation of the calculations behind measuring feature importance in tree-based machine learning algorithms — The aim of this article is to familiarize the reader with how are the importance of features calculated in decision trees. Personally, I have not found an in-depth explanation of this concept and thus this article was born. All the code used in this article is publicly available and can…

Coding and explaining in depth the very popular and competition-winning gradient boosting algorithm using Python — The aim of this article is to explain every bit of the popular and oftentimes mysterious gradient boosting algorithm using Python code and visualizations. Gradient boosting is the key part of such competition-winning algorithms as CAT boost, ADA boost or XGBOOST thus knowing what is boosting, what is the gradient…

BubbleSort, SelectionSort, InsertionSort, MergeSort and QuickSort algorithms are explained in detail using code and visualizations — This article is a continuation of one of my previous articles: https://medium.com/codex/algorithms-with-python-introduction-97f3bf3cf247. It will be easier to follow the ideas presented in this article after reading the one provided above. In this article, I will present my coded implementations for the following popular sorting algorithms:

An introductory article to algorithms that showcases the bubble sort algorithm in Python — No matter the programming language, a key concept in all of them are algorithms. Programming languages come and go, but the theoretical and practical sides of algorithms stay the same. …

Software testing theory and practise in Python — This article is heavily influenced by the great book I read — “Unit Testing. Principles, Practices, and Patterns” by Vladimir Khorikov. I highly encourage anyone who wants to upgrade their skills in programming to add this book to their collection. All the examples in the book are given in the…

Coding the powerful algorithm in python using (mainly) arrays and loops — This article aims to demystify the popular random forest (here and throughout the text — RF) algorithm and show its principles by using graphs, code snippets and code outputs. The full implementation of the RF algorithm written by me in python can be accessed via: https://github.com/Eligijus112/decision-tree-python I highly encourage anyone…