I’ve been learning about web development for a while pretty much from scratch. I’ve gone over numerous guides, tutorials and documentation from various resources, among which I took note of the important and beneficial ones that I believe a beginner will benefit from the most. In this article, I’m going to share them with you as a roadmap that you can follow if you want to become a full-stack web developer in a fun and efficient way.
I’ve put together a YouTube Tutorial on building a serverless architecture using Firebase that lets you create transaction entries in your Google budget spreadsheet just by creating a Trello card. Throughout several short videos I demonstrate how to handle Trello Webhooks, use the Google Sheets API and work with Cloud Firestore from Cloud Functions. The project is built using mostly Node.js and a little bit of Python.
If you use Google Spreadsheets for personal budget management and also like to get things done from the Linux terminal as much as possible, I have some good news for you. I’ve built a CLI app to insert transaction entries in monthly budget spreadsheets with simple commands from CLI. Today I’ll be walking you through the process of building this app.
Today I’m going to walk you through the process of scraping search results from Reddit using Python. We’re going to write a simple program that performs a keyword search and extracts useful information from the search results. Then we’re going to improve our program’s performance by taking advantage of parallel processing.
Hi all, I’ve put together a YouTube video series on developing an Eclipse RCP application in Java to build a chess game with a cool AI algorithm called alpha-beta pruning. You need no prior knowledge on Eclipse RCP to follow along, but a basic understanding of the Java programming language would definitely help.
Logistic regression is a simple classification method which is widely used in the field of machine learning. Today we’re going to talk about how to train our own logistic regression model in Python to build a a binary classifier. We’ll use NumPy for matrix operations, SciPy for cost minimization, Matplotlib for data visualization and no machine learning tools or libraries whatsoever.
Learning curves are very useful for analyzing the bias-variance characteristics of a machine learning model. In this post, I’m going to talk about how to make use of them in a case study of a regression problem. We’re going to start with a simple linear regression model and improve it as much as we can by taking advantage of learning curves.
Hey everyone, welcome to my first blog post! This is going to be a walkthrough on training a simple linear regression model in Python. I’ll show you how to do it from scratch, without using any machine learning tools or libraries. We’ll only use NumPy and Matplotlib for matrix operations and data visualization.